proceedings nurture young research talent

NUR FADHLINA ZAINAL ABEDIN, EDITOR

PROCEEDINGS

Nurture Young Research TalentSERIES 2

PROCEEDINGS

NURTURE YOUNG RESEARCH TALENT

Series 2

Published by MNNF Publisher

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Perpustakaan Negara Malaysia

eISBN No 978-967-17324-9-6

Editor: Nur Fadhlina Zainal Abedin

mailto:[email protected]

PROCEEDINGS

NURTURE YOUNG RESEARCH TALENT

Series 2

CONTENT

Effects of Sand Cement Ratio to The Performances

of Strength in Waste Treatment Sludge Mortar

Muhammad Firas Bin Faisal, Ainul Haezah Binti

Noruzman, Norpishah Binti Ahmad & Muhammad Hud

Iskandar Bin Abd Sani

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Enhancing Learning via Out-of-class Learning

Inventory

Norhaslinda Hassan & Ainol Madziah Zubairi

10

Interrogating the Performance of Shariah

Compliance Food and Beverages Industry in

Malaysia

Nur Sabrina Bahtiar, Wan Anisabanum Salleh,

Suhaily Maizan Abdul Manaf & Md Noh Ab Majid

16

Marine Robot

Muhammad Akmal Bin Johari, Norhalida Binti

Othman, Noor Hafizah Binti Khairul Anuar, Nur

Amalina Binti Muhamad & Masmaria Binti Majid

22

Mobile Application for Carpooling System in UiTM

Seremban Based on Customer Rating

Nurul Najihah bt Hisamuddin, NurFatin Nabilah bt Md

Fauzi, Nur Farrah Ain bt Mohamad Johari & Rosmah

Abdul Latif

26

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Proceedings: Nurture Young Research Talent, Series 2

(FYP & Postgraduate Poster Competition, Series 2/2020) MNNF Publisher

ISBN NO: 978-967-17324-9-6

Effects of Sand Cement Ratio to the

Performances of Strength in Waste Treatment Sludge Mortar

Ainul Haezah Noruzman, Muhammad Firas Faisal, Norpishah Ahmad & Muhammad Hud Iskandar Abd Sani

Politeknik Sultan Salahuddin Abdul Aziz Shah,

Persiaran Usahawan, Seksyen U1, 40150 Shah Alam, Selangor Darul Ehsan

[email protected]

ABSTRACT

The aim of the study is to investigate the potential use of dewatered sludge cakes in mortar properties as building material. The sludge was collected from water treatment plant and the percentages used in the mixes were 2%, 4%, 6%, 8% and 10% by weight of sand in mortar. The specimen without sludge was prepared for comparison. The testing involved chemical analysis of sludges as well as compressive strength for hardened state properties. The samples were cured at 7 and 28 days, and the average of three samples of 50 mm cube samples was measured. Two types of mixes with different ratio of sand were used and compared in terms of performance in strength. The results revealed that the waste sludges had higher components of Zn, Cu, Pb and As which was trace element concentrations in the dry sludge samples. Comparisons of strength were made from two different types of sand ratio used in the mixes. It was observed that sludge in mortar performed better when mixed with ratio of 1:3 compared to 1:6. The optimum results of waste sludge mortar were denoted from the replacement of 2% of sludge in the mortar. It can be concluded that waste sludge as a result from the process of water treatment can be utilized as partial replacement of sand in mortars.

Key Words: sludge, compressive strength, mortar, mix design, performances.

1. INTRODUCTION

The consumption demand of clean water for living life and industries increases with the rise of rapid development in Malaysia. The growing population in Malaysia was estimated about 32.6 million in 2019 (Mahidin, 2019), as well as industrial sector, which was accounted for more than 36.8% of the nation’s GDP in 2014, and the highest contributors were the sectors of electronic industry, construction industry, and automotive industry (Bada, 2018). Due to

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these developments, water supply management has become important to meet the demands of the population.

Among all the industries that contribute towards major economic growth of the country, the construction sector contributes the most. The highest value of construction was recorded in the state of Selangor with a contribution of 24.5% compared to Johor (16.5%), Kuala Lumpur (15.8%), Sarawak (8.6%), and Penang (6.4%) (Bada, 2018). These has made the state of Selangor to be the main national economic drivers towards GDP Malaysia. Therefore, the water treatment plant in Selangor is among the largest produced clean water which is estimated about 4476 million liters per day (Selangor Water Works, 2020). However, it is estimated that the quantity of over 2.0 million tons of water treatment sludge or residual (WTS) is produced annually by water operators across Malaysia. Due to the cost of finding new landfill (scarcity land) and the need for sustainable best practices, sludge disposal has become a global problem, and it is necessary to look for alternative reuse of sludge (Breesem, Faris, and Abdel-Magid, 2014). Therefore, final products of treatment water known as sludge has become a subject matter from the government to reduce waste generation. Most sludge is transported to designated landfill to damp the water residual waste (WSWM, 2015).

Water treatment plants produce a wide variety of waste products as well as safe drinking water. These residuals may be organic and inorganic in liquid, solid and gaseous forms depending on the source of raw water and the types of treatment processes including coagulation/filtration, precipitative softening plant, membrane separation, ion exchange and granular activated carbon (Robinson, and Witko, 1991). One of the most common methods employed to remove suspended particles and colloids from raw water is the addition of metal salts to initiate a coagulation–flocculation process. Alum sludge is a by- product of the treatment plants that use aluminum as coagulant. The treatment uses coagulant such as aluminium sulphate known as alum, the iron-based salts ferric chloride and ferric sulphate, which are the resultants of chemical reaction of Al and Fe salts in alkaline conditions to form hydroxide precipitates that remove impurities via co-precipitation, sorption, flocculation and settling (Dassanayake, Jayasinghe, Surapaneni, et al., 2015; Turner, Wheeler, Stone, et al., 2019).

In general, the construction industry in Malaysia plays a vital role towards the country’s development (Shehu, Endut, Akintoye, et al., 2014). The increasing demand in rapid development has attributed to the global consumption of usage of resource materials, for instance the utilization of natural aggregates and sand which are the main components in concrete and mortar production. However, the environmental concern is one of the main challenging issues affecting the natural concrete aggregate production (Ismail, Hoe, and Ramli, 2013). The exhausts of these materials have become pertinent if necessary, however, action taken to save the natural resources is prohibited. Therefore, it has become important to find alternative practices to decrease the requirement needed to produce innovative building materials due to the increase of waste generation of these materials. Waste treatment sludge is a hazardous waste produced from purifying water, which has been identified as a potential alternative ingredient in making concrete. Water treatment sludge has the potential to be used as building material in many aspects of studies in terms of strength and durability including replacement of mixing water (Roccaro, Franco, Contrafatto, et al., 2015), partial replacement of cement (Owaida, Hamid, Abdullah, et al., 2013), partial replacement of fine aggregates (Andrade, Wenzel, Da, et al., 2018), and application in civil engineering (Da Silva, Morita, Lima, et al., 2015). Among those researches, there is less information regarding the utilization of sludge in mortar properties focusing on comparison between two mixes of sand cement ratio, respectively. This study aims to investigate the possible use of sludge as partial replacement of sand in mortar properties and tested for compressive strength. Finally, the addition of sludge would improve the properties of mortar as well as to reduce environmental impacts associating

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from its waste generation and landfill disposals.

2. EXPERIMENTAL PROGRAM

2.1 Cement Ordinary Portland Cement (OPC) complying with BS EN 197-1: 2011 was used in all mortar mixes. The chemical composition of the cement is as shown in Table 1. The determination of major and trace element present in cement based composites followed the validation of EDXRF procedure by Mijatović et al. (2019). It was observed that high percentages of Ca in cement samples were due to the presence of limestone (CaCO3) used as raw material in cement production.

Table 1 Concentration of Elements with EDXRF Analysis

Major Element Portland Cement (%)

Si 2.1 Al 0.497 Fe 2.476 Ca 49.01 Mg 0.201 S 0.496 K 0.384 Ti 0.123 P -

Mn 973.2 (ppm) Sr -

Trace element Cr 12.1 (ppm) Zn 447.5 (ppm) Cu - As - Ni - Pb -

2.2 Fine Aggregate Natural river-washed quartz sand complying with BS 882: 1992 was used as fine aggregate respectively. The sand grading is shown in Table 2, and the fine modulus of sand is 2.10.

Table 2 Grading of Fine Aggregate

Sieve Size

Mass of each Sieve

weight Retained

Net Weight

Retained weight

Passing weight

Cumulative percentage

passing (mm) (g) (g) (g) (g) (g) (%)

2.36

488

513

25

25

475

95

1.18 353 520 167 192 308 62

0.6 304 398 94 286 214 43

0.3 275 442 167 453 47 9

0.15 258 298 40 493 7 1

Pan 354 363 7 500 0 0

2.3 Waste Sludge Waste sludge in dry condition as shown in Figure 1 was collected from water purification plant at Bukit Badong, Selangor. Dewatered sludge cakes are the by-products from processing of water treatment plant to provide clean water supply around the state of Selangor. The raw water is mainly sourced from surface water collected by several dams,

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lakes and rivers, and treated at a nearby water treatment plant. In this study, the raw water is collected from Sungai Selangor Dam and Sungai Tinggi Dam, with a capacity of 950 million liters per day (MLD) (Selangor Water Works, 2020). Sludge produced during the coagulation and flocculation process is passed through the dewatering facility, and the dehydrated sludge is subject to land filling. The representative samples of dehydrated sludge were tested for chemical analysis using energy dispersive X-ray fluorescence (ED- XRF) technique.

