Application of Data Mining Using the K-Means Clustering Algorithm for Opening Industrial Classes in Vocational High Schools

Aan Rosydiana; Dian Sediana; Christina Juliane

doi:10.24014/ijaidm.v5i2.19172

Application of Data Mining Using the K-Means Clustering Algorithm for Opening Industrial Classes in Vocational High Schools

Aan Rosydiana, Dian Sediana, Christina Juliane

Abstract

Vocational High School has a goal to enter the world of work, meaning that it must have a skill program to be relevant to the industrial world. However, adapting to the industrial world is difficult, one of the things that is happening between industries is increasing. Various efforts continue to be made, among others, by establishing an industrial class, the formation of an industrial class is expected to produce students who have competencies in accordance with the standards required by the collaborating industries. The formation of an industrial class can be done by applying data mining methods, in order to form the right industrial class and in accordance with predetermined criteria. This study aims to classify new student registration data at State Vocational Schools at the Regional Education Office XIII Branch of West Java Province in 2022 and the results of the grouping are used to form industrial classes. The clustering process is carried out using the K-Means algorithm and cluster analysis is carried out with the help of RapidMiner software. The results showed that the data clustering was formed into 4 clusters. The cluster that has the highest number is cluster 1 and the cluster that has the lowest number is cluster 0. There are variables used for data grouping, including school variables and expertise programs, from these variables it is obtained that the schools selected by students are based on the highest order and have expertise programs contained in their clusters, which need to be considered when opening industrial classes.

Keywords

Data Mining, Clustering, Industrial Class, K-Means Algorithm

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References

N. Wibowo, “Upaya memperkecil kesenjangan kompetensi lulusan sekolah menengah kejuruan dengan tuntutan dunia industri,” J. Pendidik. Teknol. dan Kejuru., vol. 23, no. 1, pp. 45–59, 2016.

H. Achsani, D. Kustono, and S. Suhartadi, “Model Kelas Industri pada Mitsubishi School Program di Sekolah Menengah Kejuruan,” J. Pendidik. Teor. Penelitian, dan Pengemb., vol. 5, no. 8, pp. 1078–1085, 2020.

S. S. Danutirta, “Pengelolaan Kelas Industri di SMK N 2 Klaten,” Hanata Widya, vol. 7, no. 6, pp. 30–44, 2018.

A. Saifudin, “Metode data mining untuk seleksi calon mahasiswa pada penerimaan mahasiswa baru di Universitas Pamulang,” J. Teknol., vol. 10, no. 1, pp. 25–36, 2018.

F. Yunita, “Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru,” Sist. J. Sist. Inf., vol. 7, no. 3, pp. 238–249, 2018.

T. Purwanti, W. Ramdhan, and S. Santoso, “Penerapan Metode Klasterisasi K-Means untuk Strategi Promosi Pada SMK Tamansiswa Sukadamai,” JUTSI (Jurnal Teknol. dan Sist. Informasi), vol. 1, no. 2, pp. 141–146, 2021.

M. Triandini, S. Defit, and G. W. Nurcahyo, “Data Mining dalam Mengukur Tingkat Keaktifan Siswa dalam Mengikuti Proses Belajar pada SMP IT Andalas Cendekia dengan Menggunakan Metode K-Means Clustering,” J. Inf. Dan Teknol., pp. 167–172, 2021.

J. Hutagalung, “Pemetaan Siswa Kelas Unggulan Menggunakan Algoritma K-Means Clustering,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 1, pp. 606–620, 2022.

A. Aditya, I. Jovian, and B. N. Sari, “Implementasi K-Means Clustering Ujian Nasional Sekolah Menengah Pertama di Indonesia Tahun 2018/2019,” J. Media Inform. Budidarma, vol. 4, no. 1, pp. 51–58, 2020.

M. L. Sibuea and A. Safta, “Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustering,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 4, no. 1, pp. 85–92, 2017.

S. N. B. Sembiring, H. Winata, and S. Kusnasari, “Pengelompokan Prestasi Siswa Menggunakan Algoritma K-Means,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 1, pp. 31–40, 2022.

Z. Nabila, A. R. Isnain, P. Permata, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. Dan Sist. Inf., vol. 2, no. 2, pp. 100–108, 2021.

R. A. Indraputra and R. Fitriana, “K-Means Clustering Data COVID-19,” J. Tek. Ind., vol. 10, no. 3, pp. 275–282, 2020.

R. P. Primanda, A. Alwi, and D. Mustikasari, “Data Mining Seleksi Siswa Berprestasi Untuk Menentukan Kelas Unggulan Menggunakan Metode K-Means Clustering (Studi Kasus di MTS Darul Fikri),” KOMPUTEK, vol. 5, no. 1, pp. 88–100, 2021.

T. Syahputra, J. Halim, and E. P. Sintho, “Penerapan Data Mining Dalam Menentukan Pilihan Jurusan Bidang Studi Sma Menggunakan Metode Clustering Dengan Teknik Single Linkage,” JURTEKSI (Jurnal Teknol. Dan Sist. Informasi), vol. 4, no. 2, pp. 205–208, 2018.

A. Ali, “Klasterisasi Data Rekam Medis Pasien Menggunakan Metode K-Means Clustering di Rumah Sakit Anwar Medika Balong Bendo Sidoarjo,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 1, pp. 186–195, 2019.

R. Nofitri and N. Irawati, “Analisis Data Hasil Keuntungan Menggunakan Software Rapidminer,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 5, no. 2, pp. 199–204, 2019.

W. Widiyanti, S. Solichin, and Y. Yoto, “Kerjasama Sekolah Menengah Kejuruan Dan Industri (Studi Kasus Pendidikan Kelas Industri Smk Nasional Malang Dengan Astra Honda Motor),” Teknol. dan Kejuru. J. Teknol. Kejuru. dan Pengajarannya, vol. 40, no. 2, pp. 181–192, 2017.

DOI: http://dx.doi.org/10.24014/ijaidm.v5i2.19172

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