Aplikasi Rekomendasi Dosen Pembimbing Skripsi Berbasis Tf-Idf Dan Cosine Similarity
Abstract
The selection of undergraduate thesis supervisors is a critical academic process; however, it is often conducted manually and lacks systematic consideration of topic–expertise alignment. This study proposes a thesis supervisor recommendation system based on text modeling techniques, employing Term Frequency–Inverse Document Frequency (TF-IDF) for term weighting and cosine similarity to measure relevance between students’ thesis titles and lecturers’ scientific publications. The system generates the top three recommended supervisors and is evaluated by directly comparing the results with faculty-assigned supervisors, achieving a matching rate of 12.22 percent. Although the matching rate is relatively low, the recommendations demonstrate strong academic relevance. Unlike previous studies, this work evaluates recommendation outcomes against institutional assignments, positioning the system as a decision support tool rather than a replacement mechanism. The findings indicate that the proposed approach has the potential to enhance efficiency, transparency, and objectivity in the thesis supervisor selection process.
Keywords: Recommendation system; Thesis supervisor; Undergraduate thesis; TF-IDF; Cosine similarity
Abstrak
Pemilihan dosen pembimbing skripsi merupakan proses akademik yang krusial, namun pada banyak perguruan tinggi masih dilakukan secara manual dan belum secara sistematis mempertimbangkan kesesuaian topik dengan keahlian dosen. Penelitian ini mengusulkan sistem rekomendasi dosen pembimbing berbasis pemodelan teks dengan menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) sebagai metode pembobotan dan cosine similarity untuk mengukur relevansi antara judul skripsi mahasiswa dan publikasi ilmiah dosen. Sistem menghasilkan tiga rekomendasi dosen teratas dan dievaluasi dengan membandingkannya secara langsung terhadap dosen pembimbing yang ditetapkan oleh fakultas, dengan tingkat kecocokan sebesar 12,22 persen. Meskipun nilai kecocokan relatif rendah, rekomendasi yang dihasilkan menunjukkan relevansi akademik yang baik. Berbeda dengan penelitian sebelumnya, penelitian ini memosisikan sistem sebagai alat pendukung pengambilan keputusan, bukan sebagai pengganti penetapan institusional. Hasil penelitian menunjukkan potensi sistem dalam meningkatkan efisiensi, transparansi, dan objektivitas pemilihan dosen pembimbing skripsi.
Keywords
References
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