Poverty Forecasting Analysis in Bengkulu Province in 2025-2027

Kartika Yuni Hartati Situngkir; Barika Barika

doi:10.55927/fjmr.v3i12.12223

Authors

Kartika Yuni Hartati Situngkir Universitas Bengkulu
Barika Barika Universitas Bengkulu

DOI:

https://doi.org/10.55927/fjmr.v3i12.12223

Keywords:

Poverty Forecasting, Central Statistics Agency, Bengkulu Province

Abstract

Poverty remains a major challenge in Bengkulu Province, with a relatively high poverty rate compared to other provinces in Sumatra. This study aims to predict the poverty rate in Bengkulu Province using the Markov Chain approach for the period 2025-2027, based on historical data from 2015-2024 obtained from the Central Statistics Agency (BPS). Through the analysis of transition probabilities between poverty statuses using the Markov Chain approach, this study found that most districts/cities in Bengkulu Province showed a consistent downward trend in poverty, especially in areas such as South Bengkulu and Rejang Lebong. However, several areas, such as Central Bengkulu and Seluma, still experience quite high poverty rates, and are even predicted to increase in the coming years. This indicates that these areas require more attention in poverty alleviation policies. The results of this prediction are expected to be a reference for the government in formulating more effective poverty alleviation policies, with a focus on increasing access to education, health, and job creation. Thus, this study provides an important contribution to more targeted policy planning to reduce poverty levels in Bengkulu Province.

Downloads

Download data is not yet available.

References

Arifin, A. (2019). Analisis Faktor - Faktor Yang Mempengaruhi Kemiskinandi Indonesia. Jurnal Administrasi Publik Dan Bisnis, 1(2), 1–15. https://doi.org/10.36917/japabis.v1i2.18

Bengkulu, B. P. S. P. (n.d.). Persentase Penduduk Miskin September 2022 naik menjadi 9,57 persen. https://www.bps.go.id/id/pressrelease/2023/01/16/2015/persentase-penduduk-miskin-september-2022-naik-menjadi-9-57-persen.html

Bappenas. (2020). Pendidikan dan Kesehatan sebagai Pilar Pengentasan Kemiskinan di Indonesia. Jakarta: Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional.

Chambers, R., & Conway, G. (2020). Sustainable Rural Livelihoods: Practical Concepts for the 21st Century. IDS Discussion Paper 296, Institute of Development Studies.

Ekaputri, R. A., Barika, B., Azansyah, A., & Zulyanto, A. (2023). Analysis of Economic Growth, Agglomeration and Poverty in Southern Sumatera. Equity: Jurnal Ekonomi, 11(1), 36–45. https://doi.org/10.33019/equity.v11i1.136

Ferezegia, D. V. (2018). Jurnal Sosial Humaniora Terapan Analisis Tingkat Kemiskinan. Jurnal Sosial Humaniora Terapan, 4(1), 1–6. http://journal.vokasi.ui.ac.id/index.php/jsht/article/download/6/1

Jennifer Anne Haley. (2001). Information To Users Umi. Dissertation, 274.

Lai, P., He, Q., Grundy, J., Chen, F., Abdelrazek, M., Hosking, J., & Yang, Y. (2019). Summary of Changes (Issue 1).

Malau, L. A. (2023). Memprediksi Kemiskinan Menurut Kabupaten Di Kota Medan Menggunakan Metode Analisis Rantai Markov. E-Jurnal. http://dx.doi.org/10.31219/osf.io/yjs3c

Masuku, F. N., Langi, Y. A. R., & Mongi, C. (2018). Analisis Rantai Markov Untuk Memprediksi Perpindahan Konsumen Maskapai Penerbangan Rute Manado-Jakarta Analysis of Markov Chain To Predict Consumer Movement of Airline Route Manado-Jakarta. Ilmiah Sains, 18(2), 1–5.

Maslow, A. H. (2021). Ekonomi dan Kesejahteraan: Teori dan Praktik Pembangunan. Jakarta: Pustaka Pelajar.

Matondang, E. (2017). Finding Out the Potency of Nusa Tenggara Timur in Poverty Allevation: The Effect of Local Government’s Policy. Jurnal Bina Praja, 9(2), 231–242. https://doi.org/10.21787/jbp.09.2017.231-242

Muryani, Esquivias, M. A., Sethi, N., & Iswanti, H. (2021). Dynamics of Income Inequality, Investment, and Unemployment in Indonesia. Journal of Population and Social Studies, 29, 660–678. https://doi.org/10.25133/JPSSv292021.040

Nawawi, A. H., & Evangs Mailoa. (2024). Prediksi Lahan Deforestasi Dan Reforestasi Hutan Kalimantan Timur Dengan Metode Rantai Markov. Decode: Jurnal Pendidikan Teknologi Informasi, 4(1), 251–259. https://doi.org/10.51454/decode.v4i1.268

Ningsi, B. A., & Putri, D. N. (2023). Application of Markov Chain to Prediction Poverty in Banten Province. JTAM (Jurnal Teori Dan Aplikasi Matematika), 7(1), 47. https://doi.org/10.31764/jtam.v7i1.10057

Nugroho, O. T. (1969). Mubyarto dan Ilmu Ekonomi yang Membumi. Pusat Studi Pancasila UGM, 1–20.

Nurman, T. A., Syata, I., & Wulandari, C. D. (2021). Prediksi Hasil Panen Kopi di Sulawesi Menggunakan Analisis Rantai Markov. Jurnal MSA ( Matematika Dan Statistika Serta Aplikasinya ), 9(2), 120–127. https://doi.org/10.24252/msa.v9i2.25413

Paddu, H. (2022). Determinants of Poverty Level in Indonesia. Mutmainnah, et.al DETERMINANTS OF POVERTY LEVEL IN INDONESIA under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Jurnal Ekonomi, 11(03), 2022. http://ejournal.seaninstitute.or.id/index.php/Ekonomi

Pandina, F. D. (2023). Factors Affecting Poverty in East Nusa Tenggara Province in 2017-2022. 12(04), 2348–2353.

Rambe, R. A., Purmini, P., & Anitasari, M. (2023). Probability of Getting Households Out of Poverty: Empirical Studies in Indonesia. International Journal of Economics, Management and Accounting, 31(2), 397–416. https://doi.org/10.31436/ijema.v31i2.1103

Silalahi, D. K. (2020). Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province. JTAM | Jurnal Teori Dan Aplikasi Matematika, 4(1), 76. https://doi.org/10.31764/jtam.v4i1.1888

Sudiansyah, K., Sukiyono, K., & Badrudin, R. (2023). Peramalan Harga Bawang Putih di Kota Bengkulu, Provinsi Bengkulu dan Indonesia. Buletin Agritek, 4(2), 34–48. https://epublikasi.pertanian.go.id/berkala/bulagritek/article/view/3533/3513

Sukarna, dan. (2014). Aplikasi Analisis Rantai Markov Untuk Memprediksi Status Pasien Rumah Sakit Umum Daerah Kabupaten Barru Application Ofmarkovchain Analysisforpredictingstatus of Patient At Barru Hospital. Online Jurnal of Natural Science, 3(3), 313–321.

Todaro, M. P., & Smith, S. C. (2022). Economic Development (13th ed.). Boston: Pearson Education.

World Bank. (2021). The Impact of Infrastructure on Poverty Reduction in Southeast Asia. Washington, D.C.: World Bank Group.