Article Sidebar

Submitted
June 24, 2025
Published
September 30, 2025
Download
pdf
Statistic
Read Counter : 4 Download : 2

Main Article Content

Abstract

The process of selecting a thesis advisor that aligns the thesis topic with the advisor's area of expertise will greatly assist in the thesis preparation process for students. However, in reality, the process of determining the thesis advisor is often not based on the advisor's area of expertise. The process of determining the thesis advisor is often done manually and inefficiently, which can lead to a mismatch between the advisor's area of expertise and the student's thesis topic. From this problem, this research developed a recommendation model for selecting thesis supervisors using the Cosine Similarity method, utilizing thesis title data and the list of lecturers' areas of expertise. The process begins with preprocessing the text data of thesis titles, then calculating word weights using TF-IDF and measuring similarity to finally match the students' thesis titles with the lecturers' areas of expertise, which are calculated using the Cosine Similarity algorithm. From the results of testing the process of matching research topics from student thesis titles with the expertise and research of lecturers, it was produced accurately, as indicated by a precision value of 81% and a recall of 90%. This result also shows that the relevance level of the recommendations is very high. The F1 score obtained, which is 85%, indicates that the modeling has a good balance between precision and sensitivity (recall).

Keywords

Modeling Thesis Title Supervisor Cosine Similarity Expertise

Article Details

References

  1. Dharmawan, H. et al. (2023) Hafizh Dharmawan, Sistem Rekomendasi Buku Dengan Metode K-Nearest Neighbor Pada Gramedia SISTEM REKOMENDASI BUKU DENGAN METODE K-NEAREST NEIGHBOR (K-NN) PADA GRAMEDIA, Sistem Informasi.
  2. Fisabilillah, D., Auliasari, K. and Pranoto, Y.A. (2025) ‘Sistem Rekomendasi Konversi Program MSIB Dengan Mata Kuliah Prodi Informatika ITN Malang Menggunakan Cosine Similarity’, SKANIKA: Sistem Komputer dan Teknik Informatika, 8(1), pp. 83–94.
  3. Januzaj, Y. and Luma, A. (2022) ‘Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands Based on Maximum Number of Common Words’, International Journal of Emerging Technologies in Learning, 17(12), pp. 258–268. Available at: https://doi.org/10.3991/ijet.v17i12.30375.
  4. Nasrullah, A.H. (2024) ‘Integrasi Tf-Idf Dan Algoritma Cosine Similarity Untuk Deteksi Tingkat Kemiripan Judul Penelitian (Studi Kasus Mahasiswa Fakultas Ilmu Komputer UNISAN Gorontalo)’, INTEC Journal: Information Technology Education Journal, 3(3). Available at: https://scholar.google.com/.
  5. Rinjeni, T.P., Indriawan, A. and Rakhmawati, N.A. (2024) ‘Matching Scientific Article Titles using Cosine Similarity and Jaccard Similarity Algorithm’, in Procedia Computer Science. Elsevier B.V., pp. 553–560. Available at: https://doi.org/10.1016/j.procs.2024.03.039.
  6. Salam, A. and Putraga Albahri, F. (2022) ‘Sistem Rekomendasi Tugas Akhir Mahasiswa pada AMIK Indonesia untuk Mendukung Merdeka Belajar-Kampus Merdeka Menggunakan Metode Collaborative Filtering (CF)’, Jurnal Teknologi Informasi dan Komunikasi), 6(2), p. 2022. Available at: https://doi.org/10.35870/jti.
  7. Wahyuni, S. and Abdullah, A. (2025) ‘Penggunaan Information Retrieval untuk Mendeteksi Kesamaan Judul Skripsi dengan Modified Cosine Similarity’, 8(2), pp. 117–126. Available at: https://doi.org/10.31764/justek.vXiY.ZZZ.
  8. Dharmawan, H. et al. (2023) Hafizh Dharmawan, Sistem Rekomendasi Buku Dengan Metode K-Nearest Neighbor Pada Gramedia SISTEM REKOMENDASI BUKU DENGAN METODE K-NEAREST NEIGHBOR (K-NN) PADA GRAMEDIA, Sistem Informasi.
  9. Fisabilillah, D., Auliasari, K. and Pranoto, Y.A. (2025) ‘Sistem Rekomendasi Konversi Program MSIB Dengan Mata Kuliah Prodi Informatika ITN Malang Menggunakan Cosine Similarity’, SKANIKA: Sistem Komputer dan Teknik Informatika, 8(1), pp. 83–94.
  10. Januzaj, Y. and Luma, A. (2022) ‘Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands Based on Maximum Number of Common Words’, International Journal of Emerging Technologies in Learning, 17(12), pp. 258–268. Available at: https://doi.org/10.3991/ijet.v17i12.30375.
  11. Nasrullah, A.H. (2024) ‘Integrasi Tf-Idf Dan Algoritma Cosine Similarity Untuk Deteksi Tingkat Kemiripan Judul Penelitian (Studi Kasus Mahasiswa Fakultas Ilmu Komputer UNISAN Gorontalo)’, INTEC Journal: Information Technology Education Journal, 3(3). Available at: https://scholar.google.com/.
  12. Rinjeni, T.P., Indriawan, A. and Rakhmawati, N.A. (2024) ‘Matching Scientific Article Titles using Cosine Similarity and Jaccard Similarity Algorithm’, in Procedia Computer Science. Elsevier B.V., pp. 553–560. Available at: https://doi.org/10.1016/j.procs.2024.03.039.
  13. Salam, A. and Putraga Albahri, F. (2022) ‘Sistem Rekomendasi Tugas Akhir Mahasiswa pada AMIK Indonesia untuk Mendukung Merdeka Belajar-Kampus Merdeka Menggunakan Metode Collaborative Filtering (CF)’, Jurnal Teknologi Informasi dan Komunikasi), 6(2), p. 2022. Available at: https://doi.org/10.35870/jti.
  14. Wahyuni, S. and Abdullah, A. (2025) ‘Penggunaan Information Retrieval untuk Mendeteksi Kesamaan Judul Skripsi dengan Modified Cosine Similarity’, 8(2), pp. 117–126. Available at: https://doi.org/10.31764/justek.vXiY.ZZZ.