Figure 1: Sludge Resulting from Water Treatment Process

2.4 Mix Proportion In order to investigate the strength properties of waste sludge mortar, 7 mixes were employed. Two types of mix ratio used were 1:3 and 1:6, respectively. The free water to cementitious ratio was maintained constant at 0.5 for all mortar mixes. The compositions of sludge used as substitute for fine aggregate were 0%, 2%, 6%, 8% and 10% in cement mortar as shown in Table 3 and Table 4.

Table 3 Mix Proportion with 1:3 Ratio

S0% S2% S4% S6% S8% S10%

Cement (g) 625 625 625 625 625 625

Sand (g) 1875 1837 1800 1762 1725 1687

Water (g) 313 313 313 313 313 313

Sludge (g) 0 38 75 113 150 188

Table 4 Mix Proportion with 1:6 Ratio

S0% S2% S4% S6% S8% S10%

Cement (g) 357 357 357 357 357 357

Sand (g) 2143 2100 2057 2014 1972 1929

Water (g) 180 180 180 180 180 180

Sludge (g) 0 43 86 129 171 214

2.5 Compressive Strength The trial mix used mortar cubes sized 50 mm x 50 mm x 50 mm for compressive strength testing. The test was carried out according to ASTM C109 for each mix design, and the cubic mortars were tested at 7 and 28 days. The average of three values was taken as the strength value for all batches.

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3. RESULTS AND DISCUSSION

3.1 Particle Size Distribution The grading curves of fine aggregates are shown in Figure 2. The sand used in this study satisfied the grading requirements of fine aggregate according to BS 882:1992 which was suitable for mortar mixtures.

Figure 2: Grading Curves of Sand

3.2 Characteristics of water treatment sludge The chemical characteristics of water treatment sludge are shown in Table 5. The sludge was collected from water treatment plant (WTP) in Bukit Badong, Selangor, and the water was treated by using aluminum salt in the process of coagulation and flocculation. Major chemical compositions such as Si, Al, Fe, and Ca were found in the sludge, and these parameters were the main components of the sludge. Other trace metals were also found in the dried sludge. The higher elemental concentrations such as Zn, Cu, Pb, and As are thetypes of heavy metals which are harmful to the environment and population. Most of the chemical compositions and trace elements have similar characteristics with other studies (Ahmad, Ahmad, and Alam, 2016).

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Table 5 Concentration of Elements of Water Treatment Sludge with EDXRF Analysis

Major Element

Water Treatment Sludge (%)

Si 11.245

Al 6.156

Fe 7.484

Ca 0.378

Mg -

S -

K 2.562

Ti 0.602

P 0.136

Mn 468 (ppm)

Sr 52.9

Trace element

Cr 134 (ppm)

Zn 642.6 (ppm)

Cu 237.1 (ppm)

As 244.9 (ppm)

Ni 20.2 (ppm)

Pb 246.5 (ppm)

3.3 Effects of Waste Sludge in Compressive Strength Figures 3 and 4 illustrate the variation of compressive strength at 7 and 28 days with different percentage replacements of fine aggregate by addition of sludge. Each of the figure shows the performance of strength with ratio of 1:3 and 1:6 mix design, respectively. By referring to Figure 4, it can be observed that the compressive strength of the mortar decreased gradually as the percentages of sludge increased in mortar mixes. It was also found that the compressive strength of the mortar increased with the increase of the curing days. The specimens used mix ratio of 1:3 and the control specimens showed the compressive strength values of 7.17 MPa and 11.63 MPa at 7 and 28 days. From the graph, the high compressive strength was spotted at 2% replacement of sand with sludge in mortar specimen. However, after adding more percentages of sludge in the mortar mixes, the compressive strength values showed degradation in strength. The strength development for the mixes, for instance, strength developed at 7 curing days were 7.17, 8.57, 6.8, 5.27, 4.7 and 4.43 MPa for control mortar, and 2, 4, 6, 8 and 10% of sludge mortar, respectively. Similarly, at the same water cement ratio, mortar mixtures were seen to develop strength of 11.63, 12.6., 10.27, 10.17, 9.47 and 7.77 MPa at 28 days.

Compared to other batches which used the mix ratio of 1:6, the compressive strength values for control specimen 4.43 MPa and 6.30 MPa at 7 and 28 days are as shown in Figure 5. From the graph, it can be observed that except the control mortar specimen, the inclusion of sludge in mortar showed gradual degradation in strength. Higher percentages of sludge in mortar showed lower reduction in strength. The strength development at 7 days of curing were 4.43, 2.43, 1.73, 1.20, 1.13, and 1.0 MPa, whereas strength at 28 days of curing were 6.30, 3.03, 2.63, 2.60, 2.53 and 2.13 MPa, respectively. From the analysis, it can be realized that strength of mortar specimens was affected by mix design ratios and percentages of sludge used in the mixtures. The performance of strength was much higher when using mix ratio of 1:3 compared to 1:6. Mix designs containing cement strength class 11.63 MPa had better strength compared to mix designs containing cement strength class

6.30 MPa. In fact, mix designs also play their role in contributing strength performances similar to a previous research done by Eskandari-Naddaf and Kazemi (2018).

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Figure 3: Compressive Strength of Water Treatment Sludge Mortar at 7 and 28 Days from Batch of 1:3 Mix Ratios

Figure 4: Compressive Strength of Water Treatment Sludge Mortar at 7 and 28 Days from

Batch of 1:6 Mix Ratios

On the other hand, it was observed that the optimum strength for the partial replacement of sludge in mortar was 2%, related to bond strength of mortars. The strength results were observed to have similar behaviors and in line with earlier studies which used water treatment sludge in mortar properties (Andrade, Possan, ChiaradiaWenzel, et al., 2019). The higher the percentages of sludge in mortar, less bonding occurred, and decrease in compressive strength.

4.0 CONCLUSION

The conclusions that can be drawn from this study are as follows;

The results show that the mix design has a greater effect on the performance of strength in mortar properties.

Using mix design containing sand-cement ratio of 3 shows the optimum strength of 2% of water treatment sludge in mortar.

As the water treatment sludge replacement percentages increase, the compressive strength decreases due to possible lack of effective bonding within the particle sizes in

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mortar.

Grading of fine aggregate meets the requirements of overall grading aggregate limit.

ACKNOWLEDGMENT

The authors gratefully acknowledge Politeknik Sultan Salahuddin Abdul Aziz Shah for support and guidance.

REFERENCES

Ahmad, T., Ahmad, K., and Alam, M. (2016). Characterization of Water Treatment Plant’s Sludge and its Safe Disposal Options. Procedia Environmental Sciences, 35, 950–955.

https://doi.org/10.1016/j.proenv.2016.07.088 Andrade, J. J. de O., Possan, E., ChiaradiaWenzel, M., and da Silva, S. R. (2019). Feasibility of using

calcined water treatment sludge in rendering mortars: A technical and sustainable approach. Sustainability (Switzerland), 11(13). https://doi.org/10.3390/su11133576

Andrade, J. J. de O., Wenzel, M. C., Da, G. H., and Rocha, S. R. da S. (2018). Performance of rendering mortars containing sludge from water treatment plants as fine recycled aggregate. Journal of Cleaner Production, 192, 159–168. https://doi.org/10.1016/j.jclepro.2018.04.246

Bada, F. (2018). The Biggest Industries In Malaysia. Retrieved May 18,2020. Retrieved from https://www.worldatlas.com/articles/the-biggest-industries-in-malaysia.html

Breesem, K. M., Faris, F. G., and Abdel-Magid, I. M. (2014). Reuse of Alum Sludge in Construction Materials and Concrete. Infrastructure University Kuala Lumpur Research Journal, 2(1), 20–30.

