Analisis Cluster dengan K-Means untuk Pengelompokan Kabupaten/Kota di Provinsi Jawa Timur Berdasarkan Pembangunan TIK Tahun 2021-2022
DOI:
https://doi.org/10.35870/jtik.v8i2.1815Keywords:
K-Means, Regency/City Grouping, ICT Development, East Java Province, Cluster AnalysisAbstract
The National Statistics Agency has grouped the IP-TIK by province into 4 categories (high, medium, low and very low). However, the details of IP-TIK in each province have not been explained in detail by city or district. The province chosen was East Java. In this province, the Human Development Index (HDI) is at a high level, but there is inequality in the distribution. This research aims to explore information on whether there are gaps in ICT development using a data mining approach, namely grouping. This is so that the East Java Provincial government focuses more on ICT development at the lower level. The method used is k-Means with Euclidean distance calculations. The highest group results are in cluster 2, which indicates that the IP-ICT level in East Java province is on average moderate. Group inequality occurred in cluster 4 for the household having a computer. Those included in cluster 4 (low category) include Pacitan Regency, Lumajang Regency, Probolinggo Regency, Bangkalan Regency, Sampang Regency and Pamekasan Regency.
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Indeks Pembangunan Teknologi Informasi dan Komunikasi 2021, 2022. Jakarta: Badan Pusat Statistik Indonesia.
Al-Mursyid, A. R. (2020). Pengaruh pembangunan teknologi informasi dan komunikasi terhadap pembangunan kawasan timur Indonesia. Jurnal Ekonomi Pembangunan STIE Muhammadiyah Palopo, 5(2), 53-66. DOI: http://dx.doi.org/10.35906/jep01.v5i2.372.
Saputra, R. A., Nuryadin, D., & Winarti, A. S. (2022). PENGARUH TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) DAN KEBEBASAN EKONOMI TERHADAP PEMBANGUNAN MANUSIA DI KAWASAN NEGARA ASEAN TAHUN 2014-2019. SIBATIK JOURNAL: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, 1(10), 2107-2120.
Jatipaningrum, M. T., Azhari, S. E., & Suryowati, K. (2022). Pengelompokan kabupaten dan kota di provinsi jawa timur berdasarkan tingkat kesejahteraan dengan metode k-means dan density-based spatial clustering of applications with noise. Jurnal Derivat: Jurnal Matematika Dan Pendidikan Matematika, 9(1), 70-81. DOI: https://doi.org/10.31316/j.derivat.v9i1.2832.
Kamila, I., Khairunnisa, U., & Mustakim, M. (2019). Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau. Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, 5(1), 119-125. DOI: http://dx.doi.org/10.24014/rmsi.v5i1.7381.
Muningsih, E., & Kiswati, S. (2018). Sistem aplikasi berbasis optimasi metode elbow untuk penentuan clustering pelanggan. Joutica: Journal of Informatic Unisla, 3(1), 117-124. DOI: https://doi.org/10.30736/jti.v3i1.196.
Hartanti, N. T. (2020). Metode Elbow dan K-Means Guna Mengukur Kesiapan Siswa SMK Dalam Ujian Nasional. J. Nas. Teknol. dan Sist. Inf, 6(2), 82-89.
Dewi, D. A. I. C., & Pramita, D. A. K. (2019). Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali. Matrix: Jurnal Manajemen Teknologi dan Informatika, 9(3), 102-109. DOI: http://dx.doi.org/10.31940/matrix.v9i3.1662.
Anggara, M., Sujaini, H., & Nasution, H. (2016). Pemilihan distance measure Pada K-Means clustering Untuk Pengelompokkan member di Alvaro fitness. JUSTIN (Jurnal Sistem Dan Teknologi Informasi), 4(1), 186-191.
Statistik, I. B. P. (1994). Statistik kesejahteraan rakyat. (No Title).
Nugraha, A., Nurdiawan, O., & Dwilestari, G. (2022). Penerapan Data Mining Metode K-Means Clustering Untuk Analisa Penjualan Pada Toko Yana Sport. JATI (Jurnal Mahasiswa Teknik Informatika), 6(2), 849-855. DOI: https://doi.org/10.36040/jati.v6i2.5755.
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