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All Journal Jurnal Kajian Ilmiah Journal of Computer Engineering: Progress, Application and Technology
Nasir, Alfian
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Telkom Bandung vocational school scheduling application using a website-based genetic algorithm Ramadhani, Putri; Nasir, Alfian; Abdullah, Zakia Mahbub; Novianty, Astri; Setianingsih, Casi
CEPAT Journal of Computer Engineering: Progress, Application and Technology Vol 4 No 02 (2025): November 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cepat.v3i01.6709

Abstract

Scheduling is a very important factor for the learning process at school, it is also very important at SMK Telkom Bandung. At this school, the schedule is still done manually. Because of this, many problems occurred, there were schedule clashes and the teaching and learning process was hampered. Therefore, a scheduling system was created using a genetic algorithm which is one of the optimization algorithms and can be used in various case studies such as scheduling subjects at school. With the application of the genetic algorithm, it can produce an automatic and optimal subject schedule. From several trials on the genetic algorithm, the best fitness value is 1 with an average execution time of 14.42990657 seconds. The results of alpha testing and beta testing show that the website created is running well and is feasible to use. With this system, it is hoped that it will make it easier for admins, teachers and students to access schedules.
Pengolahan Data Menggunakan Algoritma Untuk Sistem Pendukung Keputusan Karyawan Terbaik Bawiling, Hendry; Saputra, Indra; Nasir, Alfian; Tundjungsari, Vitri
Jurnal Kajian Ilmiah Vol. 26 No. 1 (2026): Januari 2026
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/pesfvt11

Abstract

Identifying high-performing employees is a critical component of human resource management, as it directly influences organizational productivity, work climate, service quality, and strategic goal achievement. However, conventional employee performance assessments often rely on subjective managerial judgment, making them vulnerable to personal bias and inconsistencies that can lead to dissatisfaction, decreased morale, and internal conflict. To address these challenges, Decision Support Systems (DSS) that employ data-processing algorithms have been increasingly adopted to enhance objectivity and accuracy in employee evaluation. This study conducts a Systematic Literature Review (SLR) of 25 scholarly publications published between 2017 and 2025 and indexed in nationally and internationally recognized databases. The analysis focuses on the types of algorithms applied, system development methodologies, and their relevance to optimizing the identification of top-performing employees. The findings indicate that multi-criteria decision-making methods, particularly the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW), are the most frequently used algorithms, followed by TOPSIS, PROMETHEE, MABAC, ELECTRE, Weighted Product, SMART, and hybrid approaches. In terms of system development, several studies did not explicitly specify their methodology, while others adopted structured approaches such as the System Development Life Cycle (SDLC) and Waterfall models. This review highlights methodological trends, identifies research gaps, and proposes potential directions for future studies on algorithm-based DSS applications in employee performance evaluation
Co-Authors Abdullah, Zakia Mahbub Bawiling, Hendry Casi Setianingsih Indra Saputra Novianty, Astri Putri Ramadhani, Putri Vitri Tundjungsari