Da Silva, E. M., Morita, D. M., Lima, A. C. M., and Teixeira, L. G. (2015). Manufacturing ceramic bricks with polyaluminum chloride (PAC) sludge from a water treatment plant. Water Science and Technology, 71(11), 1638–1645. https://doi.org/10.2166/wst.2015.132

Dassanayake, K. B., Jayasinghe, G. Y., Surapaneni, A., and Hetherington, C. (2015). A review on alum sludge reuse with special reference to agricultural applications and future challenges. Waste Management, 38(1), 321–335. https://doi.org/10.1016/j.wasman.2014.11.025

Eskandari-Naddaf, H., and Kazemi, R. (2018). Experimental evaluation of the effect of mix design ratios on compressive strength of cement mortars containing cement strength class 42.5 and 52.5 MPa. Procedia Manufacturing, 22, 392–398. https://doi.org/10.1016/j.promfg.2018.03.060

Ismail, S., Hoe, K. W., and Ramli, M. (2013). Sustainable Aggregates: The Potential and Challenge for Natural Resources Conservation. Procedia - Social and Behavioral Sciences, 101, 100–109. https://doi.org/10.1016/j.sbspro.2013.07.183

Mahidin, M. U. (2019). Current Population Estimates, Malaysia, 2018-2019. Retrieved from https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=155&bul_id=aWJZRkJ4U EdKcUZpT2tVT090Snpydz09&menu_id=L0pheU43NWJwRWVSZklWdzQ4TlhUUT09

Mijatović, N., Terzić, A., Pezo, L., Miličić, L., and Živojinović, D. (2019). Validation of energy-dispersive X-ray fluorescence procedure for determination of major and trace elements present in the cement based composites. Spectrochimica Acta - Part B Atomic Spectroscopy, 162, 105729.

https://doi.org/10.1016/j.sab.2019.105729 Owaida, H. M., Hamid, R., Abdullah, S. R. S., Kofli, N. T., and Taha, M. R. (2013). Physical and

mechanical properties of high performance concrete with alum sludge as partial cement replacement. Jurnal Teknologi (Sciences and Engineering), 65(2), 105–112.

https://doi.org/10.11113/jt.v65.2198 Robinson, M. P., and Witko, J. B. (1991). Overview of Issues and Current State-of-the Art Water

Treatment Plant Waste Management Programs. In Annual Conference Proceedings. AWWA Quality for the New Decade, Philadelphia, PA. June 23–27. Retrieved from

http://www.nesc.wvu.edu/pdf/dw/publications/ontap/2009_tb/water_treatment_DWFSOM49.pdf Roccaro, P., Franco, A., Contrafatto, L., and Vagliasindi, F. G. A. (2015). Use of Sludge from Water

and Wastewater Treatment Plants in the Production of Concrete : An Effective End-of-Waste Alternative. In Proceedings of the 14th International Conference on Environmental Science and Technology.

http://www.worldatlas.com/articles/the-biggest-industries-in-malaysia.html

http://www.worldatlas.com/articles/the-biggest-industries-in-malaysia.html

http://www.nesc.wvu.edu/pdf/dw/publications/ontap/2009_tb/water_treatment_DWFSOM49.pdf

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Selangor Water Works. (2020). In Wikipedia. Retrieved May 17, 2020. Retrieved from https://en.wikipedia.org/wiki/Selangor_water_works

Shehu, Z., Endut, I. R., Akintoye, A., and Holt, G. D. (2014). Cost overrun in the Malaysian construction industry projects: A deeper insight. International Journal of Project Management, 32(8), 1471–1480. https://doi.org/10.1016/j.ijproman.2014.04.004

Turner, T., Wheeler, R., Stone, A., and Oliver, I. (2019). Potential Alternative Reuse Pathways for Water Treatment Residuals: Remaining Barriers and Questions—A Review. Water, Air, and Soil Pollution, 230(9). https://doi.org/10.1007/s11270-019-4272-0

WSWM. (2015). Study on Current Issues for Water Supply and Wastewater Management in Malaysia. Journal of Chemical Information and Modeling (Vol. 53). Academy of Sciences Malaysia. https://doi.org/10.1017/CBO9781107415324.004



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Enhancing Learning via Out-of-class Learning

Inventory

Norhaslinda Hassan1 & Ainol Madziah Zubairi2

Academy of Language Studies, UiTM Pulau Pinang1

Kuliyyah of Education International Islamic University Malaysia2

[email protected]

ABSTRACT The central focus of the implementation of Outcome-Based Education (OBE) is structured educational system, which focuses on what students are able to do successfully at the end of their learning experiences. In short, the ultimate outcome of OBE is student learning. With regards to the preset study, the focus is on language learning. Successful language development is associated with active out-of-class learning activities engagement. Against this background, this Out-of-Class Learning Inventory (OoCLI) is devised to assist teachers in engaging students’ out-of-class language learning. In doing so, an Outcome-Based Assessment (OBA), ELC231 test battery is used as the assessment to be measured. The items were devised based on generated themes from semi-structured interviews of 11 informants, which were then validated using Item Objective Congruence (IOC) method. A total of 65 students responded to the 6-point Likert scale survey and this is followed by scrutinizing the validity and reliability of the items using Rasch Measurement. 4 criteria were employed, namely item and person reliability, separation index, item fit and item polarity. Item reduction were done to gain the best fit of the items to Rasch model. A total of 14 items were deleted, leaving only 20 items with 1 open-ended item. Having shown evidence of high reliability and statistical validity, OoCLI may therefore be employed for assessing students’ out-of-class learning, as well as a self-assessment inventory for the students. Key Words: OBE, OBA, out-of-class learning, thematic analysis, IOC, Rasch

1. INTRODUCTION Malaysia has in recent years announced its decision to implement Outcome-Based Education (OBE) at all higher learning institutions with the focus on developing world class human capital. It has to be noted that assessment is the key component of OBE system, i.e. aligning the assessments methods to the course outcomes. In order to ensure that all Learning Outcomes (Los) are achieved, it is imperative to employ Constructive Alignment (CA), in which, the Course Outcomes (Cos) are aligned to the Teaching and Learning Activities (TLAs), and the Assessment Tasks (ATs). Assessment methods include assignments, tests, quizzes, final exams, projects and etc. ATs must be manageable, some



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TLAs as part of assessment task, design rubrics, ask students to reflect, requires a portfolio to be kept and set tasks that are practical to be carried out by the students. In addition, OBE system emphasizes on continuous assessment by means of employing formative and summative assessments, as well as authentic assessment. Hence, teachers are to align their assessment methods with the outcome statements. Since OBE is student-centered and the central focus is on student learning, it is important for learners to know their strengths and weaknesses, as well as their further development needs. It is worth noting that learning comprises diverse formal and informal setting experiences that complement each other (Colley, Hodkinson & Malcolm, 2003; National Research Council, 2009). In other words, in-class and out-of-class learning, in which the former is formal and the latter is informal setting. Bäumer et al., (2011, p. 92) pointed out that these two settings are viable in building “a complex web of synchronic as well as chronological learning opportunities”. With regards to language learning, out-of-class learning has been empirically proven to have positive correlation with language gains (Inozu et al, 2010; Larsson, 2012; Richards, 2009; Sundqvist, 2011). Finding and employing various out-of-class opportunities for learning has been observed in successful language learners (Benson et al., 2003; Borrero & Yeh, 2010) Therefore, assisting language learning in constructing quality out-of-class learning experiences is deemed imperative (Blyth & LaCroix-Dalluhn, 2011; Stickler & Emke, 2011). It is against this backdrop that the researchers devise this OoCLI with a twofold purpose; to serve teachers and students. OoCLI may aid teachers to assess students’ out-of-class learning practices, which may culminate in enhanced learning process. Information on students’ out-of-class learning practices would be available to the teachers and this may help teachers to use this information in their teaching. Apart from that, this inventory may serve as a self-assessment, in which the students, while answering the inventory may become aware of their out-of-class activities and thus, assess themselves. This will provide some awareness in the students about their out-of-class learning practices. In doing so, the inventory is devised to gain access to ‘what’ (learning content, materials, tasks, etc.) and ‘how’ (the rate and sequence of learning) students learn outside of the classroom, including their test preparation and challenges faced outside of the class.

2. THE DEVELOPMENT OF OoCLI

This inventory is developed by means of employing both qualitative and quantitative

methods. Table 1 demonstrates the methods applied and the outcomes of the

methodologies.

Table 1: Methods and outcomes of OoCLI development

Method/Instrument Outcome/Product

Qualitative Face-to-face semi-structured Interview (n=11)

Themes

Qualitative Inter-rater (n=2) Items for survey (n= 36)

Quantitative IOC (n=5) Reviewed items for survey (n= 36)

Quantitative Rasch Measurement (reliability, separation index, item polarity and item fit)

Validated items for survey (n= 20)

For the purpose of the present study, ELC231 test battery was chosen as the assessment. Therefore, students who have taken ELC231 in UiTM, Penang Branch Campus were approached as the respondents of this study. An individual face-to-face semi-structured interview was employed to gain insights from the students on their out-of-class learning experience in the course of attending ELC231. Purposeful sampling was employed, i.e. students who have taken ELC231 were chosen on voluntary basis as the informants in



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order to better understand how they experienced learning with the influence of OBE. A lecturer was approached and asked for voluntary participants. 11 students volunteered to be interviewed at their free time. The students were contacted via WhatsApp and an appointment was set for the interview, which lasted for about 30 to 45 minutes. The students were first explained briefly about the study and some background knowledge were provided so that they will be able to answer the interview questions. The students were then explained about ethical issues and they signed the consent letter. A recorder was used to record the interview for the purpose of transcriptions later for data analysis. The interviews were then transcribed and analyzed by means of thematic analysis. The researchers looked for main ideas in the interviews and this is done twice to ensure that there are no main ideas being overlooked. After that, the main ideas from each interview were combined and the themes were formed. A qualitative phenomenological method was employed to provide grounds for investigating a phenomenon as lived and experienced by a number of individuals rather than focusing on differences between individuals, building theories or documenting case studies (Creswell, et.al, 2007). Also, this method allows participants the opportunity to narrate their experiences with as much detail as possible, including their subjective reflections and judgments (Smith et al., 2009). To ensure the reliability of the generated themes, a Professor in the area of Language Testing in IIUM and a lecturer in UiTM who has the experience of teaching ELC231 were approached to rate the generated themes. The ratings were computed and the inter-rater reliability for the generated themes is 90.5%.

