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Recommendation System for Thesis Topics Using Content-based Filtering
https://doi.org/10.22146/ijccs.62716 Hans Satria Kusuma(1*), Aina Musdholifah(2)
(1) Bachelor Program of Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
When pursuing their bachelor degree, every students are required to pursue a thesis in order to graduate from the major that they take. However, during the process, students got several difficulty regarding chosing their thesis topics. Therefore, a recommendation system is needed to classify thesis topics based on the students’ interest and abilities. This study developed a recommendation system for thesis topics using content-based filtering where the students will be asked to choose the course that they interested in along with their grades. After getting all the required data, the recommendation system will process the data and then it’ll show the title and the abstract of publication that fits the criteria.
In this research, there are 2 datasets that is used, there are lecturer publication within 3 years and syllabus data of Computer Science UGM course. After running this research, it was found that the recommendation system has an average 7.46 seconds running time. It was also found that the recommendation system got an average 83% of the recommendation system objectives. The recommendation system objectives consist of relevance, novelty, serendipity, and increasing recommendation diversity.
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[1] E. Setiawan, “Kamus Besar Bahasa Indonesia (Online),” 2020. [Online]. Available: http://kbbi.web.id/skripsi . [Accessed: 20-Apr-2020].
[2] T. Suwartiningsih, B. S. Rintyarna, and D. Arifianto, “Rekomendasi Topik Tugas Akhir Mahasiswa Teknik Informatika Universitas Muhammadiyah Jember Menggunakan Metode Naïve Bayesian Classifier,” Universitas Muhammadiyah Jember, 2016. Available: http://digilib.unmuhjember.ac.id/download.php?id=1915. [Accessed: 12-Mar-2020]
[3] E. Daniati, “Decision support system to deciding thesis topic,” Proc. - 2017 Int. Semin. Appl. Technol. Inf. Commun. Empower. Technol. a Better Hum. Life, iSemantic 2017, vol. 2018-Janua, pp. 52–57, 2017.
[4] S. Kannan et al., “Preprocessing Techniques for Text Mining,” Int. J. Comput. Sci. Commun. Networks, vol. 5, no. 1, pp. 7–16, 2015.
[5] A. Setiawan, E. Kurniawan, and W. Handiwidjojo, “Implementasi Stop Word Removal Untuk Pembangunan Aplikasi Alkitab Berbasis Windows 8,” J. EKSIS, vol. 6, no. 2, pp. 1–11, 2013.
[6] J. Kanerva, F. Ginter, and T. Salakoski, “Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks,” Nat. Lang. Eng., 2020.
[7] D. Worth, “Introduction to Modern Information Retrieval, 3rd Edition,” Aust. Acad. Res. Libr., vol. 41, no. 4, pp. 305–306, 2010.
[8] Resnick, P. and Varian, H. R., 1997. Recommender systems. Commun. ACM, 40(3), pp. 56-58 [online]. Available : https://dl.acm.org/doi/10.1145/245108.245121.
[9] J. Chen, C. Chen, and Y. Liang, “Optimized TF-IDF Algorithm with the Adaptive Weight of Position of Word,” vol. 133, pp. 114–117, 2016.
[10] M. D’Agostino and V. Dardanoni, “What’s so special about Euclidean distance? A characterization with applications to mobility and spatial voting,” Soc. Choice Welfare, vol. 33, no. 2, pp. 211–233, 2009.
DOI: https://doi.org/10.22146/ijccs.62716
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