Penerapan Metode K-Means Clustering dalam Pengelompokkan Tingkat Pengangguran di Provinsi Lampung

Imelia Puspita Sari; Muhammad Rijal Firmansyah; Arzella Meilantika; Ade Herlina; Nuari Anisa Sivi

doi:10.55606/juisik.v6i2.2414

Penerapan Metode K-Means Clustering dalam Pengelompokkan Tingkat Pengangguran di Provinsi Lampung

Authors

Imelia Puspita Sari Universitas Nahdlatul Ulama Lampung
Muhammad Rijal Firmansyah Universitas Nahdlatul Ulama Lampung
Arzella Meilantika Universitas Nahdlatul Ulama Lampung
Ade Herlina Universitas Nahdlatul Ulama Lampung
Nuari Anisa Sivi Universitas Nahdlatul Ulama Lampung

DOI:

https://doi.org/10.55606/juisik.v6i2.2414

Keywords:

Data Mining, K-Means Clustering, Lampung, Open Unemployment Rate, Unemployment

Abstract

Unemployment is one of the main problems in economic development in Indonesia. One of the indicators used to measure the economic condition of a region is the Open Unemployment Rate. The purpose of this study is to provide information regarding a more detailed grouping of the number of unemployment rates in each district/city in Lampung Province, through the application of the K-Means clustering algorithm method. The data used in the study were taken from the official website of the Central Statistics Agency (BPS) of Lampung Province in 2025, which includes data on the Open Unemployment Rate (TPT) and the Poverty Rate. The application of K-Means clustering was used to break down the data into groups. The research method includes four steps: data collection, data preparation, modeling, and evaluation (using the Davies Bouldin Index theory), using the RapidMiner application as a support to facilitate data mining processing. The results of this study are to provide information about the unemployment rate groups in Lampung Province into 3 groups, namely low unemployment rate, medium unemployment rate, and high unemployment rate, which will divide all districts in Lampung Province.

References

R. D. Pratiwi, M. N. Nurhadiyah, and S. Ningsih, “ANALISIS TREN TINGKAT PENGANGGURAN TERBUKA DAN HUBUNGANNYA DENGAN KONDISI EKONOMI PROVINSI LAMPUNG PERIODE 2014-2024,” vol. 3, pp. 413–424, 2026.

P. Dan and P. Ekonomi, “INVESTASI MODAL MANUSIA BIDANG PENDIDIKAN : DAMPAK,” vol. 6, no. 1, pp. 21–37, 2020.

B. P. Statistik, “No Title,” Badan Pusat Statistik Provinsi Lampung. [Online]. Available: https://lampung.bps.go.id/id

W. Syaharani et al., “Penerapan Algoritma k-Means pada Clustering Data Penduduk Miskin untuk Penghapusan Kemiskinan Ekstrem Application of k-Means Algorithm on Clustering Poor Population Data,” vol. 13, pp. 1732–1747, 2024.

N. Nurjanah, N. Suarna, W. Prihartono, T. Informatika, R. P. Lunak, and G. Tasikmalaya, “IMPLEMENTASI K-MEANS CLUSTERING UNTUK MENGELOMPOKAN,” vol. 8, no. 2, pp. 2462–2468, 2024.

A. Alif, R. Mulyana, A. R. Juwita, A. M. Siregar, and A. Fauzi, “Penerapan Algoritma K-means Clustering dan Hierarchical Clustering dalam Mengelompokkan Data Pengangguran di Karawang,” no. 2021, 2024, doi: 10.33364/algoritma/v.21-2.2155.

P. Studi and S. Informasi, “Penerapan Metode K-Means Pada Pengelompokkan Pengangguran Di Indonesia,” vol. 6, pp. 61–74.

N. P. Dharshinni, G. Singh, J. April, T. Naibaho, R. D. L. Tobing, and A. Simamora, “Penerapan Algoritma K-Means Pada Data Pengangguran Di Jawa Barat,” vol. 3, no. 1, pp. 22–33, 2023.

N. Sari, H. H. Handayani, and A. M. Siregar, “Implementasi Clustering Data Kasus Covid 19 Di Indonesia Menggunakan Algoritma K-Means,” vol. 11, no. 1, pp. 7–12, 2023.

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,” 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:237684110

I. Sumiati, A. Rasyid, F. Adinata, M. H. A.- Musa, R. Herdiana, and C. Lukman, “Penerapan Metode Two-Step Cluster Untuk Pengelompokan Desa Berdasarkan Kepadatan Penduduk,” vol. 05, no. 02, pp. 36–41, 2021.

N. I. Pastia and F. N. Dikananda, “Pengelompokan Data Pengangguran Terbuka Menggunakan Algoritma K-Means Berdasarkan Provinsi Jawa Barat Program Studi Manajemen Informatika , STMIK IKMI Cirebon,” vol. 12, no. 1, pp. 59–69, 2023.

M. Anam and A. M. Majid, “Analisis Pola Pengangguran Menggunakan Metode Clustering Algoritma K-Means Di Wilayah Kabupaten Cirebon,” vol. 4, no. 4, pp. 1691–1701, 2025.

I. Kamila et al., “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” vol. 5, no. 1, pp. 119–125, 2019.

D. Ananda, S. Rohimah, B. Susilo, D. Wulan, and A. Mustofa, “Implementation of K-Means in Grouping Birth Certificate Data in Indonesia Implementasi K-Means Dalam Pengelompokkan Data Akta Kelahiran di Indonesia,” pp. 66–71, 2022.

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Published

2026-06-24

How to Cite

Imelia Puspita Sari, Muhammad Rijal Firmansyah, Arzella Meilantika, Ade Herlina, & Nuari Anisa Sivi. (2026). Penerapan Metode K-Means Clustering dalam Pengelompokkan Tingkat Pengangguran di Provinsi Lampung. Jurnal Ilmiah Sistem Informasi Dan Ilmu Komputer, 6(2), 128–139. https://doi.org/10.55606/juisik.v6i2.2414