To reiterate, the focus of this OoCLI is ‘what’ (i.e. learning content, materials, tasks, etc) and ‘how’ (i.e. the rate and sequence) learning is going on outside of the classroom, as well as students’ test preparation and challenges faced. Consequently, the generated themes were categorised accordingly. 36 (34 + 2 open ended) items were devised from the generated themes using 6-point reflect me Likert scale, i.e. very untrue of me to very true of me and were divided into 4 sections, namely activities, assessment, motivation to learn and challenges faced.

It is noteworthy that the content of the survey items have to be appropriate and met the objectives of the study. Therefore, these items were rated and reviewed by 5 expert judges to establish content validity. The expert judges were given 6-point scale from Very Irrelevant to Very Relevant. Item objective congruence (IOC) method (Rovinelli & Hambleton, 1977) was employed to measure the fit of individual items to content domain and to enable individual items to be assessed quantitatively. UiTM lecturers who have the experience of teaching ELC231 were approached as the expert judges and granted their agreement on voluntary basis. Since assessments in the OBE system is criterion-referenced, IOC is the preeminent step employed to validate criterion-referenced test (McCowan& McCowan, 1999) as how well the items measure the objective can be answered by means of IOC method. More specifically, a content expert will evaluate each item by giving the item a rating of 1 (for clearly measuring), -1 (clearly not measuring), or 0 (degree to which it measures the content area is unclear) for each objective (Turner & Carlson, 2002). The calculation of IOC index was done based on the degree to which an item measures (or does not measure) a specific objective. In deciding the cut off score, Rovinelli and Hambleton (1977) propose that “if one-half of the content specialists judged an item to be a perfect match to an objective, while the others were not able to make a decision, the computed value of the index would be .50”.

The 36-item survey was distributed to students who have taken ELC231 and the respondents were informed that their participation was voluntary. Electronic survey, i.e. Google Form is utilized as a platform to disseminate the survey and 65 respondents answered the survey. To confirm the construct validity of the survey, the data were analyzed using Winsteps Rasch software version 3.72.1 (Linarce, 2009). According to Baghaei (2008), the Racsh model has been used widely to analyse questionnaires and construct



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validity. Moreover, data that fitted the model indicates a valid test, in which a construct is underlying the covariance among the items and causes the item responses (Baghaei & Tabatabaee Yazi, 2016; Borsboom, 2008). Therefore, 34 items and 65 participants were subjected to the Rasch analysis to estimate the fit of data to the model. 4 criteria to assess the usefulness of measurement, i.e. reliability and validity are reported namely, person and item reliability, separation index, item polarity, and item fit.

In Rasch measurement, both item and person reliability are reported to indicate that the items can measure consistently. A value that is more than 0.7 is deemed significant and proposes that the items can measure consistently (Bond & Fox, 2007). Apart from the reliability value, the isolation index value is also looked into as the isolation index yield the isolation against the difficulty level according to the item. According to Linacre (2005), value of greater than 2.0 yields good isolation index. It has to be noted that the reliability of OoCLI is acceptable, in which the real person reliability index is 0.77 and model person reliability index is 0.84. This implies that the scale discriminates very well between the persons. The isolation index is slightly low as the real person separation index is 1.85 and the model person separation index is 2.33. The reliability of the items for OoCLI are higher, which is yielded in real item reliability (0.93) and model item reliability (0.94). Similarly, the isolation index values are also good, i.e. real item separation index is 3.71 and model item separation index is 3.94.

The next criterion is employed to determine if the items are measuring in the same direction by means of scrutinizing the item polarity. Items showing positive PT-Mea Corr value, which is more than 0.3 are good items, while items with negative value of PT-Mea Corr need to be dropped or reviewed as the items signifies no focus to the dimension being assessed (Bond & Fox, 2007). 8 items (item number 10, 13, 25, 30, 31, 32, 33, and 34) have PT-Mea Corr values of less than 0.3. The researchers moved on to the next criteria before deciding which items to be deleted by means of scrutinizing the fit indices. The fit indices exhibit productive measurement for survey data with rating scale. In Rasch model, perfect fit is indicated in the values of Outfit and Infit mean squares (MNSQ), which ranges from 0.6 to 1.4 (Wright & Linacre, 1994, Bod & Fox, 2007). As a result, 4 items were identified to not having the perfect fit, i.e. items 25, 15, 10, and 21. Because of the lack of fit to the model, the items were then scrutinized and decisions on item reduction were made and a total of 14 items were deleted. The items were deleted due to low values in item polarity and item fit. Apart from that, the items were carefully chosen so that they will not distort the survey as a whole. The 14 items deleted include items number 1,10,13,15, 25, 26, 27, 28, 29, 30, 31, 32, 33 and 34, leaving 20 items and 1 open-ended item. Items from 2 sections, namely motivation and challenges were dropped as some of the items in these sections are misfitting items. Hence, the survey has only 2 sections, i.e. activities and assessment. The challenges section is converted into an open-ended item. Following the item reduction, the 4 criteria were reanalyzed. The person reliability index has become higher; real person reliability index is 0.85 and model person reliability index is 0.89. Similarly, the isolation index also increases, i.e. real person separation index is 2.34 and model person separation index is 2.78. The item reliability values go a bit lower but are still good; real item reliability (0.89) and model item reliability (0.90). The real item separation index is 2.84 and model item separation index is 3.01. This information is presented in the Summary Statistics Winsteps Output Table (Table 2). Apart from person and item reliability, the value of Cronbach Alpja coefficient (0.89) suggests that there is a high level of interaction between 65 persons and 20 items. Notably, an instrument having very good psychometric internal consistency is considered a highly reliable instrument.



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Table 2: Summary Statistics

With regards to item polarity, no item was found to be below the value of 0.3. Finally, the item fit indices are scrutinized. The infit MNSQ values for items 7 and 2 are slightly more than 1.6, while the infit mnsq valuse for items 5 is 0.59. Despite the slight misfitting value, the researchers decided to retain the 3 items as these items are necessary to provide a better understanding of out-of-class learning. To ensure a sound conclusion is drawn, the precision of measurement of OoCLI is evaluated to provide accurate and reliable measurement. The Item Column Fit Order, in which Model S.E or the Standard Error of Measure is scrutinized. A well-targeted instrument should be within 0.5 logits, i.e. < 0.5. With regards to the OoCLI, the Model S.E range between 0.1 to 0.2 logits and this suggests reliable and good Item fit. The 6-point Likert scale of the survey is converted into dichotomous yes or no as suggested by Stone (1998, p.1): “dichotomies in rating scales are more useful than multiple ratings”. His conjecture is based on the prominent Minnesota Multiphasic Personality Inventory (MMPI). Since the objective of this study is to devise an inventory on out-of-class learning, Stone’s view is taken into account.

3. POTENTIAL USE OF OOCLI For the purpose of this study, only percentages were counted to show how teachers can make use of the data retrieved from the inventory. It was found that majority of the students learn English outside of the class without any specific reference to the curriculum, which means students learn informally and by means of authentic real-life situation. The use of technology is prevalent as most of the students prefer to learn English through discussions with friends (85%), using mobile apps (68%), Instagram (85), Google (83%), YouTube (79%), watch English movies (82%) and listen to English songs (88%) as their out-of-class learning activities. With regards to test preparation, 94 % of students prepare for test when exam is near. 40% recorded that they do not prepare for exam, which means 60% of the students do prepare for their tests. 94% of the students feel that homework given by their lecturer are helpful for their exam preparation. The challenges they faced outside of the class include friends, limited printed materials, packed classes, low proficiency level and they felt that they needed extra English classes.

Teachers may assign students with group work or projects as their out-of-class learning activities. Also, teachers may assign case study or problem-based learning to enhance students’ higher order thinking skill. Moreover, teachers can incorporate technology in their teaching activities, as well as assigning homework or activities outside of the class that may interest the students, i.e. using technology as a platform to learn. There



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is abundance of electronic teaching platform available, such as Google classroom, Edmodo, to name a few. A follow up discussion about the project may be done in classroom by means of presentation, forum, etc.

It has to be noted that when students are given a project work, which has to be done outside of the class, this may culminate in the sought-after skills that the students could practice in their future career. This will then make their learning worthwhile. As students acknowledge that their learning is worthwhile, they could then justify their learning and find a motivation to learn (Brophy, 1999). It has to be noted that majority of the students felt that homework given by their teachers are useful in their test preparation. Hence, the researchers deem that teachers may assign students with homework that are related to their test preparation. The inventory helps teachers to gain access to students’ learning activities outside of the class, apart from enabling students to self-assess themselves with regards to their out-of-class learning. Upon answering the inventory, teachers may go through the items in the inventory one by one with the students. While discussing on the items, teachers may prompt the students to think about their out-of-class learning. This may be followed by encouraging students to learn outside of the class as it will enhance their learning, especially with regards to real-life situation. According to Resnick (1987), in-class learning alone may not be sufficient to prepare students for real-world challenges. Hence, out-of-class learning complements in-class learning as events and objects in physical worlds are openly connected via out-of-class learning. Notably, students of this new age are not dependent on in-class learning as they are found to use various formal and informal resources to support their learning and hence, learning goes beyond in-class language learning contexts (Lai, 2013; Gao, 2010).

