Penerapan Algoritma Genetika untuk Rekomendasi Topik Skripsi Berdasarkan Minat dan Kompetensi Mahasiswa
DOI:
https://doi.org/10.62951/bridge.v3i4.695Keywords:
academic competence, genetic algorithm, recommendation system, student interests, thesis topicAbstract
The selection of a thesis topic that aligns with students’ interests and competencies often poses a challenge in academic environments. Inappropriate topic selection can lead to decreased motivation and delays in completing the final project. This study aims to develop a thesis topic recommendation system based on a genetic algorithm that considers students’ interests and academic abilities. The data used include grades from core courses, results of research interest questionnaires, and a list of thesis topics provided by academic supervisors. Each topic is represented as a chromosome, while the fitness function is calculated based on the level of compatibility between student attributes and topics. The selection process employs the roulette wheel method, with single-point crossover and random mutation to generate an optimal solution population. The test results show that the recommendation system based on the genetic algorithm achieves an accuracy rate of 86.7%, higher than the keyword-matching method, which only reaches 71.2%. Therefore, this approach is proven effective in assisting students to determine thesis topics that are suitable, objective, and efficient.
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Ahmad, R., Suryana, D., & Rahman, M. (2023). Penerapan Algoritma Genetika dalam Optimasi Rekomendasi Topik Tugas Akhir Mahasiswa. Jurnal Teknologi Informasi dan Komputer, 9(2), 55-64.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. The University of Michigan Press.
Kurniawan, Y., & Nurhadi, D. (2020). Aplikasi Algoritma Genetika dalam Penentuan Rute Perjalanan Menggunakan Sistem Navigasi. Jurnal Teknologi dan Informatika, 9(2), 45-53.
Pramudita, I., & Prasetyo, H. (2021). Implementasi Algoritma Genetika dalam Optimasi Jadwal Kuliah. Jurnal Sistem Komputer, 7(2), 23-30.
Prasetyo, D. A. (2021). Implementasi Algoritma Genetika untuk Sistem Rekomendasi Pemilihan Proyek Mahasiswa. Jurnal Informatika dan Sains, 8(1), 27-36.
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender Systems Handbook (2nd ed.). Springer. https://doi.org/10.1007/978-1-4899-7637-6
Santrock, J. W. (2011). Psikologi Pendidikan (Edisi Kedua). Jakarta: Penerbit Erlangga.
Sari, M., & Gunawan, F. (2022). Optimasi Penentuan Minat Belajar Mahasiswa Menggunakan Algoritma Genetika Berbasis Data Akademik. Jurnal Sistem Cerdas dan Komputasi, 5(3), 101-110.
Sharma, R., & Gupta, S. (2020). Artificial Intelligence and Optimization Algorithms: Applications in Industry. Wiley.
Suyanto. (2019). Algoritma Genetika dan Aplikasinya dalam Optimasi. Yogyakarta: Andi Offset.
Taufik, M., & Syamsudin, R. (2021). Pemrograman Algoritma Genetika dalam Sistem Pembelajaran Berbasis Web. Jurnal Ilmu Komputer dan Teknologi, 6(4), 85-95.
Turban, E., Sharda, R., & Delen, D. (2018). Decision Support and Business Intelligence Systems (10th ed.). Pearson Education.
Wijaya, I. M. (2020). Rancang Bangun Sistem Rekomendasi Topik Skripsi Menggunakan Pendekatan Content-Based Filtering. Jurnal Teknologi Informasi dan Komunikasi, 11(1), 73-81.
Zhang, J., & Li, X. (2021). Machine Learning Algorithms for Data Mining and Business Intelligence. Springer. https://doi.org/10.1007/978-3-030-89262-3
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