Optimizing K-Means Algorithm by Using Particle Swarm Optimization in Clustering for Students Learning Process

Rudi Hariyanto, Mohammad Zoqi Sarwani

Abstract


In the implementation of learning, several factors affect the student learning process, including internal factors, external factors, and learning approach factors. For example, the physical and spiritual condition of students. Physiological aspects (body, eyes and ears and talents of students, student interests). External factors, for example, environmental conditions around students, family, teachers, community, friends) Thus, learning achievement is significant because educational institutions' success can be seen from how many students learning achievement. This research's first focus is to do student clustering based on their learning process using 11 parameters. Second, using the PSO algorithm to get maximum clustering results. The research data were obtained from vocational secondary education institutions in the city of Pasuruan. The data is obtained from the results of school reports and questionnaires as much as 100 student data. Data attributes include environmental features, social features, and related school features to group student data for learning data processing. From the classification results using the PSO method, the silhouette value is 0.97140754, very close. These results indicate that the PSO method can improve the K-Means clustering method's performance in the classification process of student learning interest. 

Keywords


Learning process, Optimization Algorithm, PSO, K-Means, Clustering

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References


Langeveld, M.J. 1980. Exceptional Children: an Introductory. Survey of Special Education (Sixth Ed). Macmillan Publishing. New York.

Syah Muhibbin,. 2006. Psikologi Belajar , Jakarta: PT. Raja Grapindo Persada.

Efraim Turban, dkk. 2005. Decision Support Systems and. Intelligent Systems. Edisi 7, Jilid 1, New Jersey: Pearson Education.

Y. A. Auliya, "Improve Hybrid Particle Swarm Optimization and K-Means by Random Injection for Land Clustering of Potato Plants," Proc. - 2019 Int. Conf. Comput. Sci. Inf. Technol. Electr. Eng. ICOMITEE 2019, vol. 4, no. 1, pp. 192–198, 2019, DOI: 10.1109/ICOMITEE.2019.8921207.

M. Sarwani and D. Sani, “Implementasi Metode K-Means Sebagai Pengelompokan Siswa Berdasarkan Proses Belajar Siswa,” pp. 1131–1135, 2018.

J. Aranda and W. A. G. Natasya, “Penerapan Metode K-Means Cluster Analysis Pada Sistem Pendukung Keputusan Pemilihan Konsentrasi Untuk Mahasiswa International Class Stmik Amikom Yogyakarta,” Semin. Nas. Teknol. Dan Multimed. 2016, vol. 4, no. 1, pp. 4-2–1, 2016, [Online]. Available: https://ojs.amikom.ac.id/index.php/semnasteknomedia/article/view/1293.

F. Y. Bisilisin, Y. Herdiyeni, and B. I. B. P. Silalahi, “Optimasi K-Means Clustering Menggunakan Particle Swarm Optimization pada Sistem Identifikasi Tumbuhan Obat Berbasis Citra K-Means Clustering Optimization Using Particle Swarm Optimization on Image Based Medicinal Plant Identification System,” Ilmu Komput. Agri-Informatika, vol. 3, no. 2002, pp. 38–47, 2014.

Sujoto, T.S.Si., M.M.Kom. 2011. “Kecerdasan Buatan”. Penerbit ANDI yogjakarta.

A. Saidul and J. L. Buliali, "Implementasi Particle Swarm Optimization pada K-Means Untuk Clustering Data Automatic Dependent Surveillance-Broadcast," Eksplora Inform., vol. 8, no. 1, p. 30, 2018, DOI: 10.30864/eksplora.v8i1.150.

Rahmawati, dkk. 2019. Optimasi K-Means untuk Pengelompokan Data Kinerja Akademik Dosen menggunakan Particle Swarm Optimization, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN: 2548-964X Vol. 3, No. 4, April 2019, hlm. 4102-4110 http://j-ptiik.ub.ac.id

Achyani, 2018. Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung. JURNAL INFORMATIKA, Vol.5 No.1 2355-6579, E-ISSN: 2528-2247

S. Kusumadewi, Membangun Jaringan Syaraf Tiruan Menggunakan MATLAB & EXCEL LINK. Yogyakarta: Graha Ilmu, 2004ISSN:

Kusumadewi S, Hartati S, Harjoko A, Wardoyo R. 2006. Fuzzy Multi-Attribute Decision Making (FUZZY MADM). Graha Ilmu, Yogyakarta.

Daihani, DU., 2001, Komputerisasi Pengambilan Keputusan, PT Elex Media Komputindo Gramedia, Jakarta.

Kusrini, 2007, Konsep dan Aplikasi Sistem Pendukung Keputusan, Penerbit Andi, Yogyakarta.




DOI: http://dx.doi.org/10.25139/inform.v6i1.3459

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