REFERENCES Baghaei, P. (2008). The Rasch model as a construct validation tool. Rasch Measurement

Transactions, 22(1), 1145-1146.

Benson, P., Chik, A. & Lim, H. Y. (2003). Becoming autonomous in an Asian context: Autonomy as a sociocultural process. In D. Palfreyman & R. C. Smith (Eds.), Learner autonomy across cultures: Language education perspectives (pp. 23-40). Basingstoke: Palgrave Macmillan.

Blyth, D. A., & LaCroix-Dalluhn, L. (2011). Expanded learning time and opportunities: Key principles, driving perspectives, and major challenges. New Directions for Youth Development, 131, 15-27. doi: 10.1002/yd.405

Brentani, E., & Golia, S. (2007). Unidimensionality in the Rasch model: how to detect and interpret. Statistica, 67(3), 253-261.

Brophy, J. (1999). Toward a model of the value aspects of motivation in education: Developing appreciation for.. Educational psychologist, 34(2), 75-85.Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ, US.

Borsboom, D. (2008). Latent variable theory. Measurement, 6, 25-53. Creswell, J. W., Hanson, W. E., Clark Plano, V. L., & Morales, A. (2007). Qualitative research designs:

Selection and implementation. The counseling psychologist, 35(2), 236-264. Gao, X. S. (2010). Strategic language learning: The roles of agency and context. Bristol: Multilingual

Matters.



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Interrogating the Performance of Shariah

Compliance Food and Beverages Industry in

Malaysia

Nur Sabrina Bahtiar, Wan Anisabanum Salleh, Suhaily Maizan Abdul Manaf & Md Noh Ab Majid

Universiti Teknologi MARA, Terengganu, Malaysia

[email protected]

ABSTRACT Food and beverages industries are among Malaysia's most influenced and among the top industry ranks. Particularly for Shariah enforcement purposes, it is becoming the most essential issues to be discussed further. For better understanding, this study will focus on assessing the companies' performance for food and beverages that follow Shariah compliance as the demand for halal products is keep increasing day by day. To be more specific, the dependent variable of this study is return on asset (ROA) meanwhile the independent variables are total asset, quick ratio, inventory turnover, and debt ratio to complementing this study. Annual reports for selected companies have been collected from Osiris and Eikon Thomson Reuters for year 2013 to 2018. Findings from previous studies is expected to give direction to this investigation, for instance total asset, quick ratio and debt ratio will be significantly positive with return of assets. Meanwhile, the inventory turnover will be significantly negative with return of assets. Key Words: Profitability, Food and Beverages Industry, Shariah Compliance.

1. INTRODUCTION Malaysian food and beverages (F&B) industry is a fast-growing consumer product industry characterized by a large export market and the most promising industry in the world. It generates high percentage of profit for most countries including Malaysia. Food and beverages industry involve in processing raw food material, packaging and distributing them. This includes fresh, prepared food as well as packaged foods and non-alcoholic beverages. Based on Malaysian Investment Development Authority (MIDA), there are notable foreign and multinational companies (MNC’s) producing process food products in Malaysia. It encompasses sector such as cocoa and chocolate products, fishery products, cereals products, processed fruits and vegetables, confectionery, foods ingredients, herbs and spices and beverages. Due to advancements in food technologies and distributions,

mailto:[email protected]



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almost all products are exposed to various ingredients and manufactured foods as well as halal or non-halal categorization.

As with other religions in the world, Islam has introduced clear and definite concepts of halal and haram that its follower must adhere to. In a study done by Abdul Raufu and Ahmad Naqiyuddin (2013), with its acceptance as an alternative benchmark for health, hygiene, and quality control of what is eaten, the idea of halal products or food is gaining worldwide attention. Both Muslim and many non-Muslim customers embrace goods or foods manufactured according to halal prescriptions. Therefore, it is very important for Muslims to consume halal food and must be free from tainted elements (Zunirah, Suhaiza & Yusserie, 2010).

1.1 Problems Statement A company's profitability indicates the ability of a company to produce profits at a rate of revenue, assets and other capital resources over a given duration. Knowing the determinants of profitability is crucial to helping managers build an effective cost-effectiveness strategy for their company (Farah & Nina, 2016). However, there are limited studies examining factors that influence profitability on Shariah compliance companies as well as industries. Furthermore, signifying that factors which affecting the Shariah profitability of food and beverage sectors has not yet been sufficiently investigated. Though taking this into consideration, the insufficiency of empirical investigation is a key problem in those Shariah food and beverages sectors, as it will difficult for management and key players to monitor and create sound and healthy financial conditions for better sales and production. For that matter, the researcher attempts to give some insight contribution and enhance new findings of this area of research for companies to fill the knowledge gaps in empirical evidence.

2. LITERATURE REVIEW This study is aiming to measure the performance Shariah compliance food and beverages industry in Malaysia. Return on assets (ROA) will be used to represent the performance of those companies, while total assets, quick ratios, inventory turnover and debt ratios will be independent variables for this study. Analysis of the financial statements is undertaken to assess the financial results of a company. In financial analysis the strengths and weaknesses of a company are established by creating a clear relationship between the balance sheet products and the account of profit and loss (Muhammad, Humaira, Naila & Aneela, 2017). Different types of ratios are used to measure the choice between financial performance and capital structure. A ratio is used as a benchmark in financial analysis for determining a firm's financial situation and results. Muhammad et al. (2017) also mentioned that ratios help to analyze vast volumes of financial data and to draw qualitative conclusions about the financial results of the company. Hence, ROA is also known as return on investment, which means how businesses use their assets to produce income effectively if the ROA ratio is high then it is considered good for performance and growth.

In a study conducted by Omar, Abdul Aziz, Syed Ahsan and Nour Aldeen (2016) in examining the relationship between liquidity (quick) ratios and indicators of financial performance (profitability) found that a positive relationship between liquidity ratios with return on assets being measured. This is supported with the similar findings from Bolek and Wilinski (2012), Vayanos and Wang (2012), Priya and Nimalathasan (2013), and Ruziqa (2013). A significant impact of liquidity ratios on ROA but insignificant results for return on equity (ROE) and return on investment (ROI) has been derived from Saleem and Rehman (2011) and Khidmat and Rehman (2014). However, Khaldun (2014) noted that there is a weak significant relationship between current ratio, quick ratio, cash ratio, and gross profit margin, and those ratios together impact significantly on the growth of profit of industrial



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companies in sector food and drink. While Akter and Mahmud, (2014) conclude that there is no significant relationship between current ratio and ROA.

Efficiency ratio measures on how effectively the company utilizes these assets, as well as how well it manages its liabilities to maintain its company’s performance. According to Abdillah (2020), the higher level of inventory turnover causes the company to sell merchandise more quickly so that operating profit will increase, and ultimately net income will increase. Thus, the higher the inventory turnover, the higher the risk that can be minimized, and the greater a company's productivity. However, high profits for the company do not automatically mean high profitability, but high profitability may be ascertained to be substantial. From the findings by Abdillah (2020), the results of this study are inventory turnover does not have a positive effect on ROA. This is also similar with Farooq (2019) indicates that ROA does not affect by inventory turnover ratio but affects by sales growth ratio, net working capital, and size of the firm.

Previous studies have shown that there is positive relationship between leverage (debt ratio) with companies’ performance food and beverages listed industry (Ramlan & Nordin, 2018; Maria & Udeh, 2019). However, by utilizing Generalized Method of Moments (GMM), Kebewar (2012) revealed that debt ratio has no effect on profitability, regardless of the size of company. In a nutshell, most of the research reveals that there is positive and significant relationship between leverage on company’s performance but some of them proved that there is negative relationship between debt ratio and profitability. It shows that the companies increase in debts and will lead to reduction in the asset of utilization of the company.

3. METHODOLOGY Data from year 2013 to 2018 was selected to investigate the performance of Shariah compliance food and beverages industry in Malaysia. The selection of the samples in this study initially considered 10 companies from 23 listed Shariah food and beverages companies in Bursa Malaysia. The companies chosen are as Apollo Food Holdings Berhad, Hwa Tai Industries Berhad, Hup Seng Industries Berhad, Oriental Food Industries Sdn Bhd, Kawan Food Berhad, Power Root (M) Sdn Bhd, Spritzer Sdn Bhd, Dutch Lady Milk Industries Berhad, Fraser and Neave (F&N) Ltd and Nestle (M) Berhad. Secondary data has been collected through Osiris, Eikon and financial report of each selected companies.

