Analysis of student sentiment towards the quality of final project guidance using the Support Vector Machine Algorithm
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Abstract
This research aims to analyze student sentiment towards thesis guidance services at Nurul Jadid University (UNUJA) using sentiment analysis methods with the Support Vector Machine algorithm. Thesis guidance services play a crucial role in shaping high-quality and competitive human resources within the university environment. However, students' sentiment assessments of these services are often complex and may differ from the perspectives of their advisors. The research approach used is quantitative analysis by collecting student feedback data through questionnaires and interviews. The text data from student responses is then processed to clean and format the data before being implemented with the Support Vector Machine algorithm. This algorithm will classify the sentiment into positive, negative, or neutral groups based on the information contained in the text responses. Based on the results of the conducted study, using the Support Vector Machine (SVM) method for sentiment analysis of thesis guidance quality at Nurul Jadid University, this study achieved an accuracy of 87%, precision of 88%, recall of 87%, and an F1 score of 86%.
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Wijaya, A. ., Anan, M. R. ., Maulidi, M. F. ., Maulana, M. F. ., Purnomo, H. ., & Arifin, Z. . (2024). Analysis of student sentiment towards the quality of final project guidance using the Support Vector Machine Algorithm. Journal of Intelligent Decision Support System (IDSS), 7(4), 328-337. https://doi.org/10.35335/idss.v7i4.280
References
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Sudhir, P., & Suresh, V. D. (2021). Comparative study of various approaches, applications and classifiers for sentiment analysis. Global Transitions Proceedings, 2(2), 205–211. https://doi.org/10.1016/j.gltp.2021.08.004
Sulaeman, A. F., Supianto, A. A., & Bachtiar, F. A. (2019). Analisis Sentimen Opini Mahasiswa Terhadap Saran Evaluasi Kinerja Dosen Menggunakan TF-IDF dan Support Vector Machine. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(6), 5647–5655. http://j-ptiik.ub.ac.id
Sunardi, Fadlil, A., & Suprianto. (2018). Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier Pada Angket Mahasiswa. SAINTEKBU: Jurnal Sains Dan Teknologi, 10(2), 1–9.
Teniwut, W. A., Hasyim, C. L., & Pentury, F. (2022). Towards smart government for sustainable fisheries and marine development: An intelligent web-based support system approach in small islands. Marine Policy, 143, 105158. https://doi.org/10.1016/J.MARPOL.2022.105158
Bafna, P., Pramod, D., & Vaidya, A. (2016). Document Clustering: TF-IDF Approach. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) , 61–66.
Caroline, F., Budi, R. G. S., & Rivan, M. E. Al. (2024). Analisis Sentimen Masyarakat terhadap Kasus Korupsi PT. Timah Menggunakan Metode Support Vector Machine. Jurnal Ilmu Komputer Dan Informatika (JIKI), 4(1), 43–50. https://doi.org/10.54082/jiki.141
Cheng, L., Yang, Y., Zhao, K., & Gao, Z. (2020). Research and improvement of tf-idf algorithm based on information theory. Advances in Intelligent Systems and Computing, 905, 608–616. https://doi.org/10.1007/978-3-030-14680-1_67
Das, B., & Chakraborty, S. (n.d.). An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation.
Fide, S., Suparti, & Sudarno. (2021). Analisis Sentimen Ulasan Aplikasi TikTok di Google Play Menggunakan Metode Support Vector Machine (SVM) dan Asosiasi. Jurnal Gaussian, 10(3), 346–358. https://ejournal3.undip.ac.id/index.php/gaussian/
Hananto, B. K., Pinandito, A., & Kharisma, A. P. (2018). Penerapan Maximum TF-IDF Normalization Terhadap Metode KNN Untuk Klasifikasi Dataset Multiclass Panichella Pada Review Aplikasi Mobile. Jurnal Pengembangan Teknologii Informasi Dan Ilmu Komputer, 2(12), 6812–6823. http://j-ptiik.ub.ac.id
Indriyani, F. A., Fauzi, A., & Faisal, S. (2023). Analisis Sentimen Aplikasi TikTok Menggunakan Algoritma Naive Bayes dan Support Vector Machine. TEKNOSAINS : Jurnal Sains, Teknologi Dan Informatika, 10(2), 176–184. https://doi.org/10.37373/tekno.v10i2.419
Irfani, F. F., Triyanto, M., Hartanto, A. D., & Kusnawi. (2020). Analisis Sentimen Review Aplikasi Ruangguru Menggunakan Algoritma Support Vector Machine. Jurnal Bisnis, Manajemen Dan Informatika, 16(3), 258–266.
