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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences
Rozita Jailani, Rozita
Universiti Teknologi MARA
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Feature selection techniques and classification algorithms for student performance classification: a review Alias, Muhamad Aqif Hadi; Hambali, Najidah; Abdul Aziz, Mohd Azri; Taib, Mohd Nasir; Jailani, Rozita
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3230-3243

Abstract

The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.
Evaluating telemedicine diabetes mellitus: a mobile health app for type-2 diabetes Karim, Muhammad Zakwan Abdul; Thamrin, Norashikin M.; Shauri, Ruhizan Liza Ahmad; Jailani, Rozita; Manaf, Mohd Haidzir Abd; Mustapa, Nurul Amirah
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp787-795

Abstract

Telemedicine diabetes mellitus (Tele-DM) mobile health (mHealth) tool functionality, usefulness, and user feedback were examined in this study. Data from nine distinct users of type-2 diabetes (T2D) patients, healthcare professionals (HCPs), and administrators was analyzed to determine functionality. Data retrieval times increased with database user data amount, according to the study. A 3-month program with five T2D patients reduced weight (0.98 kg) and Hemoglobin A1c (HbA1c) (0.34%). This shows that Tele-DM helps manage diabetes, but more participants are needed to confirm. Nine Tele-DM customers were satisfied with the app's reception, according to 14 online questionnaires. Overall, Tele-DM simplifies diabetic self-management in a novel way. This study shows its potential to transform diabetes management and address major healthcare issues.
Co-Authors Abdul Aziz, Mohd Azri Alias, Muhamad Aqif Hadi Hambali, Najidah Heru Supriyono Karim, Muhammad Zakwan Abdul Manaf, Mohd Haidzir Abd Mohd Nasir Taib Mustapa, Nurul Amirah Shauri, Ruhizan Liza Ahmad Thamrin, Norashikin M. Tokhi, Mohammad Osman Wijayanto, Anugrah Adi