Pemetaan Sebaran Tingkat Kemiskinan Berbasis Sistem Informasi Geografis di Provinsi Jawa Tengah Tahun 2023–2025

Authors

  • Adisti Khairunnisa N Universitas PGRI Semarang
  • Bambang Agus Herlambang Universitas PGRI Semarang
  • Ahmad Khoirul Anam Universitas PGRI Semarang

DOI:

https://doi.org/10.55606/jtmei.v4i3.5784

Keywords:

Central Java, Geographic Information System, Poverty, Spatial Analysis, WebGIS

Abstract

Poverty remains a major socio-economic challenge in regional development, particularly in Central Java Province, Indonesia. Variations in geographic characteristics, economic structures, and development levels have led to significant disparities in poverty across districts and cities. Based on official data from the Central Bureau of Statistics (BPS) of Central Java, poverty conditions have shown dynamic changes during 2023–2025. This study aims to develop a web-based Geographic Information System (WebGIS) to map the spatial distribution of poverty levels at district and city administrative levels. The research involves collecting poverty data from BPS, processing spatial data using administrative boundary shapefiles, and performing attribute joins between spatial and non-spatial datasets. Thematic maps are created using QGIS and exported into an interactive web format through the qgis2web plugin, integrated into a WebGIS platform built with HTML, CSS, and JavaScript. The resulting WebGIS effectively visualizes spatial patterns and disparities of poverty across regions, allowing users to explore data by year and identify high-poverty areas for development prioritization. This research demonstrates the potential of WebGIS as an analytical and decision-support tool for regional planning, enabling policymakers to design more targeted and data-driven poverty alleviation strategies.

Downloads

Download data is not yet available.

References

Arya, P. K., Sur, K., Dhote, S., & Siral, H. (2025). Integrating multi-source satellite imagery and socio-economic household data for wealth-based poverty assessment of India: A GIS and machine learning–based approach. Social Indicators Research. https://doi.org/10.1007/s11205-025-03614-w

Asrin, F. (2023). Web map services (WMS) data geospasial batas administrasi kelurahan indikatif. Jurnal Informatika Polinema, 10(1), 93–98. https://doi.org/10.33795/jip.v10i1.1495

Azzahra, F., Azzarah, R. A., Harahap, M. A., & Enjelina, S. (2025). Integrasi SIG dalam analisis kepadatan penduduk di Desa Limau Manis, Desa Medan Sinembah, dan Desa Ujung Serdang. Jurnal Pendidikan dan Ilmu Geografi, 10(1), 84–90.

Bahri, S., Midyanti, D. M., & Hidayati, R. (2020). Layanan masyarakat di Kota Pontianak. Journal of Computer Engineering System and Science, 5(1). https://doi.org/10.24114/cess.v5i1.15666

Fattaah, R., & Muchayan, A. (2024). Pemetaan ketimpangan sosial dan ekonomi pada masyarakat miskin di wilayah Kecamatan Genteng berbasis WebGIS. Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer, 6(2). https://doi.org/10.26905/jasiek.v7i2.12285

Huluuma, M. M., Talakua, A. C., Yogia, H., & Uly, P. (2024). Geographic information system for mapping poverty levels in East Sumba District. Malcom: Indonesian Journal of Machine Learning and Computer Science, 4(1), 181–187. https://doi.org/10.57152/malcom.v4i1.1050

Maharani, C., Ningrum, D. A., Fatmawati, A. E., & Fadilla, A. (2024). Dampak kemiskinan terhadap kualitas pendidikan anak di Indonesia: Rekomendasi kebijakan yang efektif. Journal of Macroeconomics and Social Development, 1(3), 1–10. https://doi.org/10.47134/jmsd.v1i3.199

Maulana, R., Pitoyo, A. J., Arif, M., & Alfana, F. (2022). Analisis pengaruh kemiskinan dan kondisi ekonomi terhadap indeks pembangunan manusia di Provinsi Jawa Tengah tahun 2013–2017. Media Komunikasi Geografi, 23(1), 12–24. https://doi.org/10.23887/mkg.v23i1.39301

Priseptian, L., & Primandhana, W. P. (2022). Analisis faktor-faktor yang mempengaruhi kemiskinan. Jurnal Forum Ekonomi, 24(1), 45–53. https://doi.org/10.30872/jfor.v24i1.10362

Putri, O. S., Sitohang, L. L., & Prasetya, S. P. (2025). Visualisasi dan analisis distribusi spasial jumlah penduduk miskin Jawa Timur berbasis SIG dengan teknologi QGIS. Riset Konseptual, 9(4), 824–834. https://doi.org/10.28926/riset_konseptual.v9i4.1339

Riadi, B., Hermawan, E., Hadiyat, Y., Firmansyah, Y., & Pamungkas, P. P. (2023). Spatial assessment of poverty distribution in West Java Province. Global Scientific Review, 22, 274–287.

Sakti, P., Pusat, B., Kabupaten, S., & Korespondensi, P. (2022). Analisis kemiskinan digital Indonesia di era revolusi industri 4.0. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 9(1). https://doi.org/10.25126/jtiik.202295021

Sari, Y. A., Studi, P., Pembangunan, E., & Ekonomi, F. (2021). Pengaruh upah minimum terhadap tingkat pengangguran terbuka Jawa Tengah. Equilibrium, 10(2), 121–130. https://doi.org/10.35906/je001.v10i2.785

Watrianthos, R., & Suryadi, S. (2023). Pemetaan tingkat kriminalitas di Indonesia: Analisis spasial dengan pendekatan SIG pada tingkat provinsi. Bulletin of Information Technology, 4(2), 353–360. https://doi.org/10.47065/bit.v4i3.861

Wells, J., Grant, R., Chang, J., & Kayyali, R. (2021). Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualise socio-economic and public health data. BMC Public Health, 21, Article 12072. https://doi.org/10.1186/s12889-021-12072-1

Downloads

Published

2025-09-30

How to Cite

Adisti Khairunnisa N, Bambang Agus Herlambang, & Ahmad Khoirul Anam. (2025). Pemetaan Sebaran Tingkat Kemiskinan Berbasis Sistem Informasi Geografis di Provinsi Jawa Tengah Tahun 2023–2025. Jurnal Teknik Mesin, Industri, Elektro Dan Informatika, 4(3), 19–27. https://doi.org/10.55606/jtmei.v4i3.5784