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All Journal JOIV : International Journal on Informatics Visualization International Journal of Advanced Science Computing and Engineering
Saringat, Mohd Zainuri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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University Course Timetabling System For Part-Time Students Ruslaan, Mohd Asyraf; Zakaria, Zalmiyah; Saringat, Mohd Zainuri; Kasim, Shahreen
International Journal of Advanced Science Computing and Engineering Vol. 1 No. 2 (2019)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (297.012 KB) | DOI: 10.62527/ijasce.1.2.5

Abstract

University timetabling system is a part of a timetabling problem that aims to produce course timetable that meets student needs such as the maximum number of subjects that can be offered, the maximum number of elective subjects that can be offered and the number of subject students can take. In every semester, the timetabling process in UTMSPACE is done manually where there are likely to be a small number of students who will have problems because the subject to be taken is not in the subjects offering list. Additionally, the number of subjects offered is also not optimal and this will result in a loss on UTMSPACE because each subject is offered at a cost. Therefore, in order to solve this problem, the Heuristic-based approach is used to overcome the problems mentioned and speed up the process to generate timetable. Heuristic engines have been developed using PHP language programming. This approach has been successfully tested and implemented using real-time scheduling data at UTMSPACE for Software Engineering course. The results show that Heuristics has successfully solved the problem of producing a timetable without affecting students who want to enroll the subject offered.
A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment Hassan, Mustafa Hamid; Mostafa, Salama A.; Baharum, Zirawani; Mustapha, Aida; Saringat, Mohd Zainuri; Afyenni, Rita
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1228

Abstract

The risk assessment of air pollution is an essential matter in the area of air quality computing. It provides useful information supporting air quality (AQ) measurement and pollution control. The outcomes of the evaluation have societal and technical influences on people and decision-makers. The existing air pollution risk assessment employs different qualitative and quantitative methods. This study aims to develop an AQ-risk model based on the Nested Monte Carlo Simulation (NMCS) and concentrations of several air pollutant parameters for forecasting daily AQ in the atmosphere. The main idea of NMCS lies in two main parts, which are the Outer and Inner parts. The Outer part interacts with the data sources and extracts a proper sampling from vast data. It then generates a scenario based on the data samples. On the other hand, the Inner part handles the assessment of the processed risk from each scenario and estimates future risk. The AQ-risk model is tested and evaluated using real data sources representing crucial pollution. The data is collected from an Italian city over a period of one year. The performance of the proposed model is evaluated based on statistical indices, coefficient of determination (R2), and mean square error (MSE). R2 measures the prediction ability in the testing stage for both parameters, resulting in 0.9462 and 0.9073 prediction accuracy. Meanwhile, MSE produced average results of 9.7 and 10.3, denoting that the AQ-risk model provides a considerably high prediction accuracy.
Co-Authors Aida Mustapha, Aida Baharum, Zirawani Hassan, Mustafa Hamid Mostafa, Salama A. Rita Afyenni Ruslaan, Mohd Asyraf Shahreen Kasim, Shahreen Zakaria, Zalmiyah