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All Journal JOIV : International Journal on Informatics Visualization
Mat Jusoh, Ruzanna
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes Binti Muhammad Zahruddin, Nursyuhadah Alghazali; Kamarudin, Nur Diyana; Mat Jusoh, Ruzanna; Abdul Fataf, Nur Aisyah; Hidayat, Rahmat
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2454

Abstract

This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.
A Mixed Integer Linear Programming for Exam-Invigilator Assignment Problem: A Case Study at Universiti Pertahanan Nasional Malaysia Irfan Hanafi, Muhammad Aiman; Syed Ali, Sharifah Aishah; Mat Jusoh, Ruzanna; Ali, Fazilatulaili; Abd Rahman, Norzaura
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2196

Abstract

The assignment of invigilators for examinations is a complex and challenging task, particularly when faced with numerous factors that must be carefully considered. Critical elements are essential in this process, including staff availability, room capacity, and time constraints, requiring thorough evaluation and coordination. This paper focuses on improving the allocation of invigilators for examinations at Universiti Pertahanan Nasional Malaysia (UPNM). The issue arises when academic staff members responsible for teaching the subject are also assigned as exam invigilators, which conflicts with their primary role of assisting students in addressing their queries during examinations. It is essential to reconsider the distribution of invigilator roles, ensuring that academic staff members can focus solely on providing educational support. In contrast, qualified non-academic staff handle invigilation duties effectively. A mixed-integer linear programming (MILP) model is formulated using the existing examination timetable to solve this problem. The model is solved using a simple algorithm implemented in the XPress MP programming language, resulting in an improved solution that requires less computational effort than the conventional method. This approach offers an alternative and better solution for scheduling examination invigilators at UPNM, ensuring the efficient and effective management of exam procedures while maximizing the utilization of available resources. It can serve as a starting point for future investigations into UPNM's scheduling procedures.
COVID-19 Social Distancing Tracking and Monitoring System (SDMOS-19) Binti Abdullah, Nurafrina Arrysya; Kamarudin, Nur Diyana Binti; Makhtar, Siti Noormiza; Mat Jusoh, Ruzanna; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1199

Abstract

The Coronavirus disease (COVID-19), stemming from the SARS-CoV-2 virus, has garnered global concern as a virulent infectious ailment. Recognized as an epidemic by the World Health Organization (WHO), the persistently mutating virus sustains its transmission within communities. Individuals have been advised to uphold a safe interpersonal distance, notably around five feet, to mitigate its spread during social interactions. Addressing this imperative, an innovative automated social distancing detection system is conceived, leveraging the Convolutional Neural Network (CNN) algorithm. This system operates on two distinct input modes: static images and recorded videos recorded on closed-circuit television (CCTV). Remarkably, the proposed automated system adeptly quantifies and surveils the extent of social distancing among individuals in densely populated settings. A sophisticated framework accurately discerns social distancing compliance, delineating between hazardous and secure intervals via distinct red and green bounding box indicators. The culmination of this endeavor reveals an impressive 90% detection accuracy for both input modes. Notably, this proposed system holds substantial promise for implementation within sprawling premises such as expansive shopping malls or recreational parks. Seamlessly enforcing automated safety distance assessment expedites real-time insights to security departments and other relevant authorities. Consequently, the efficacy of citizens in upholding safe interpersonal distances can be promptly evaluated and, if necessary, corrective measures can be expeditiously instituted. This automated system ensures public health and safety maintenance, particularly in difficult circumstances.
Co-Authors Abd Rahman, Norzaura Abdul Fataf, Nur Aisyah Alde Alanda, Alde Ali, Fazilatulaili Binti Abdullah, Nurafrina Arrysya Binti Muhammad Zahruddin, Nursyuhadah Alghazali Irfan Hanafi, Muhammad Aiman Kamarudin, Nur Diyana Kamarudin, Nur Diyana Binti Makhtar, Siti Noormiza Rahmat Hidayat Syed Ali, Sharifah Aishah