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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Abdul Halim Hasugian
Universitas Islam Negeri Sumatera Utara, Deli Serdang
Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Peminatan Skripsi Mahasiswa Ilmu Komputer dengan Algoritma K-Nearest Neighbor Helmi Gunawan; Abdul Halim Hasugian
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6457

Abstract

Technological advancements have made it easier for students to access information. However, many Computer Science students still struggle to determine a thesis specialization that aligns with their skills and interests. This study employs the K-Nearest Neighbor (KNN) algorithm to classify students' thesis specializations based on their academic data. The dataset consists of 100 students, with 80 used for training and 20 for testing. The KNN model was applied with parameters k=3k=3k=3 and k=5k=5k=5, generating predictions for thesis specializations such as Artificial Intelligence, Data Mining, and other fields. Model evaluation was conducted using accuracy, precision, recall, and confusion matrix metrics. The results show that the KNN model with k=3k=3k=3 achieved an accuracy of X%, precision of Y%, and recall of Z%. The implementation of KNN in this study demonstrated reasonably accurate results and can serve as a recommendation tool for students in selecting their thesis topics.
Co-Authors Helmi Gunawan