Isnan, M., Elwirehardja, G. N., & Pardamean, B. (2023). Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model. Procedia Computer Science, 227, 168–175. https://doi.org/10.1016/j.procs.2023.10.514
Jeven, F. I. M., Feoh, G., & Ardiada, I. M. D. (2023). Sentiment Analysis on Hybrid Learning Using Support Vector Machine Algorithm. Jurnal Kesehatan, Sains, Dan Teknologi (JAKASAKTI), 2(3), 69–74. https://jurnal.undhirabali.ac.id/index.php/jakasakti/index
Kotsiantis, S. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica, 31, 249–268.
Mahendrajaya, R., Buntoro, G. A., & Setyawan, M. B. (2019). Analisis Sentimen Pengguna Gopay Menggunakan Metode Lexicon Based dan Support Vector Machine. KOMPUTEK: Jurnal Teknik Universitas Muhammadiyah Ponnorogo, 3(2), 52–63. http://studentjournal.umpo.ac.id/index.php/komputek
Meiyanti, T. V., Hatta, M., & Sevtiana, A. (2023). Analisis Sentimen Mahasiswa Dengan Dosen Menggunakan Metode Naïve Bayes Classifier Pada Kuesioner Dosen. Jurnal Manajemen Sistem Informasi, 1(2), 55–59. https://doi.org/10.51920/jurminsi.v1i2.143
Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2011). SentiFul: A Lexicon for Sentiment Analysis. IEEE Transactions on Affective Computing, 2(1), 22–36. https://doi.org/10.1109/T-AFFC.2011.1
Novitasari, I., Kurniawan, T. B., Dewi, D. A., & Misinem. (2022). Analisis Sentimen Masyarakat Terhadap Tweet Ruang Guru Menggunakan Algoritma Naive Bayes Classifier (NBC). Jurnal Mantik, 6(3), 2685–4236.
Pradhan, A., Senapati, M. R., & Sahu, P. K. (2022). Improving sentiment analysis with learning concepts from concept, patterns lexicons and negations: Improving sentiment analysis with learning concepts. Ain Shams Engineering Journal, 13(2). https://doi.org/10.1016/j.asej.2021.08.004
Prayogo, D. M., Gunadi, K., & Setyati, E. (2020). Pengenalan Jenis Bunga Anggrek Menggunakan Metode Color Local Binary Pattern dan Support Vector Machine. Jurnal Infra, 08(01), 1–7.
Rizal, F., Julianto, E. E., Firmansyah, E., & Jannah, N. L. (2021). Integrasi Aplikasi Manajemen Praktikum dan Tugas Akhir dengan Kredit Poin Mahasiswa Berbasis Android Untuk Mempermudah Dosen Dalam Memonitoring Kegiatan Mahasiswa. Jurnal Kecerdasan Buatan, Komputasi Dan Teknologi Informasi, 2(1).
Saputra, D., Haryani, H., Surniadari, A., Martias, M., & Akbar, F. (2022). Sistem Informasi Bimbingan Tugas Akhir Mahasiswa Berbasis Website Menggunakan Metode Waterfall. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 403–416. https://doi.org/10.30812/matrik.v21i2.1591
Sudhir, P., & Suresh, V. D. (2021). Comparative study of various approaches, applications and classifiers for sentiment analysis. Global Transitions Proceedings, 2(2), 205–211. https://doi.org/10.1016/j.gltp.2021.08.004
Sulaeman, A. F., Supianto, A. A., & Bachtiar, F. A. (2019). Analisis Sentimen Opini Mahasiswa Terhadap Saran Evaluasi Kinerja Dosen Menggunakan TF-IDF dan Support Vector Machine. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(6), 5647–5655. http://j-ptiik.ub.ac.id
Sunardi, Fadlil, A., & Suprianto. (2018). Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier Pada Angket Mahasiswa. SAINTEKBU: Jurnal Sains Dan Teknologi, 10(2), 1–9.
Teniwut, W. A., Hasyim, C. L., & Pentury, F. (2022). Towards smart government for sustainable fisheries and marine development: An intelligent web-based support system approach in small islands. Marine Policy, 143, 105158. https://doi.org/10.1016/J.MARPOL.2022.105158
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