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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Reputasi: Jurnal Rekayasa Perangkat Lunak
Tanhella Zein Vitadiar
Universitas Hasyim Asy'ari
Computer Science & IT

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Optimisation of TNI Personnel Postings Using an Android-Based AHP System Syahfrizal Fakhri Irawan; ginanjar setyo permadi; Tanhella Zein Vitadiar
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Mei 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v7i1.12683

Abstract

Efficiency and objectivity in the placement of Indonesian National Armed Forces (TNI) personnel are crucial aspects of military human resource management. This study aims to develop an Android-based personnel placement recommendation system using the Analytic Hierarchy Process (AHP) method to support faster, more accurate, and consistent decision-making. The study population comprised 300 active TNI personnel, with a purposive sample of 120 personnel possessing complete data on rank, length of service, education, competency, and disciplinary records. Data were collected from internal documents, personnel records, performance reports, and information from the TNI Air Force Planning and Budget Staff. The results indicate that rank and length of service are the dominant criteria, while education, competency, and disciplinary records provide additional contribution. The system generates objective, consistent, and user-friendly recommendations, validated through functionality and user acceptance testing. The findings demonstrate that implementing AHP in an Android-based recommendation system enhances efficiency, transparency, and objectivity in personnel placement and has potential for adoption in other TNI units or similar military organizations
Decision Support System for Identifying Student Learning Styles in Elementary School using Naïve Bayes Algorithm ikhsanul khakim; Ahmad Heru Mujianto; Tanhella Zein Vitadiar; Chamdan Mashuri
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.39725

Abstract

Identifying student learning styles is essential for teachers to design effective and adaptive teaching strategies. At SDN Rejoagung 3, this process is currently conducted manually through observation and interviews, which are prone to subjective bias. This research develops a web-based decision support system to classify student learning styles—Visual, Auditory, and Kinesthetic—using the Naïve Bayes algorithm. The system was built using data collected via questionnaires from students in grades 1 to 6. Testing was conducted using a confusion matrix to evaluate the model's performance. The results show that the Naïve Bayes algorithm successfully classified learning styles with an accuracy of 94.12%. This system provides a more objective and systematic tool for teachers to identify students' preferences, enabling more personalized instructional delivery in an elementary school context
Co-Authors Ahmad Heru Mujianto Chamdan Mashuri Ginanjar Setyo Permadi ikhsanul khakim Syahfrizal Fakhri Irawan