4. RESULTS DISCUSSION AND ANALYSIS

4.1. Descriptive statistics

Table 1 Descriptive statistics of study variables ROA Quick ratio Inventory turnover Debt ratio LgTotal Asset

Minimum -4.81 0.36 2.5 0 5.07267 Maximum 49.8 15.99 10.4 0.74 15.02305 Mean 15.2525 2.757333 6.6545 0.1778333 11.48456 Std. Dev. 12.53315 3.503417 1.823438 0.1887613 3.221455

From the above table, it shows the readings for descriptive statistic consists of minimum value, maximum value, mean, and standard deviation for dependent and independent variables. The dependent variable is ROA while the independent variables are quick ratio, inventory ratio, debt ratio and total asset, with the contribution of 60 observations. For ROA, the minimum value is -4.8% while the maximum value is 49.8%. The mean is 15.2525% while standard deviation is 12.53315% respectively. Quick ratio recorded a minimum value of 0.36% and maximum value is 15.99%, inventory turnover has a minimum value of 2.5%



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and maximum value of 10.4%. On the other hand, debt ratio as another other independent variable shows the minimum, maximum and mean value of 0%, 0.74% and 0.17783333% respectively. Lastly, the total asset indicates that the minimum value is 5.07267% and maximum value is 15.02305% while the mean and standard deviation is 11.48456% and 3.221455% respectively. The greater the standard deviation of each variable, the greater the variance between each of them, while low standard deviation means that low variability among those variables.

4.2. Pearson correlation analysis Pearson’s correlation coefficient is the test statistics that used to measures the strength of linear relationship between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance.

Table 2 Pearson correlation of study variables

ROA Quick ratio Inventory

turnover Debt ratio

LgTotal Asset

ROA 1.0000 Quick ratio -0.2582 1.0000 Inventory turnover -0.0319 0.4050 1.0000 Debt ratio -0.4255 -0.2293 0.1775 1.0000 LgTotal Asset 0.3444 -0.0523 -0.3204 0.1982 1.0000

Referring to table 2, it represents the correlation between all the variables. To

achieve the significant level the value must be below than 0.5. It shows that total asset has a positive significant relationship with the dependent variable. It indicates that total asset has parallel relationship where is when one variable increase or decreases, the other variable will increase or decrease in corresponding. Besides, for the quick ratio, inventory turnover and debt ratio have negative but significant relationship with the ROA which is not close with 1 or -1. Referring to the table, the value of quick ratio, inventory ratio and debt ratio are -0.2585, -0.0319 and -0.4255 respectively.

4.3. Regression model The regression model shows the relationship between the dependent variable (ROA) and independent variables (TA, QR, ITO and DR).

ROA = -24.92792 + 2.55878 TA** – 2.308817QR**+ 4.01145 ITO** – 53.61283 DR** + Ɛ Note: **5% indicate the significant level (TA = Total asset, QR = Quick ratio, ITO = Inventroy turnover, DR = Debt ratio, Ɛ = Error terms

The coefficient measures the extent to which two or more variables move together.

The positive correlation shows the extent to which the variables are increased or decreased in parallel, the negative correlation indicates that the extent to which one variable increase when the other decline. For the result exists, if total asset increases by RM 1 million, it will increase the return on assets by RM 2.55878, and so on with the other variables.

As for overall analysis, this study wants to know the relationship between return on asset (ROA) with total asset, quick ratio, inventory turnover, and debt ratio. The study found that total asset was positively correlate and significantly affect the return on asset at 5% level of significant and the probability value is 0.000.This findings is in line with some empirical studies conducted by Muhammad et al. (2017) and Tian (2017) which allow that total asset has been positive and significant with ROA.

In contrast, these findings reveal that quick ratio indicates that there has a



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significant but negative relationship with return on asset at level 5% and the probability value of this study is 0.000. This means that quick ratio has an inverse relationship with ROA where once the ROA increase, then the quick ratio will decrease. However, this finding is totally different and in contrast with the finding made by some of the previous researchers Bolek and Wilinski (2012), Vayanos and Wang (2012), Priya and Nimalathasan (2013), and Ruziqa (2013), but corresponding with studies done by Saleem and Rehman (2011) and Khidmat and Rehman (2014).

Inventory turnover also shows that there is a negative relationship but significantly correlated with ROA, similar with findings by Farooq (2019) and Abdillah (2020). While debt ratio is significantly negative with the return on asset as supported by Kebewar (2012) but differs with Ramlan and Nordin, (2018) and Maria and Udeh (2019. Negative means that the debt ratio and ROA have an inverse relationship, meaning to say that if the companies increase in debts, it will lead to a reduction of ROA or profitability value of the company.

5. RECOMMENDATION

From this study, there are a few recommendations to be suggested as there are several gaps in knowledge in these findings that would benefit future research. Since this study has only been able to be conducted in Malaysian Shariah compliance food and beverages industry, future researchers that have better resources and are able to obtain all data are highly advisable to conduct their studies in other countries as well differentiate between non-Shariah companies too with more independent variables (such as external factors) in order to improve the effectiveness of performance determination. Hence, it is more favourable if the analysis tests individually on more companies to indicate individual improvements on the companies’ performances. On top of that, the researchers can obtain different perspectives from different countries when they find out more about regulations and new findings in those countries. Besides, the future researchers can also add more periods of years to get better results. This is one of the ways to obtain better outcomes and make the analysis of companies in different countries more interesting. Moreover, the data finding could also be contrasting with this study as the number of years are increased.

REFERENCES Abdillah, A. N. (2020). Effect of inventory turnover on the level of profitability. Materials Science and

Engineering, 725, 1-5. Abdul Raufu, A., & Ahmad Naqiyuddin, B. (2013). Halal food and products in Malaysia: People’s

awareness and policy implications. Intellectual Discourse, 21(1), 7-32. Akter, A., & Mahmud, K. (2014). Liquidity-profitability relationship in Bangladesh banking industry.

International Journal of Empirical Finance, 2(4), 112-134. Bolek, M., & Wilinski, W. (2012). The influence of liquidity on profitability of polish construction sector

companies. Financial Internet Quarterly, 8(1), 77-89. Chaleda, Md Aminul, I., Tunku Salha, T. A., & Anas, N. M. G. (2019). The effects of corporate

financing decisions on firm value in Bursa Malaysia. International Journal of Economics and Finance, 11(3), 127-135.

Farah, M., & Nina, S. (2016). Factors affecting profitability of small medium enterprise (SMEs) firm listed in Inodnesia Stock Exchange. Journal of Economics, Business and Management, 4(2),

132-137. Farooq, U. (2019). Impact of inventory turnover on the profitability of non-financial sector firms in

Pakistan. Journal of Finance and Accounting Research, 1(1), 34-51. Hamdu, K. M., & Wan Adriana, K. (2016). The impact of total risk management on company’s

performance. Social and Behavioral Sciences, 220, 271-277. Kebewar, M. (2012). The effect of debt on corporate profitability: Evidence from French service sector.



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Master thesis dissertation, Faculté de Droit, d'Economie et de Gestion, Rue de Blois,1-17. Khaldun, K. (2014). The influence of profitability and liquidity ratios on the growth of profit of

manufacturing companies: A study of food and beverages sector companies listed on Indonesia Stock Exchange (period 2010-2012). International Journal of Economics, Commerce and Management, 2(12), 1-17.

Khidmat, W., & Rehman, R. (2014). Impact of liquidity and solvency on profitability chemical sector of Pakistan. Economics Management Innovation, 6(3), 34-67.

Muhammad Hamid, C., Humaira, M., Naila, F., & Aneela, Y. (2017). Capital structure impact om financial performance of Sharia and non-Sharia complaint companies of Pakistan Stock Exchange. International Journal of Business and Management Review, 5(1), 54-70.

Omar, D., Abdul Aziz, A. B., & Syed Ahsan, J., Nour Aldeen, G. (2016). Exploring the relationship between liquidity ratios and indicators of financial performance: An analytical study on food industrial companies listed in Amman Bursa. International Journal of Economics and Financial Issues, 6(2), 435-441.

Priya, K., & Nimalathasan, B. (2013). Liquidity management and profitability: ZA case study of listed manufacturing companies in Sri Lanka. International Journal of Technological Exploration and Learning, 2(4), 135-151.

Ruziqa, A. (2013). The impact of credit and liquidity risk on bank financial performance: The case of Indonesian conventional bank with total asset above 10 trillion Rupiah. International Journal of Economic Policy in Emerging Economies, 6(2), 93-106.

Saleem, Q., & Rehman, R. (2011). Impacts of liquidty ratios on profitability (Case of oil and gas companies of Pakistan). International Journal of Research in Business, 1(7), 78-91.

Siti Nasirah, A. (2017). Firm risk and performance: The role of corporate governance in Dutch Lady Malaysia. Master thesis dissertation, Universiti Utara Malaysia, 1-14.

Valerie, C. S. N. (2017). The relationship between risk and performance of Nestle (Malaysia) Berhad. Master thesis dissertation, Universiti Utara Malaysia, 1-18.

Vayanos, D., & Wang, J. (2012). Liquidity and asset return under asymmetric information and imperfect competition. The Review of Financial Studies, 25(5), 1339-1365.

Wan Shahzlinda, S. S., & Wan Shahdila, S. S. (2015). Impact of firm leverage to performance: Evidence from Shariah and non-Shariah compliant companies in Malaysia. Proceeding of International Conference on Economics and Banking, 140-148.

Zunirah, T., Suhaiza, Z., & Yusserie, Z. (2010). Conceptualizations on the dimensions for Halal orientation for food manufacturers: A study in the context of Malaysia. Pakistan Journal of Social Sciences, 7(2), 56-61.



