Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means
DOI:
https://doi.org/10.30812/matrik.v23i1.3341Keywords:
Cluster, Fuzzy C-means, Silhouette Coefficient, Pearson Correlation, Principal Componen AnalysisAbstract
The student’s Final Project is critical as a requirement to graduate from the University. In the PSTI at Mataram University, each student is required to choose a specialization lab to focus on the final project topic that they will work on. From the questionnaire, 57.7% of students answered that it is difficult to select a lab, and others answered that they prefer to determine the labs based on the grades of the courses that represent each lab. This research aimed to group and analyze students in the final project specialization lab by using the main method, namely Fuzzy C-Means (FCM). The methods used were FCM for clustering, Silhouette Coefficient for analysis of cluster quality results, Pearson Correlation, and Principal Component Analysis for the feature selection processing. The results of this study showed that the FCM method followed by a method for feature selection has better results than previous studies that used the K-Means method without feature selection; with this research result using 131 data, the cluster validation result is 0.501, after feature selection using Pearson correlation is 0.534. Thus, Fuzzy C-Means followed by the right feature selection method can group students into specialization laboratories with good results and can be further developed.
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English Language Teaching for EFL Learners, vol. 3, no. 2, pp. 41–53, sep 2021.
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Confidence Based on Relevant Research,†Frontiers in Psychology, vol. 13, no. June, pp. 1–10, jun 2022.
[4] D. Nirmala and E. P. Hendro, “Problema Dalam Memilih Judul Penelitian Kebahasaan Bagi Pemula,†Jurnal Harmoni, vol. 5,
no. 1, pp. 15–19, 2020.
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Sistem Embedded and Logic, vol. 9, no. 1, pp. 1–14, 2021.
[6] N. A. Ningrum and M. A. Syaputra, “Penerapan Metode Fuzzy C-Means untuk Penentuan Kompetensi Mahasiswa di STMIK
Dharmawacana Metro,†Journal Computer Science and Informatic Systems:J-Cosys, vol. 2, no. 1, pp. 1–7, 2022.
[7] P. Subekti, T. D. Andini, and M. Islamiyah, “Sistem Penentuan Konsentrasi Jurusan Bagi Mahasiswa Informatika Menggunakan
Metode K-Means Di Institut Asia Malang,†Jurnal Manajemen Informatika (JAMIKA), vol. 12, no. 1, pp. 25–39, 2022.
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Jurusan Pada Sekolah Menengah Tingkat Atas,†POSITIF : Jurnal Sistem dan Teknologi Informasi, vol. 7, no. 2, pp. 108–119,
2021.
[9] H. Haviluddin, S. J. Patandianan, G. M. Putra, N. Puspitasari, and H. S. Pakpahan, “Implementasi Metode K-Means Untuk
Pengelompokkan Rekomendasi Tugas Akhir,†Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, vol. 16, no. 1, pp.
13–18, mar 2021.
[10] A. Abdullah and S. Sucipto, “Sistem Pendukung Keputusan Untuk Rekomendasi Topik Skripsi Dengan Metode Fuzzy AHP,â€
Jurnal Transformatika, vol. 18, no. 2, pp. 231–239, jan 2021.
[11] Antoni Erga and Y. Nataliani, “Seleksi Fitur Pada Pengelompokkan Posisi Pemain Basket Menggunakan Fuzzy C-Means,â€
Journal Of Information Technology and Computer Science, vol. 3, no. 1, pp. 77–84, 2021.
[12] J. Mantik, D. Hartama, and M. Anjelita, “Analysis of Silhouette Coefficient Evaluation with Euclidean Distance in the Clustering
Method (Case Study: Number of Public Schools in Indonesia),†Jurnal Mantik, vol. 6, no. 3, pp. 3667–3677, 2022.
[13] Winarno, “Comparison Of Clustering Levels Of The Learning Burnout Of Students Using The Fuzzy C-Means And K-Means
Methods,†Jurnal Teknologi Informasi dan Pendidikan, vol. 16, no. 1, pp. 38–53, 2023.
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Algorithm,†Journal of Physics: Conference Series, vol. 1757, no. 1, pp. 1–10, jan 2021.
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Published
2023-11-20
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Section
Articles
How to Cite
[1]
I. Rahmatullah, G. S. Nugraha, and A. Aranta, “Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means”, MATRIK, vol. 23, no. 1, pp. 143–154, Nov. 2023, doi: 10.30812/matrik.v23i1.3341.
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