Shuzlina Abdul Rahman

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



Research Interests: Computer systems and computational processes, Data Mining, Data Structures and Algorithms


Dr. Shuzlina Abdul Rahman is an Associate Professor of Information System in Universiti Teknologi Mara. She received a master’s degree in information technology in Universiti Utara Malaysia in 2000. In 2012, she received her PhD degree from the Universiti Kebangsaan Malaysia. She teaches courses related to the philosophy of artificial intelligence, data mining, advanced decision support systems, business intelligence and intelligent systems development. Her primary research interests involve the computational intelligence, data mining and optimization and intelligent data analytics.

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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House Price Prediction using a Machine Learning Model: A Survey of Literature

By Nor Hamizah Zulkifley Shuzlina Abdul Rahman Nor Hasbiah Ubaidullah Ismail Ibrahim

DOI:, Pub. Date: 8 Dec. 2020

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

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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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