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Marine Robot

Muhammad Akmal Johari, Norhalida Binti Othman, Noor Hafizah binti Khairul Anuar, Nur Amalina Muhamad & Masmaria Abd Majid

Faculty of Electrical Engineering, Universiti Teknologi MARA Cawangan Johor,

Kampus Pasir Gudang, Bandar Seri Alam, 81750 Masai, Johor

[email protected]

ABSTRACT State of the government found out that the major factor lead to environment cleanliness issue is due to lack of self-awareness among the citizen. The major pollution happen was water pollution where plastic waste were being littering into the ocean, rivers, and lakes. There are a lot of prevention problems that have been implemented such as manual cleaning perform using nets, enforcement of laws, and launched the awareness campaign that does not resulted to a great impression. This project designs the semi-automatic robot called as ‘Marine Robot’ that using Bluetooth module, Arduino Uno as a microcontroller, DC motor, servomotor and ultrasonic sensor to drive the system. The proposed system resulted to semi-automatic water surface cleaning process that could be controlled by human to improve the quality of environment especially in water pollution. In future work, rain sensor can be done to improve the system design. Key Words: Arduino, ocean, pollution

1. INTRODUCTION Water covers 70 percent of our planet. Its existence plays an important role in the lives of the living organisms on this planet. Ocean is a home to millions of species that is a benefit to humans such as fish. In fact, a sixth of the animal protein that human consumes came from the ocean. Oceans also generate half of oxygen and currently contains 97 percent of the world’s water.

The main issue is that the ocean is being polluted by humans. The rubbish they throw in rivers and lakes are dragged by the current and enters the ocean and finally to a place named The Great Pacific Garbage patch. Most trash are plastic in the form of bottles, bag, wrap and much more.

To overcome this problem, several methods have been used. One is to by collecting manually which is by using a net connected to a long pole. Although effective, it takes time and energy to accomplish the task. Even harder should the amount of trash that needs to be clean is larger and requires transportation to reach the area such as the middle of a lake.



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Therefore, this Marine Robot is innovate to lessen the burden for humans to pick up the trash manually as well as trapped the trash easily.

2. OBJECTIVES

1. To design a robotic trash collector system by using Arduino Uno R3 as microcontroller.

2. To control the robotic trash collector by using Bluetooth module.

3. METHODOLOGY a. Block Diagram This system starts with the connection of the ultrasonic sensor as an input as shown in Figure 1. The application in the phone through Bluetooth module will control the movement of the motor. The Arduino Uno R3 is used as the microcontroller. When ultrasonic sensor detects trash, the LED will be on and alert the user. Then user will control the Marine Robot to trap the trash. The trash will be collected by using net that is controlled by servo motor. This process will continue until the net is full and the robot will return to user.

Figure 1: Block Diagram of Marine Robot



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b. Schematic Diagram

Figure 2: Schematic Diagram of Marine Robot

4. RESULTS AND DISCUSSION The Marine Robot will be switched on first before being released on the water. When ultrasonic sensor detects the trash, the servo motor will lower the net and user will control the movement of the DC motor through Bluetooth module. Once the trash has been trapped, the net will be closed.

5. NOVELTY The Marine Robot as a token to prevent water pollution. This project has won ‘Gold Award’ in 2019 International Thinker Innovation & Entrepreneurship Challenge (I-TIEC 2019).

6. CONCLUSION The aim and objectives of this project has been achieved. The prototype of Marine Robot is successfully built and the whole system functioning. The project managed to design a robotic trash collector system by using Arduino Uno R3 as a microcontroller and Bluetooth module to control the robotic trash collector system. However, there are some limitations of the prototype. For example, only trash like plastic only can be trapped by this Marine Robot due to its light in weight. Possible future works will be discussed later. Overall, the system can be implemented successfully. The system provides a cost effective and simple solution to prevent water pollution especially in Malaysia.



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REFERENCES Thiyagesan, Shyam Shankaran, Ravi, Viswesh Kum (2018), Smart Garbage Collector and Disposer Apoorva S., Chaithanya, Rukuma S. Prabhu, Saiswaroop B. Shetty, Denita D’Souza (2017),

Autonomous Garbage Collector Robot. Ruide (Ray) Chen Scott Chu Bao Nguyen Kevin Tan (2010), Automated Garbage Collecting Robot. Osiany Nurlansa, Dewi Anisa Istiqomah, and Mahendra Astu Sanggha Pawitra (2014), AGATOR

(Automatic Garbage Collector) as Automatic Garbage Collector Robot Model Ritika Pahuja, Narender Kumar (2014), Android Mobile Phone Controlled Bluetooth Robot using 8051

Microcontroller



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Mobile Application for Carpooling System in UiTM Seremban Based on Customer Rating

Nurul Najihah bt Hisamuddin, NurFatin Nabilah bt Md Fauzi, Nur

Farrah Ain bt Mohamad Johari & Rosmah Abdul Latif

Faculty Computer and Mathematical Sciences, UiTM Negeri Sembilan,

Persiaran Seremban 3/1, Seremban 3, 70300, Seremban

[email protected]

ABSTRACT Students need a transport to go anywhere they want to go. In Malaysia, Grab and myCar are the most known e-hailing services. For UiTM Seremban students, its hard to get transportation like Grab because it is limited in UiTM Seremban area and because of that, the cost of using this services is a bit high. Most of the passengers want the driver who can provide them a comfortable service, make them feel safe and can minimize risk during their riding. In term of selecting driver, some students prefer to find the driver which suit with his/her need. The use of star rating in this application will help the passenger to rate the driver and based on the rating given it can help the other passenger to decide in choosing the driver according to the ranking. Currently, using grab and myCar, passenger cannot choose their own preferred driver. Therefore, by developing carpooling mobile application, it will give a chance and some advantages to the passenger for deciding by their own, according to their need to choose their own preferred driver. Moreover, it also helps the student to save their time of waiting the driver because the passenger can select nearby driver around them. This carpooling application uses Average Rating method before generates the lists of the driver. The higher rate driver is the top driver while the lowest total rate is placed at the bottom. At the end of the study, it shows the mobile application that lists the driver from the highest rating to the lowest rating based on their average to help the passenger to select their own driver when they want to carpool.

Key Words: e-hailing, carpooling, mobile application, Average Rating method.

1. INTRODUCTION In this growing era, public transportation is an important basis in daily life which is to ease people to move from place to place. Carpooling or car-sharing is a scheme that requires more than one individual to travel in a vehicle and prevents drivers from driving alone. Furthermore, by using carpool system it certainly will save time and cost.

Generally, casual carpooling is the driver will decide to pick up passengers to enable the occupancy of vehicles in order to share the trip cost and on the spot forming the crews,



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Burris and Winn (2006). The instances of organized and most efficient carpooling application are Droupr, lyft, Pool My Ride and many more. Most of these are operating in the overseas. By using carpooling application, it fortuitously will give the occupant a community based and safe travelling experience.

However, according to Correia and Viegas (2011) classical carpooling systems have low flexibility, likewise in the handling of variants of schedules and destinations for sooner or later people who are bugging other people. Therefore, this has resulted only small groups who make positive changes in commute-mode to carpooling to stay with this new mode until they are no more travelling to their workplace, Burris and Winn (2006) .

The issue that escalated concerning past research is whether there is any way to enhance flexibility by considering a distinct type of organizing and supporting this option transportation. Most studies that relates to this study was published a long time ago. Hence, there is a need for more exploration of the sources to improve carpooling usage.

In UiTM Seremban, there is a transportation service that operates around UiTM area. It eases the students who stayed far from home and do not have any transports to move to the destination they want to go to. But the service provided seems disorganized and not systematic. In particular, the transportation service did not have an efficient system. This is because sometimes their service is not available due to transport break down or other reasons. This boldly will provoke the other users who need to go to a destination in a rush due to the van tardiness.

As a result, to encounter this problem we come up with a mobile application related to the

carpooling system among UiTM Seremban residents. The main purpose of this study is to sort the list

of the driver based on customer rating. On top of that, this mobile application provides the list of the

diver that can help the passenger choose the driver on their own.

2. LITERATURE REVIEW Hesitant Fuzzy Linguistic Term Set The purpose is to build the carpooling system that is safer and trusted (Montes, Sanchez, Villar, & Herrera, 2018). Their system gives the detail of information about the driver and passengers. The researches use hesitant fuzzy linguistic term set (HFLTS) as a model on their study. The idea of hesitant fuzzy set HFS was presented in quantitative setting. The function of HFLTS is to help people in making a decision.

Decision making is the process to select the best option that can give a benefit to the user. If the user cannot make a decision, it will give an advice to the user. The model is easy to use and very straight forward so it is very suitable to make a decision. They also said that the passenger user can rate and choose their driver for their safety. In the study, before the passenger want to find the driver, they can search location that they want to go, the detail of the transport and the detail of the driver. Even the driver also can check their passenger’s detail in the application.

