Recommendation System to Propose Final Project Supervisors using Cosine Similarity Matrix

Zulfa Fajrul Falah(1*), Fajar Suryawan(2),

(1) Universitas Muhammadiyah Surakarta
(2) Universitas Muhammadiyah Surakarta, Surakarta
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v8i2.16235

Abstract

The selection of a supervisor is an important thing and one of the determinants of whether or not a student's final project research is successful. At the location of this research, students select a supervisor by considering his academic records, and recommendations from classmates or seniors. Words of mouth dominate their motivation, and many students do not have a basis for their choice. Selection of the fit supervisor has a significant impact on students' progression. Students will be more enthusiastic about doing the final project and may get facilitation in their research because the topics of students' projects match supervisors' interests and ongoing works. This study aims to make a recommendation system that suggests a supervisor for a student. The student fills in the title, abstract, and keywords of his proposal. The application proposes a prospective supervisor by calculating the similarity of the data with titles, abstracts, and keywords of published articles found in Google Scholar. This recommendation system uses the content-based filtering method to produce a list of recommendations. The cosine similarity algorithm calculates how similar the topic proposed by students is to the lecturer's interests. In building a website-based recommendation system, the author uses two Django web frameworks as the backend and ReactJs as the frontend. The system is successful in suggesting final project supervisors that have matched interest and expertise with students' proposals.

Keywords

cosine similarity; recommendation system; web scraping; content-based filtering

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References

P. Nagarnaik and A. Thomas, “Survey on recommendation system methods,” in 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, Feb. 2015, pp. 1603–1608. doi: 10.1109/ECS.2015.7124857.

L. Sharma and A. Gera, “A Survey of Recommendation System: Research Challenges,” International Journal of Engineering Trends and Technology., p. 5, 2013.

J. Son and S. B. Kim, “Content-based filtering for recommendation systems using multiattribute networks,” Expert Syst. Appl., vol. 89, pp. 404–412, Dec. 2017, doi: 10.1016/j.eswa.2017.08.008.

S. Debnath, N. Ganguly, and P. Mitra, “Feature weighting in content based recommendation system using social network analysis,” in Proceeding of the 17th international conference on World Wide Web - WWW ’08, Beijing, China, 2008, p. 1041.

D. Pyle, “Data Preparation for Data Mining,” p. 466, 1999.

Z. Jianqiang, G. Xiaolin, and Z. Xuejun, “Deep Convolution Neural Networks for Twitter Sentiment Analysis,” IEEE Access, vol. 6, pp. 23253–23260, 2018, doi: 10.1109/ACCESS.2017.2776930.

D. Gunawan, “Evaluasi Performa Pemecahan Database dengan Metode Klasifikasi Pada Data Preprocessing Data mining,” Khazanah Informatika, Jurnal Ilmu Komputer dan Informatika, Vol. 2, no. 1 2016.

R. K. Roul, J. K. Sahoo, and K. Arora, “Modified TF-IDF Term Weighting Strategies for Text Categorization,” in 2017 14th IEEE India Council International Conference (INDICON), Roorkee, Dec. 2017.

S. Robertson, “Understanding Inverse Document Frequency: On Theoretical Arguments for IDF,” Journal of Documentatio, no. 60, pp. 503–520, Oct. 2004

Yun-tao, Z., Ling, G. & Yong-cheng, W. “An improved TF-IDF approach for text classification.” Journal of Zheijang University - Science A 6, 49–55 (2005).

M. Nilashi, K. Bagherifard, O. Ibrahim, H. Alizadeh, L. A. Nojeem, and N. Roozegar, “Collaborative Filtering Recommender Systems,” Research Journal of Applied Science, Engineering, and Technology, vol. 5, no. 16, pp. 4168–4182, 2013

R. Samuel, R. Natan, and U. Syafiqoh, “Penerapan Cosine Similarity dan K-Nearest Neighbor (K-NN) pada Klasifikasi dan Pencarian Buku,” Journal of Big Data Analytic and Artificial Intelligence vol. 1, no. 1, p. 6, 2018.

F. Rahutomo, T. Kitasuka, and M. Aritsugi, “Semantic Cosine Similarity”. The 7th International Student Conference on Advanced Science and Technology (ICAST), 2012.

I. Sommerville, Software engineering, 9th ed. Boston: Pearson, 2011.

Mohd. Ehmer Khan, “Different Approaches To Black box Testing Technique For Finding Errors,” International Journal of Software Engineering and Applications, vol. 2, no. 4, pp. 31–40, 2011

F. Mubarak, “Perbandingan Cosine Similarity dan Euclidean Distance pada Sistem Rekomendasi Film Menggunakan Metode Item Based Multi Criteria Collaborative Filtering,” 2019, Accessed: Nov. 03, 2021. [Online]. Available at: https://digilib.uns.ac.id/dokumen/76073/Perbandingan-Cosine-Similarity-dan-Euclidean-Distance-pada-Sistem-Rekomendasi-Film-Menggunakan-Metode-Item-Based-Multi-Criteria-Collaborative-Filtering

A. Abdullah and M. W. Pangestika, “Perancangan Sistem Pendukung Keputusan dalam Pemilihan Dosen Pembimbing Skripsi Berdasarkan Minat Mahasiswa dengan Metode AHP (Analytical Hierarchy Process) di Universitas Muhammadiyah Pontianak,” JEPIN J. Edukasi Dan Penelit. Inform., vol. 4, no. 2, Art. no. 2, Dec. 2018, doi: 10.26418/jp.v4i2.27651.

Amalina, N., & Sutikno, S. (2017). Sistem Rekomendasi Dosen Pembimbing Tugas Akhir Berbasis Text Mining Menggunakan Vector Space Model [Skripsi, Universitas Diponegoro].

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