Implementation of DBSCAN Algorithm for Grouping Poverty Levels in Central Java Province
DOI:
https://doi.org/10.30865/json.v6i4.8553Keywords:
Poverty, Central Java Province, DBSCAN Clustering, Silhouette Score, Davies-Bouldin IndexAbstract
Poverty is a complex problem that hampers socio-economic development in Indonesia, especially in Central Java Province, which encounters significant challenges, with a poverty rate reaching 10.77% in 2023. This study aims to identify spatial patterns of poverty in 35 districts/cities in Central Java Province by grouping areas based on the number of poor individuals reported by the Central Java Province Statistics Agency (BPS) in 2023. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm groups districts/cities based on poverty data density with optimized parameters to produce statistically significant clusters. The results of the analysis reveal four clusters, specifically cluster 0 (moderate poverty), cluster 1 (high poverty), cluster 2 (very high poverty), and cluster 3 (low poverty). Model validation was executed using the Silhouette Score (0.447) and Davies-Bouldin Index (0.441), which showed the validity of the clustering. This study is anticipated to provide strategic implications for the Central Java Provincial Government in formulating more effective poverty alleviation policies, such as resource allocation adjusted to each cluster's characteristics. In addition, this study enables future exploration of additional socio-economic factors influencing poverty, such as the Human Development Index, education, health, infrastructure, resource accessibility, and comparative analysis of clustering algorithms for enhanced accuracy.References
A. Bahauddin, A. Fatmawati, and F. Permata Sari, “Analisis Clustering Provinsi Di Indonesia Berdasarkan Tingkat Kemiskinan Menggunakan Algoritma K-Means,” J. Manaj. Inform. dan Sist. Inf., vol. 4, no. 1, pp. 1–8, 2021, doi: 10.36595/misi.v4i1.216.
L. S. Hasibuan, “Analisis Pengaruh Ipm, Inflasi, Pertumbuhan Ekonomi Terhadap Pengangguran Dan Kemiskinan Di Indonesia,” J. Penelit. Pendidik. Sos. Hum., vol. 8, no. 1, pp. 53–62, 2023, [Online]. Available: https://jurnal-lp2m.umnaw.ac.id/index.php/JP2SH/article/view/2075/1261
U. Qulsum, J. Anggraini, and E. Putri, “ANALYSIS OF POVERTY AS A MAIN PROBLEM OF THE INDONESIAN,” 2024.
A. A. M. Davidescu, T. M. Nae, and M. S. Florescu, “From Policy to Impact: Advancing Economic Development and Tackling Social Inequities in Central and Eastern Europe,” Economies, vol. 12, no. 2, 2024, doi: 10.3390/economies12020028.
N. N. F. R, D. S. Anggraeni, and U. Enri, “Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient,” Tematik, vol. 9, no. 1, pp. 29–35, 2022, doi: 10.38204/tematik.v9i1.901.
N. K. Surbakti, “Data Mining Pengelompokan Pasien Rawat Inap Peserta BPJS Menggunakan Metode Clustering (Studi Kasus?: RSU.Bangkatan),” J. Inf. Technol., vol. 1, no. 2, pp. 47–53, 2021, doi: 10.32938/jitu.v1i2.1470.
M. Richia Putri, Gibran Satya Nugraha, and Ramaditia Dwiyansaputra, “Pengelompokan Provinsi di Indonesia Berdasarkan Indikator Pendidikan Menggunakan Metode K-Means Clustering,” J. Comput. Sci. Informatics Eng., vol. 7, no. 1, pp. 1–8, 2023, doi: 10.29303/jcosine.v7i1.509.
N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” J. Ilm. Inform. Dan Ilmu Komput., vol. 3, no. 1, pp. 46–56, 2024, [Online]. Available: https://doi.org/10.58602/jima-ilkom.v3i1.26
R. R. A. Rahman and A. W. Wijayanto, “Pengelompokan Data Gempa Bumi Menggunakan Algoritma Dbscan,” J. Meteorol. dan Geofis., vol. 22, no. 1, p. 31, 2021, doi: 10.31172/jmg.v22i1.738.
N. Sukarno Wijaya, M. Jajuli, and B. Arif Dermawan, “Penerapan Algoritma K-Means Clustering Dalam Menentukan Daerah Prioritas Penanganan Kemiskinan Di Wilayah Jawa Timur,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 4, pp. 7579–7584, 2024, doi: 10.36040/jati.v8i4.10248.
S. N. Mayasari and J. Nugraha, “Implementasi K-Means Cluster Analysis untuk Mengelompokkan Kabupaten/Kota Berdasarkan Data Kemiskinan di Provinsi Jawa Tengah Tahun 2022,” KONSTELASI Konvergensi Teknol. dan Sist. Inf., vol. 3, no. 2, pp. 317–329, 2023, doi: 10.24002/konstelasi.v3i2.7200.