Based on the previous study by Deveci, Canıtez, and Gökaşar (2018), they said that fuzzy is use to evaluate some of the uncertainty problem in real life. The decision maker is one of the factors influencing the selection. Therefore, to make a decision, the values of the qualitative criteria that will be gained are not accurate. It is because from the individual evaluations, there are normally characterized as “very low”, “low”, “medium”, “high”, “very high”. It is hard to precisely measure the rating of every option. There are the definitions of HFLTS. Hs is an order unlimited subset of consecutive linguistic terms. Average Rating Some of the website do some comparison about something and rank it based on the rating that have been given by the people. For example, to know the best movie among all the movie that have been released based on star rating, the best food in some area or the best



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hotel to stayed. Mostly the rating is very important to other customers to make a decision. Ganu, Elhadad, and Marian (2009) utilize an average rating to locate the true quality dependent on the product review. They just focused on the rating and based on the rating review the average rating are used before it is ranked. The normal rating given by all audits, object with the most elevated normal rating will show up at the highest point of the rank rundown. The strategy is rank the item effectively as per the quality. The various sources have an alternate rating scale, for example, 1-5 star, 0-10 stars and so on.

3. METHODOLOGY 3.1. Research Goal Average rating is the model we use as a benchmark measure. The estimated quality of an object is the average rating it has received from all the passenger feedback. This method is the one of the calculation methods with using the rating review value from the user that have been use by the other researcher before on their research. This is the straightforward technique that can help to find the average value. It also is an important calculation that are needed before the system make a rank list by sorting from the highest rating star to the lowest. The method is suitable with this research on this application when the customers want to make a decision on choosing the best driver based on their ranking. Because the best driver has the highest average. 3.2. Data Collection The student and staff details such as IDs, name and phone number will be synchronized to the UiTM database. Moreover, for the address and distance in this study will be gathered by the drives and the passengers, when they start to search their location in the application. Their location will be stored in firebase. 3.3. Project Development We already done with some test on the app to ensure the result that relate with the calculation are accurate. The ranking will be range based on the rating star that the passenger rate to the driver. Based on the steps to implement the Average Rating method, previous rating and the new rating are needed in the calculation. The value of rating is 0 until 5. Calculation process is done in the background of the app. The result shows the average value of each driver that meant the driver is automatically arranged based on the calculation value. Moreover, the app has the page that need the passenger to rate the driver based on their preference. Equations Below is the Average Rating method used to calculate the average value before sort it into rank. as true quality, represents the rating that be given to the passenger and sssrepresents the rating that be given by the passenger. But this research it will be 2 because we assume as a submission of new rating and old rating.

= current rate. = newRating + oldRate.

= 2. (1)

, show that the previous rate is based on the rating that given by the

current passenger.



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The formula was transformed to a simplest formula. It is because the formula can easily to use in the coding and can generate the result that we want to achieve in this research.

Calculation Process Calculation process is based on the star rating that have been given by the past passenger. To make the calculation going very passenger must rate the driver. oldRate = previousRate

Table 1. The current rate for every driver based on old passenger and new

passenger when starting using the application

Driver Passenger 1 Passenger 2 Current Rate

Driver 1 3 5 4

Driver 2 4 2 3

Driver 3 3 - 1.5

Driver 4 5 - 2.5

Driver 5 3 1 2

The value of passenger 1 is the value for new rating while passenger 2 is the old rating. In order to get current rate, the old rate must be added to the new rating and divided by 2.

Table 2. The current rate for every driver when keeps getting the passengers

Driver New Passenger Current Rate

Driver 1 3 4

Driver 2 3 2.5

Driver 3 3 3

Driver 4 5 5

Driver 5 2 1.5

Next, when there is a new passenger, the rating from the new passenger will hold the new rating value while passenger 2 from Table 1 will become previous rate. To get the current rating for every each of the driver, add up both value and then divide by 2. The process will calculate current rate repeatedly as there are new passenger.



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if(dataSnapshot.exists()) {

//////////calculate the average of driver rating/////////////

String previousRate = (String) dataSnapshot.getValue().toString();

oldRate = Float.parseFloat(previousRate);

FloatcurrRate = (Float) (getrating + oldRate) / 2;

upRating.setValue(currRate);

}

else{

upRating.setValue(getrating);

}

Coding Process

Figure 1. The coding for calculate the average of driver rating

The figure above shows the code to get the value of rating and calculate the average of the rate. These values will be used to sort the rate of the driver on list of drivers.

Figure 2. The coding of sorting the list of drivers based on rating

The Figure 2 shows the code for sorting the drivers list. In this application, the driver list will be sorted descending based on the rating star that rate by customers.

4. INTERFACE RESULT

Both of the interface appeared when the user is the passenger. Most of the application that have been developed have rating interface either rating star or other else. The star rating are the important component in this system to be calculate. In this mobile application the interface a) appear until the passenger already arrived at their destination. After the passengers arrived at their destination, the system will ask the passenger to give a rating to their driver based on their satisfaction. While interface b) shows the list of the driver who available to pick up the passenger around their current location which is within 5km. The driver detail was shown in this app to help the passenger to get the details of the driver, the value of scale that have been given by the past passenger also their current distance. The list of the driver who are available was ranked based on the rate given by all passengers.

Collections.sort(resultDriver, new

Comparator<DriverObject>() { @Override

public int compare(DriverObject lhs, DriverObject rhs) {

// -1 - less than, 1 - greater than, 0 - equal, all

inversed for descending

return lhs.getRatingStar() > rhs.getRatingStar()

? -1 : (lhs.getRatingStar() <

rhs.getRatingStar() ) ? 1 : 0;

}

});



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(a) (b)

Figure 3. (a) User Interface for rating and arriving destination; (b) User Interface to Lists Driver.

5. CONCLUSION In this research, the Average Rating method is used to calculate and show the average value of each driver's rating. While to sorting the average value on the list need to use the If Else coding. The objectives of this mobile application is to ease passenger choosing the driver based on their preference and also to sort the list of driver based on passenger rating. The driver and passenger must be have the UiTM Id number that register under Seremban Campus. This application provided the list of the driver that have been sorted form the highest rating to the lowest rating. It also allows the driver to accept more than one passenger request before passenger's name listed in the driver's pickup list. This application will help the passenger to choose the suitable driver who provide a good service based on the past passenger rating feedback.

REFERENCES Başaran, S., & Haruna, Y. (2017). Integrating FAHP and TOPSIS to evaluate mobile learning

applications for mathematics. Procedia Computer Science, 120, 91-98. doi:https://doi.org/10.1016/j.procs.2017.11.214

Berrittella, M., Certa, A., Enea, M., & Zito, P. (2007). An analytic hierarchy process for the evaluation of transport policies to reduce climate change impacts.

Bruglieri, M., Ciccarelli, D., Colorni, A., & Luè, A. (2011). PoliUniPool: a carpooling system for universities.Procedia - Social and Behavioral Sciences, 20, 558-567. doi:https://doi.org/10.1016/j.sbspro.2011.08.062

Burris, M., & Winn, J. (2006). Slugging in Houston — Casual Carpool Passenger Characteristics (Vol.

9). Chakraborty, T., Ghosh, T., & Dan, P. K. (2011). Application of analytic hierarchy process and

heuristic algorithm in solving vendor selection problem. Business Intelligence Journal, 4(1), 167-177.

Deveci, M., Canıtez, F., & Gökaşar, I. (2018). WASPAS and TOPSIS based interval type-2 fuzzy



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MCDM method for a selection of a car sharing station. Sustainable Cities and Society, 41, 777-791.doi:https://doi.org/10.1016/j.scs.2018.05.034

Ganu, G., Elhadad, N., & Marian, A. (2009). Beyond the stars: improving rating predictions using review text content. Paper presented at the WebDB.

Hussain, I., Knapen, L., Galland, S., Yasar, A.-U.-H., Bellemans, T., Janssens, D., & Wets, G. (2016). Organizational-based model and agent-based simulation for long-term carpooling. Future Generation Computer Systems, 64, 125-139. doi:https://doi.org/10.1016/j.future.2016.02.019

Ibrahim, Z. (2014). A Comparative Study of the AHP and Topsis Methods for Implementing Load Shedding Scheme in a Pulp Mill System. Universiti Tun Hussein Onn Malaysia.

Kosacka-Olejnik, M., Werner-Lewandowska, K., Geisler, A., & Marciniak, F. (2018). Which Option of Sustainable Mobility Should Be Chosen?–Ahp Method Application For Sustainable Decision Making In Transport. Acta Technica Napocensis-Series: Applied Mathematics, Mechanics, and Engineering, 61(3_Spe).

Montes, R., Sanchez, A. M., Villar, P., & Herrera, F. (2018). Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust. Applied Soft Computing, 67, 941-952. doi:https://doi.org/10.1016/j.asoc.2017.05.039

Ren, F., Kong, M., & Pei, Z. (2017). A new hesitant fuzzy linguistic TOPSIS method for group multi-criteria linguistic decision making. Symmetry, 9(12), 289.

Yadav, V., Karmakar, S., Kalbar, P. P., & Dikshit, A. K. (2019). PyTOPS: A Python based tool for TOPSIS. SoftwareX, 9, 217-222. doi:https://doi.org/10.1016/j.softx.2019.02.004

PROCEEDINGS

Nurture Young Research Talent, Series 2Nur Fadhlina Zainal Abedin, Editor

This proceedings feature articles submitted for Final Year Project & Postgraduate

Poster Competition (FYPPPC), Series 2, 2020 organised by MNNF Network. FYPPPC

is a platform for graduate and postgraduate students to present their final year projects

and thesis (or dissertation) in the field of science, engineering, technology, social

sciences and humanities.

proceedings nurture young research talent

Documents