Unsupervised Clustering Untuk Pengolahan Data Kemiskinan di Jawa Tengah Dengan Menggunakan Metode Self-Organizing Maps

Yunus Anis; Eko Nur Wahyudi; Sri Mulyani

doi:10.47065/josh.v6i2.6439

Yunus Anis * Universitas Stikubank, Semarang, Indonesia
Eko Nur Wahyudi Universitas Stikubank, Semarang, Indonesia
Sri Mulyani Universitas Stikubank, Semarang, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v6i2.6439

Keywords: Self-Organizing Maps (SOM); Unsupervised Clustering; Poverty Data; Central Java; Socio-Economic Indicators

Abstract

This study aims to analyze and cluster poverty data in Central Java using the Self-Organizing Maps (SOM) method, an approach in unsupervised learning that is efficient in mapping multidimensional data into two-dimensional representations. The poverty data used includes various socio-economic indicators, such as income, education, health access, and housing conditions. By applying SOM, this study attempts to identify hidden patterns and relationships between variables that contribute to poverty in each region in Central Java. The results of this clustering are expected to provide deeper insight into the characteristics and distribution of poverty, as well as assist in making more targeted policies in poverty alleviation efforts. This study shows that the SOM method is able to effectively group areas with similar poverty characteristics, and provide visualizations that facilitate understanding of the complexity of poverty data in Central Java. The implementation of this method is able to produce 4 groups / clusters of poverty levels which are expected to be the basis for further research in socio-economic mapping, as well as a tool in planning and evaluating poverty alleviation programs at the regional level.

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References

M. E. Rijoly, F. L. Lumalessil, and B. P. Tomasouw, “Pengelompokan Desa di Kabupaten Maluku Barat Daya Berdasarkan Karakteristik Kemiskinan Menggunakan Metode Self Organizing Maps (SOM),” Zeta - Math J., vol. 5, no. 1, pp. 16–20, 2020, doi: 10.31102/zeta.2020.5.1.16-20.

R. P. P. Sinurat, “Analisis Faktor-Faktor Penyebab Kemiskinan Sebagai Upaya Penanggulangan Kemiskinan Di Indonesia,” J. Regist., vol. 5, no. 2, pp. 87–103, 2023, doi: 10.33701/jurnalregistratie.v5i2.3554.

L. Nansadiqa, “ANALISIS PENGARUH PERTUMBUHAN EKONOMI TERHADAP KEMISKINAN DI INDONESIA,” J. HEI EMA, vol. 3, no. 2, pp. 46–60, 2024, [Online]. Available: file:///C:/Users/ASUS/Downloads/_4.+Lisa+Nansadiqa_publish-hal.+46-60.pdf.

R. Y. Wulansari, N. Fadhilah, M. Huda, A. Z. Abidin, and A. E. Sujianto, “Faktor Yang Mempengaruhi Kemiskinan di Indonesia,” J. Econ. Manag. Account. Technol., vol. 6, no. 1, pp. 82–95, 2023, doi: 10.32500/jematech.v6i1.3928.

C. Maharani, D. A. Ningrum, A. E. Fatmawati, and A. Fadilla, “Dampak Kemiskinan terhadap Kualitas Pendidikan Anak di Indonesia: Rekomendasi Kebijakan yang Efektif,” J. Macroecon. Soc., vol. 1, no. 3, pp. 1–10, 2024, doi: 10.47134/jmsd.v1i3.199.

N. Saribulan, H. Rahman, and S. Rassanjani, “Perkembangan Penelitian Penanggulangan Kemiskinan di Indonesia: Analisis Bibliometrik dan Analisis Konten,” J. Ilmu Sos. dan Hum., vol. 12, no. 2, pp. 309–321, 2023, doi: 10.23887/jish.v12i2.62375.

R. H. B. Bangun and A. Meimela, “Pemetaan Kemiskinan Melalui Pendekatan Geographically Weighted Lasso,” J. Ekon. Indones., vol. 9, no. 3, pp. 233–246, 2020, doi: 10.52813/jei.v9i3.58.

J. Yanti, R. P. Sari, and D. Prawira, “PEMETAAN BERBASIS WEB TINGKAT KEMISKINAN MENGGUNAKAN MODEL KESEJAHTERAAN KELUARGA PADA APLIKASI SIG (Studi Kasus : Kelurahan Desa Kapur),” Coding J. Komput. dan Apl., vol. 11, no. 3, p. 303, 2023, doi: 10.26418/coding.v11i03.53073.

Z. A. Leleury and B. P. Tomasouw, “Pengelompokkan Dan Pemetaan Karakteristik Kemiskinan Di Kabupaten Maluku Barat Daya Provinsi Maluku Dengan Menggunakan Self-Organizing Map Dan Analisis Biplot,” BAREKENG J. Ilmu Mat. dan Terap., vol. 13, no. 2, pp. 093–106, 2019, doi: 10.30598/barekengvol13iss2pp093-106ar810.

