Sofianita Mutalib

Work place: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor



Research Interests: Data Structures and Algorithms, Data Mining, Information Systems


Sofianita Mutalib is a senior lecturer of Information System in Universiti Teknologi Mara. She received a master’s degree in Information Technology in Universiti Kebangsaan Malaysia in 1998. She teaches courses related to intelligent system development, decision support systems and data mining. Her primary research interests involve the intelligent systems, data mining as well as machine learning.

Author Articles
PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform

By Mohamed Zaim Shahrel Sofianita Mutalib Shuzlina Abdul Rahman

DOI:, Pub. Date: 8 Feb. 2021

In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.

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Prediction of Mental Health Problems among Higher Education Student Using Machine Learning

By Nor Safika Mohd Shafiee Sofianita Mutalib

DOI:, Pub. Date: 8 Dec. 2020

Today, mental health problems become serious issues in Malaysia. In generally, mental health problems are health issues that effects on how a person feels, thinks, behaves, and communicate with others. According to National Health and Morbidity Survey (NHMS) 2017, one in five people in Malaysia is depression. Then, two in five people is anxiety and one in ten people is having stress. Higher education student also one of communities that have high risk to face mental health problems. The difficulties in identifying factors of mental health problems become a challenges and obstacle to help the person with mental health problem. Objectives of this paper are (1) review mental health problem among higher education student, (2) the contributing factors and (3) review the existing machine learning to analyse and predict mental health problem among higher education student. Finding of the paper will be used for other study to further discussion on mental health problems for implementation using computational modelling.

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A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

By Syaidatus Syahira Ahmad Tarmizi Sofianita Mutalib Nurzeatul Hamimah Abdul Hamid Shuzlina Abdul Rahman

DOI:, Pub. Date: 8 Aug. 2019

Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students’ information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.

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Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

By Nurafifah Mohammad Suhaimi Shuzlina Abdul Rahman Sofianita Mutalib Nurzeatul Hamimah Abdul Hamid Ariff Md Ab Malik

DOI:, Pub. Date: 8 Jul. 2019

Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students’ achievement, university’s performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students’ academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students’ graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naïve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students’ graduation time.

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