A FUZZY LOGIC BASED EVALUATION MODEL FOR THESIS TOPIC FEASIBILITY TO ENHANCE STUDENT RESEARCH RELEVANCE
Abstract
Abstract: The determination of thesis topics is a fundamental stage in academic research, yet the evaluation process remains predominantly manual and subjective. This reliance on individual lecturer perception often leads to inconsistent feasibility assessments and fails to systematically measure the topic's alignment with strategic needs. This research aims to develop a Decision Support System (DSS) model based on fuzzy logic to assess the feasibility of thesis topics objectively and systematically, focusing on enhancing the relevance of student research. The research method employed the Fuzzy Inference System (FIS) with the Sugeno method. This model was designed through literature review and FGD to establish four criteria (Topic Relevance, Difficulty Level, Idea Novelty, Reference Availability) and 81 rule bases. The model validation results against expert judgment using 15 test data showed a high accuracy rate of 91.31%, with a Mean Absolute Percentage Error (MAPE) value of 8.69%. In conclusion, this DSS model is proven to be valid and consistent, and it can be relied upon as an objective tool to improve the quality and relevance of thesis topics.
Keywords: academic evaluation; decision support system; fuzzy logic; fuzzy sugeno; thesis feasibility
Abstrak: Penentuan topik skripsi merupakan tahapan fundamental dalam penelitian akademik, namun proses evaluasinya hingga kini masih cenderung manual dan subjektif. Ketergantungan pada persepsi dosen secara individu sering kali menyebabkan penilaian kelayakan yang tidak konsisten serta kegagalan dalam mengukur keselarasan topik dengan kebutuhan strategis secara sistematis. Penelitian ini bertujuan mengembangkan model Sistem Pendukung Keputusan (SPK) berbasis logika fuzzy untuk menilai kelayakan topik skripsi secara objektif dan sistematis, dengan fokus pada peningkatan relevansi penelitian mahasiswa. Metode penelitian yang digunakan adalah Fuzzy Inference System (FIS) dengan metode Sugeno. Model ini dirancang melalui tinjauan pustaka dan Focus Group Discussion (FGD) untuk menetapkan empat kriteria (Relevansi Topik, Tingkat Kesulitan, Kebaruan Ide, Ketersediaan Referensi) serta 81 basis aturan. Hasil validasi model terhadap penilaian pakar menggunakan 15 data uji menunjukkan tingkat akurasi yang tinggi yaitu 91,31%, dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 8,69%. Kesimpulannya, model SPK ini terbukti valid dan konsisten, serta dapat diandalkan sebagai alat objektif untuk meningkatkan kualitas dan relevansi topik skripsi.
Kata kunci: evaluasi akademik; sistem pendukung keputusan; logika fuzzy; fuzzy sugeno; kelayakan skripsi
References
J. I. Sihotang, M. Ramadhan, and M. Mesran, “Sistem Pendukung Keputusan Penentuan Judul Skripsi Mahasiswa Menggunakan Metode Fuzzy Sugeno,” Jurnal Teknologi Informasi, vol. 7, no. 1, pp. 133–140, 2023.
T. Hidayat, A. Mardali, and D. Novita, “Sistem Pendukung Keputusan Penilaian Proposal Penelitian Dosen Menggunakan Metode Fuzzy Mamdani,” Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, vol. 8, no. 1, pp. 108–116, 2022.
W. Warjiyono, I. Muslihah, and Y. Risa, “Komparasi Metode Fuzzy Tsukamoto Dan Sugeno Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,” Jurnal Teknik Informatika (JUTIF), vol. 4, no. 2, pp. 337–345, 2023.
Rizaldi, D. Anggraeni, A. Z. Syah, A. Kholiq, and R. T. A. Agus, “Decision Support System Using Fuzzy Logic Method of Tahani Model for Student Selection,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Feb. 2021. doi: 10.1088/1742-6596/1783/1/012011.
I. P. A. E. Pratama, I. G. I. Sudipa, and I. M. Ivan, “Decision Support System for Best Student Selection with Fuzzy Sugeno and SAW Methods,” Jurnal Informatika dan Teknologi Informasi, vol. 20, no. 2, pp. 223–234, 2023.
A. Setiawan, M. Yunus, and H. Kurniawan, “Sistem Pendukung Keputusan Penentuan Kelayakan Seminar Proposal Skripsi Mahasiswa Menggunakan Metode Fuzzy Tsukamoto,” Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, vol. 8, no. 2, pp. 177–184, 2022.
M. M. Hasan, A. P. U. Siahaan, and E. M. Zamzami, “Decision Support System for Scholarship Selection Using Fuzzy Logic Tsukamoto Method,” JURIKOM (Jurnal Riset Komputer), vol. 10, no. 1, pp. 143–149, 2023.
R. A. Siregar, A. Wanto, and Z. M. Nasution, “Combination of AHP and Fuzzy Logic Methods in the Selection of Thesis Supervisory Lecturers,” International Journal of Artificial Intelligence & Robotics (IJAIR), vol. 4, no. 1, pp. 16–24, 2022.
A. Rahmadi, A. Ashari, and K. M. Lhaksmana, “Thesis Topic Grouping Based on Abstract Similarity Using K-Means Clustering and TF-IDF,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 143–150, 2023.
F. E. Gunawan, A. Husein, and S. Sumijan, “Sistem Pendukung Keputusan Penilaian Kelayakan Tempat Magang Mahasiswa Menggunakan Metode Fuzzy Tahani,” Jurnal Media Informatika Budidarma, vol. 6, no. 2, pp. 920–928, 2022.
