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All Journal The IJICS (International Journal of Informatics and Computer Science)
Sinambela, Sugi Hartono
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Comparative Analysis of Sequencing Methods and Markov Models for Predicting High-Achieving Students at Budi Darma University Sinambela, Sugi Hartono; Iqbal, Muhammad; Khairul, Khairul; Darmeli Nasution; Zulham Sitorus
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8964

Abstract

The prediction of high-achieving students is a strategic step in supporting the development of academic quality within higher education institutions. This study aims to compare two data mining approaches, namely the Sequencing method and the Markov Model, in predicting high-achieving students at Universitas Budi Darma Medan. The Sequencing method is used to identify patterns in the sequence of academic grades and non-academic activities of students from semester to semester, while the Markov Model is used to calculate the probability of transitions in students' academic status based on historical data. The research adopts a quantitative approach involving 100 active students with complete academic and non-academic data. The data analyzed include semester GPA, participation in organizations, seminars, and achievements in competitions. Both methods were evaluated using metrics such as accuracy, precision, recall, and F1-score. The evaluation results show that the Sequencing method achieved an accuracy of 87%, precision of 85%, recall of 88%, and an F1-score of 86%, while the Markov Model recorded an accuracy of 81%, precision of 79%, recall of 83%, and an F1-score of 81%. Based on these results, the Sequencing method is considered superior in detecting patterns and providing more accurate predictions of students’ achievement potential. The comparison of these two methods provides a foundation for institutions to develop more accurate, objective, and comprehensive student achievement prediction systems. Thus, universities can implement early and well-targeted interventions and guidance.
Implementation of the MARCOS Method in a Decision Support System for Foundation Scholarship Determination Sinambela, Sugi Hartono; Rajagukguk, Denni M; Pristiwanto; Muhammad Iqbal Batubara; R.L harmady Tamba
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9574

Abstract

The scholarship selection process often involves multiple criteria and is prone to subjectivity when conducted manually. This study aims to implement the Multi-Attributive Ideal-Real Comparative Analysis (MARCOS) method in a Decision Support System (DSS) to determine foundation scholarship recipients objectively and systematically. The research applies a quantitative approach by evaluating several student alternatives based on academic and non-academic criteria, including academic achievement, parents’ income, number of dependents, organizational activity, and social status. The MARCOS method is employed through decision matrix construction, normalization, weighting, utility value calculation, and ranking. The results indicate that the proposed system is able to generate clear and consistent rankings of scholarship candidates. Validation results show an accuracy of 80% when compared with the foundation’s manual decision process. These findings demonstrate that the MARCOS-based Decision Support System can improve accuracy, transparency, and efficiency in scholarship determination and can be adapted to other multi-criteria decision-making problems.
Co-Authors Darmeli Nasution Denni M Rajagukguk Khairul Khairul, Khairul Muhammad Iqbal Muhammad Iqbal Batubara Pristiwanto R.L harmady Tamba Sitorus, Zulham