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All Journal International Journal of Supply Chain Management Indonesian Journal of Education and Social Science
Mazlan, Ahmad Uzair
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Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Transportation Other

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Factors Influencing Sustainable Supplier Selection: Evidence from Palm Oil Refining and Oleochemical Manufacturing Industry Vijayakumaran, Suresh Anand; Abdul Rahim, Suzari; Ahmi, Aidi; Abdul Rahman, Nor Aida; Mazlan, Ahmad Uzair
International Journal of Supply Chain Management Vol 9, No 1 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (340.665 KB)

Abstract

This study focuses on the sustainable supplier selection in Malaysia palm oil refining and oleochemical manufacturing industry. A sustainable supply chain could lead to smoothness in the long run and thus ensure higher excellence and productivity. It is important to have suppliers that adapt to the sustainability nature of the organisation in order for the entire supply chain to be productive. The independent variables which have been used for this study are the unit price, CSR engagement, and environmental competencies. The leading theory used to support this study is the triple bottom line theory and backed by the resource dependency theory and stakeholder’s theory. This study was aided by surveying 151 respondents who are directly from palm oil refining and oleochemical manufacturing sector, which is the population for this study. After using the statistical tools to analyse the data retrieved from the questionnaires, it has been inferred that the unit price, quality, and environmental competencies have a significant effect on the dependent variable whereas CSR Engagement does not have a significant relationship. This study highlights the implication in the form of theoretical and social perspectives. It concludes with the limitation and the future recommendation of sustainable supplier selection in Malaysia palm oil refining and oleochemical manufacturing industry.
Design and Development of the Students Performance Prediction Modul Using Open Education Resource Ibrahim, Abu Bakar; Sauji, Anis Athira; Mazlan, Ahmad Uzair; Dzulkifly, Summayyah
Indonesian Journal of Education and Social Sciences Vol. 4 No. 1 (2025)
Publisher : Papanda Publishier

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56916/ijess.v4i1.1144

Abstract

Data mining is a tool to assist in decision-making. It is a modern technique, and it is still at a low level of implementation in this approach in Malaysia. In the market sector, data mining methods are usually used specifically to understand, predict, and schedule, which will improve organizational results. This project aims to predict student performance in MyGuru using activity log data. The prediction in this study helps to see a clear vision of student patterns and the activities they access most when logging in to MyGuru. With the resulting prediction model, student performance can be detected, which is influenced by the activities while accessing MyGuru. In developing this research, the main important part is extracting features. Feature extraction needs to be done clearly so that accuracy values ​can be achieved. The features in this study are selected from the attributes in the activity log. After converting the raw data, the data becomes a new dataset that is used to create a model according to the classifier. The classifiers used in this research are Random Forest, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and J48, which are involved in developing the model. The most accurate classifier for predicting student performance was Random Forest, which was 96.9%. Students, while using the MyGuru system, with the total of courses viewed was 15.68%, out of a total number of 644 people.The findings show that accuracy cannot be obtained if the original dataset has some issues, such as unbalanced data. Imbalanced data can affect accuracy.
Co-Authors Abdul Rahim, Suzari Abdul Rahman, Nor Aida Abu Bakar Ibrahim Ahmi, Aidi Dzulkifly, Summayyah Sauji, Anis Athira Vijayakumaran, Suresh Anand