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Pratama, Sutan Abeng
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Computer Science & IT

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Sistem Penjadwalan Otomatis Menggunakan Algoritma Genetika pada Lingkungan Sekolah Pratama, Sutan Abeng
Jurnal Komputer Vol 3 No 2 (2025): Januari- Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v3i2.111

Abstract

Scheduling problems in school environments often present significant challenges due to the involvement of numerous variables and constraints, such as teacher availability, classroom allocation, and balanced subject distribution. Manual scheduling tends to be time-consuming and prone to errors, necessitating a more efficient and adaptive solution. This study aims to design and implement an automatic scheduling system using a Genetic Algorithm. This algorithm is chosen for its capability to solve optimization problems with complex solution spaces. The development process involves representing chromosomes as combinations of schedule elements, selecting based on conflict levels, and applying genetic operators such as crossover and mutation to generate optimal solutions. Test results show that the system is capable of producing high-quality schedules with minimal conflicts and efficient computation time. This approach significantly enhances the speed, accuracy, and flexibility of school scheduling systems
Pengembangan Sistem Rekomendasi Buku Menggunakan Collaborative Filtering Pratama, Sutan Abeng
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.112

Abstract

In the rapidly evolving digital era, the need for accurate and personalized recommendation systems is increasingly important, particularly in digital libraries and online bookstores. This study aims to develop a book recommendation system using a collaborative filtering approach, which leverages user interaction data to suggest books that align with individual preferences. The system utilizes a user-based collaborative filtering method by calculating similarities between users based on their historical book ratings. The dataset used in this research is a simulated, anonymized dataset from a school library. Testing results indicate that the system is capable of delivering relevant recommendations with good accuracy, demonstrated by a low Mean Absolute Error (MAE) score and positive user feedback. This system allows users to discover books aligned with their interests more efficiently, thereby enhancing the overall reading experience.
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