F. Zahra, A. Khalif, and B. N. Sari, “Pengelompokan Tingkat Kemiskinan di Setiap Provinsi di Indonesia Menggunakan Algoritma K-Medoids,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 2, pp. 2830–7062, 2024, [Online]. Available: http://dx.doi.org/10.23960/jitet.v12i2.4199
D. J. Manalu, R. Rahmawati, and T. Widiharih, “PENGELOMPOKAN TITIK GEMPA DI PULAU SULAWESI MENGGUNAKAN ALGORITMA ST-DBSCAN (Spatio Temporal-Density Based Spatial Clustering Application with Noise),” J. Gaussian, vol. 10, no. 4, pp. 554–561, 2021, doi: 10.14710/j.gauss.v10i4.29499.
A. F. Boer, M. Al Haris, and R. Wasono, “Pengelompokan Daerah Rawan Bencana di Pulau Sumatera dengan Metode Density Based Spatial Clustering of Applications with Noise (DBSCAN),” Semin. Nas. Unimus, pp. 389–400, 2023, [Online]. Available: https://prosiding.unimus.ac.id/index.php/semnas/article/download/1481/1485
Purwanty, S. Martha, and S. Aprizkiyandari, “Pengelompokan kabupaten/kota di kalimantan barat berdasarkan faktor-faktor kemiskinan menggunakan metode dbscan,” Bul. Ilm. Mat. Stat. dan Ter., vol. 13, no. 1, pp. 17–26, 2024.
A. A. Bushra and G. Yi, “Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms,” IEEE Access, vol. 9, pp. 87918–87935, 2021, doi: 10.1109/ACCESS.2021.3089036.
D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional,” J. Tekno Kompak, vol. 15, no. 1, p. 131, 2021, doi: 10.33365/jtk.v15i1.744.
F. Dwi Handoko, A. Fauzi, D. Ryan, F. Kurniasih, P. Mutiara, and S. Taqwaning Afifi, “Transformasi Data Menjadi Informasi Pada Bisnis Intelijen,” J. Ilmu Hukum, Hum. dan Polit., vol. 2, no. 3, pp. 313–19, 2022, doi: 10.38035/jihhp.v2i3.1043.
M. S. Hasibuan, A. H. Lubis, and M. N. Sari, “Perbandingan algoritma clustering dbscan dan k-means dalam pengelompokan siswa terbaik Comparison of dbscan and k-means clustering algorithms in grouping the best students,” vol. 5, pp. 301–309, 2024.
R. A. Satria, I. Indriati, and S. Sutrisno, “Pengelompokan Hasil Pencarian Skripsi Berbahasa Indonesia Menggunakan Metode DBSCAN dengan Pembobotan BM25,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 4, pp. 781–790, 2023, doi: 10.25126/jtiik.20241046899.
R. Adha, N. Nurhaliza, U. Sholeha, and M. Mustakim, “Perbandingan Algoritma DBSCAN dan K-Means Clustering untuk Pengelompokan Kasus Covid-19 di Dunia,” SITEKIN J. Sains, Teknol. dan Ind., vol. 18, no. 2, pp. 206–211, 2021, [Online]. Available: https://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/12469
M. Sholeh, S. Suraya, and D. Andayati, “Penerapan Data Mining pada Model Clustering Data Kuesioner Mahasiswa terhadap Kinerja Dosen,” J. Eksplora Inform., vol. 13, no. 2, pp. 208–217, 2024, doi: 10.30864/eksplora.v13i2.751.
M. Daffa Rachman and A. Voutama, “Implementasi Algoritma K-Means Dalam Sistem Rekomendasi Musik Menggunakan Python,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 3, pp. 3857–3862, 2024, doi: 10.36040/jati.v8i3.9635.
Y. I. Kurniawan, “Pengelompokan Prioritas Negara Yang Membutuhkan Bantuan Menggunakan Clustering K-Means dengan Elbow dan Silhouette,” J. Pendidik. dan Teknol. Indones., vol. 3, no. 10, pp. 455–463, 2023, [Online]. Available: https://doi.org/10.52436/1.jpti.343
S. F. Djun, I. G. A. Gunadi, and S. Sariyasa, “Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM,” JTIM J. Teknol. Inf. dan Multimed., vol. 5, no. 4, pp. 354–364, 2024, doi: 10.35746/jtim.v5i4.434.
F. Fathurrahman, S. Harini, and R. Kusumawati, “Evaluasi Clustering K-Means Dan K-Medoid Pada Persebaran Covid-19 Di Indonesia Dengan Metode Davies-Bouldin Index (Dbi),” J. Mnemon., vol. 6, no. 2, pp. 117–128, 2023, doi: 10.36040/mnemonic.v6i2.6642.
Nurmadani and F. Rakhmawati, “Clustering flood prone areas in deli serdang regency using density-based spatial clustering of applications with noise (dbscan) method,” vol. 6, no. 2, pp. 261–272, 2023, doi: 10.24042/djm.
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