Recommendations of Thesis Supervisor using the Cosine Similarity Method

Hairani Hairani, Mujahid Mujahid

Abstract


During the thesis writing process, the role of the supervisor is needed so that the completion of the thesis is timely so that the quality of the thesis is maintained. One of the problems in determining the thesis supervisor for undergraduate computer science at Bumigora University is the subjectivity of the head of the study program in determining the thesis supervisor. Not only that, the selection process for supervisors is done manually so that it can take a long time and can slow down student thesis work.  Students' thesis work will be late and not on time if the thesis topic is not in accordance with the competence of the lecturer. This study aims to apply the cosine similarity method to the recommendation of a thesis supervisor for undergraduate computer science at Bumigora University. The stages of this research consist of collecting thesis documents, pre-processing text (Case Folding, Tokenization, Filtering, Stemming), word weighting with TF-IDF, implementation of the cosine similarity method, and accuracy testing. The data used are 113 thesis documents which are divided as training data as many as 90 documents and testing data 23 documents. Based on the testing data on the test, the cosine similarity method can correctly recommend 21 of 23 thesis documents with an accuracy of 91.3%. Thus, the cosine similarity method can be applied to the case of selecting a thesis supervisor for undergraduate computer science at Bumigora University because it has very good accuracy.


Full Text:

PDF

References


A. S. Suweleh, D. Susilowati, and Hairani Hairani, “Aplikasi Penentuan Penerima Beasiswa Menggunakan Algoritma C4.5,” Jurnal BITe, vol. 2, no. 1, pp. 12–21, 2020.

H. Hairani, A. S. Suweleh, and D. Susilowaty, “Penanganan Ketidak Seimbangan Kelas menggunakan Pendekatan Level Data,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 1, pp. 109–116, 2020, doi: 10.30812/matrik.v20i1.846.

D. Kurniawan, A. Anggrawan, and H. Hairani, “Graduation Prediction System on Students using C4.5 Algorithm,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 358–365, 2020, doi: 10.30812/matrik.v19i2.685.

H. Hairani, A. Anggrawan, A. I. Wathan, K. A. Latif, K. Marzuki, and M. Zulfikri, “The Abstract of Thesis Classifier by Using Naive Bayes Method,” in Proceedings - 2021 International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021, 2021, no. August, pp. 312–315, doi: 10.1109/ICSECS52883.2021.00063.

R. Rismanto, A. Rachmad Syulistyo, and B. P. Citra Agusta, “Research Supervisor Recommendation System Based on Topic Conformity,” International Journal of Modern Education and Computer Science, vol. 12, no. 1, pp. 26–34, Feb. 2020, doi: 10.5815/ijmecs.2020.01.04.

R. T. Wahyuni, D. Prastiyanto, and E. Supraptono, “Penerapan Algoritma Cosine Similarity dan Pembobotan TF-IDF pada Sistem Klasifikasi Dokumen Skripsi,” Jurnal Teknik Elektro, vol. 9, no. 1, pp. 18–23, 2017, [Online]. Available: https://journal.unnes.ac.id/nju/index.php/jte/article/view/10955/6659.

L. Yasni, I. M. I. Subroto, and S. F. C. Haviana, “Implementasi Cosine Similarity Matching dalam Penentuan Dosen Pembimbing Tugas Akhir,” Transmisi: Jurnal Ilmiah Teknik Elektro, vol. 20, no. 1, pp. 22–28, Jan. 2018, doi: 10.14710/transmisi.20.1.22-28.

M. Asfi, N. Fitrianingsih, D. Pembimbing, N. Skripsi, and C. Bayes, “Implementasi Algoritma Naive Bayes Classifier sebagai Sistem Rekomendasi Pembimbing Skripsi,” InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan, vol. 5, no. 1, pp. 44–50, 2020, [Online]. Available: https://jurnal.uisu.ac.id/index.php/infotekjar/article/view/2536.

R. R. A. Siregar, F. A. Sinaga, and R. Arianto, “Aplikasi Penentuan Dosen Penguji Skripsi menggunakan Metode TF-IDF dan Vector Space Model,” Computatio : Journal of Computer Science and Information Systems, vol. 1, no. 2, p. 171, 2017, doi: 10.24912/computatio.v1i2.1014.

Y. R. Pradana, A. A. Supianto, and Y. T. Mursityo, “Prediksi Bidang Penelitian dan Rekomendasi Dosen Pembimbing Skripsi Berdasarkan Konten Latar Belakang pada Naskah Proposal Menggunakan Metode Multi-Class Support Vector Machine dan Weighted Product,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 2, p. 403, 2021, doi: 10.25126/jtiik.2021824511.

J. S. Simanungkalit, H. T. Sihotang, S. P. Nusantara, J. Iskandar, M. No, and N. Sumatra, “Decision Support System for Selection of Thesis Advisors Supervisors Thesis Advisors according to the Field of Science Using the AHP Method,” vol. 3, no. 4, 2020.

F. T. Anggraeni, I. Y. Purbasari, and E. F. Wulandari, “Undergraduate Thesis Supervisor Recommendation Based on Text Similarity,” in The 5th International Conference on Information Technology and Bussiness (ICITB 2019), 2019, pp. 248–256.

A. Rahman and A. Doewes, “Online News Classification using Multinomial Naive Bayes,” ITSMART: Jurnal Ilmiah Teknologi dan Informasi, vol. 6, no. 1, pp. 32–38, 2017.

A. Prabhat and V. Khullar, “Sentiment Classification on Big Data using Naïve Bayes and Logistic Regression,” in 2017 International Conference on Computer Communication and Informatics (ICCCI), 2017, pp. 1–5, doi: 10.1109/ICCCI.2017.8117734.

J. Han, M. Kamber, and J. Pei, “Data Mining Concepts and Techniques,” Third Edit., Elsevier, 2012.

E. L. Amalia, A. J. Jumadi, I. A. Mashudi, and D. W. Wibowo, “Analisis Metode Cosine Similarity pada Aplikasi Ujian Online Otomatis (Studi Kasus JTI POLINEMA),” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 2, pp. 343–348, 2021, doi: 10.25126/jtiik.2021824356.

Z. Efendi and M. Mustakim, “Text Mining Classification sebagai Rekomendasi Dosen Pembimbing Tugas Akhir Program Studi Sistem Informasi,” in Seminar Nasional Teknologi Informasi Komunikasi dan Industri, 2017, pp. 235–242, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/3273.

H. Hairani, K. Kurniawan, K. A. Latif, and M. Innuddin, “Metode Dempster-Shafer untuk Diagnosis Dini Jenis Penyakit Gangguan Jiwa Skizofrenia Berbasis Sistem Pakar,” Sistemasi : Jurnal Sistem Informasi, vol. 10, no. 2, pp. 280–289, 2021, doi: 10.32520/stmsi.v10i2.1195.




DOI: https://doi.org/10.32520/stmsi.v11i3.2003

Article Metrics

Abstract view : 409 times
PDF - 146 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.