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All Journal CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Aldyth Najma Rova Marthin
Universitas Sam Ratulangi
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Applying Analytical Hierarchy Process in a Decision Support System for Study Program Recommendation Aldyth Najma Rova Marthin; Mahardika Inra Takaendengan; Marline Sofiana Paendong
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.274

Abstract

Choosing a study program is a critical academic decision because it affects students' learning direction, skill development, and career readiness. This study designs, implements, and evaluates a web-based Decision Support System for study program recommendation using the Analytical Hierarchy Process. The model uses four criteria: interest and talent, technology-related hobby, academic score, and job prospects. The research used teacher criteria data before web implementation and student alternative data after the system was implemented. Teacher matrices were screened using the Consistency Ratio requirement, and the valid matrix produced criteria weights of 0.436 for interest and talent, 0.320 for job prospects, 0.192 for technology-related hobby, and 0.053 for academic score. The system was developed with Python and Flask, then evaluated using Black Box Testing and User Acceptance Testing. The main scenario produced Informatics Engineering as the first recommendation with a score of 0.4880 or 49 percent. Across 24 post-implementation student responses, Informatics Engineering was also the most frequent top recommendation, appearing in 10 responses, followed by Mathematics in 9 responses. However, only 16 of 96 student alternative matrices met the CR threshold, which indicates that automatic consistency validation is needed. Black Box Testing confirmed that all tested core functions worked as expected, and UAT produced an acceptance percentage of 81 percent. These results show that the proposed system can provide systematic and usable recommendation support, while consistency control remains the main technical improvement needed.
Co-Authors Mahardika Inra Takaendengan Marline Sofiana Paendong