V. D. Melliny, Y. Purwanti, and R. A. Akbar, “Faktor Penentu Tingkat Kemiskinan di Indonesia Bagian Tengah,” J. Manaj. dan Bisnis, vol. 1, no. 1, pp. 1–6, 2022.

S. H. Hastuti, W. P. Nurmayanti, and A. A. Saputri, “Penerapan Metode Clustering Self Organizing Maps (Som) Dan K-Affinity Propagation (K-Ap) Dalam Mengelompokkan Nilai Tukar Petani Di Indonesia 2022,” Var. J. Stat. Its Appl., vol. 5, no. 1, pp. 79–88, 2023, doi: 10.30598/variancevol5iss1page79-88.

L. M. Yahya, K. Kertanah, and U. Hidayaturrohman, “Penerapan Algoritma Self Organizing Maps (SOM) Dan K-Means Untuk Mengelompokkan Akseptor KB Di NTB,” J. Stat. dan Komputasi, vol. 3, no. 1, pp. 32–41, 2024, doi: 10.32665/statkom.v3i1.2960.

F. R. Hariri and D. P. Pamungkas, “Self Organizing Map-Neural Network untuk Pengelompokan Abstrak,” Creat. Inf. Technol. J., vol. 3, no. 2, p. 160, 2016, doi: 10.24076/citec.2016v3i2.74.

M. Lin, M. Liu, L. Dong, F. Caruso, and S. Li, “Modeling intraspecific variation in habitat utilization of the Indo-Pacific humpback dolphin using self-organizing map,” Ecol. Indic., vol. 144, no. August, p. 109466, 2022, doi: 10.1016/j.ecolind.2022.109466.

N. Imani, A. I. Alfassa, and A. M. Yolanda, “Analisis Cluster Terhadap Indikator Data Sosial Di Provinsi Nusa Tenggara Timur Menggunakan Metode Self Organizing Map (Som),” J. Gaussian, vol. 11, no. 3, pp. 458–467, 2023, doi: 10.14710/j.gauss.11.3.458-467.

W. Wahyuni, M. Gazali, and ..., “Pengelompokan Dan Pemetaan Karakteristik Kemiskinan Di Provinsi Nusa Tenggara Barat Menggunakan Self Organizing Map (SOM) Dan Biplot,” Syntax Lit. J. …, vol. 7, no. 11, 2022, [Online]. Available: https://jurnal.syntaxliterate.co.id/index.php/syntax-literate/article/view/9965.

S. Nisrina, W. P. Nurmayanti, Basirun, Kertanah, and Muhammad Gazali, “Penerapan Metode Clustering SOM dan DBSCAN dalam Mengelompokkan Unmet Need Keluarga Berencana di Nusa Tenggara Barat,” J Stat. J. Ilm. Teor. dan Apl. Stat., vol. 15, no. 2, pp. 237–244, 2022, doi: 10.36456/jstat.vol15.no2.a5549.

M. D. Novianto and S. Andayani, “Analisis Cluster dengan Metode SOM untuk Pengelompokan Provinsi di Indonesia berdasarkan Indikator Kriminalitas,” J. Kaji. dan Terap. Mat., vol. 9, pp. 39–53, 2023.

M. F. Faiz and A. Fauzan, “Implementasi Metode Clustering SOM Dalam menganalisis Alasan Tidak KB di Kabupaten Temanggung,” Pros. Semin. Nas. Sains Data, vol. 3, no. 1, pp. 47–58, 2023, doi: 10.33005/senada.v3i1.74.

I. A. Siregar, “Analisis Dan Interpretasi Data Kuantitatif,” ALACRITY J. Educ., vol. 1, no. 2, pp. 39–48, 2021, doi: 10.52121/alacrity.v1i2.25.

R. E. Febriani, “Sistem Informasi Pengelolaan Data Nilai Siswa,” vol. 8, no. 4, p. 2023, 2023.

D. Adri, E. R. Sari, A. H. J. Wotu, * Korenspondesnsi, and K. Kunci, “Education and Learning Journal Penggunaan Microsoft Excel dalam Penyusunan Data dalam Tabel Distribusi Frekuensi,” vol. 5, no. 2, pp. 94–111, 2024, [Online]. Available: http://dx.doi.org/10.33096/eljour.v5i2.1052http://jurnal.fai@umi.ac.id.

M. F. Iqbal and H. P. Putro, “Penerapan Simple Agile Methodology dalam Pengembangan Aplikasi Web,” J. Autom., vol. 4, no. 1, pp. 1–6, 2023.

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