Modeling the Percentage of Poor Population in West Sumatra in 2024 Using Nonparametric B-Spline Regression

Imroatul Lathifah; Fadhilah Fitri; Fitri Mudia Sari

doi:10.20956/j.v22i2.47637

Authors

Imroatul Lathifah Departemen Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Padang
Fadhilah Fitri Departemen Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Padang
Fitri Mudia Sari Departemen Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Padang

DOI:

https://doi.org/10.20956/j.v22i2.47637

Keywords:

B-Spline regression, poverty, per capita expenditure, labor force participation, average years of schooling, Generalized Cross Validation (GCV), West Sumatera

Abstract

Poverty is a multidimensional issue and remains a major challenge in West Sumatra Province. This study analyzes the percentage of the poor population in 2024 using a nonparametric B-Spline regression approach with the independent variables being the percentage of per capita expenditure on food, the labor force participation rate, and the average length of schooling. Data are sourced from the official publication of the Central Statistics Agency of West Sumatra Province in 2024. The results show that the best model is obtained from the second-order B-Spline regression with one node for each independent variable, based on the minimum Generalized Cross Validation (GCV) value. This model produces a coefficient of determination of 92.46% and a Mean Absolute Percentage Error (MAPE) of 11.02%, indicating high prediction accuracy. Substantively, the average length of schooling has a negative effect on the poverty rate, while food expenditure and labor force participation have varying effects across regions. These findings indicate that B-Spline regression is effective in capturing nonlinear and complex relationships between socioeconomic variables, but its interpretation still needs to consider the empirical context in the field.

References

[1] Agus Triono, T., & Sangaji, R. C., 2023. Faktor Mempengaruhi Tingkat Kemiskinan di Indonesia: Studi Literatur Laporan Data Kemiskinan BPS Tahun 2022. Journal of Society Bridge, 1(1), 59–67. https://doi.org/10.59012/jsb.v1i1.5

[2] Aprieza, F., Ridwan, M. K., & Wilopo, W., 2022. Forecasting Analysis on Electricity Demand in the Special Region of Yogyakarta Under the Impact of the Covid-19 Pandemic. ASEAN Journal of Systems Engineering, 6(1), 12–20. https://doi.org/10.22146/ajse.v6i1.75149

[3] Ariesta, D., Gusriani, N., & Parmikanti, K., 2021. Estimasi Parameter Model Regresi Nonparametrik B-Splin E Pada Angka Kematian Maternal. Jurnal Matematika UNAND, 10(3), 342–354. https://doi.org/10.25077/jmu.10.3.342-354.2021

[4] Aryansah, M. P., & Suparti, S., 2024. Pemodelan Regresi Semiparametrik B Spline (Studi Kasus: Pengaruh Harga Emas dan Minyak Mentah Dunia Terhadap Indeks Harga Saham Gabungan). Indonesian Journal of Applied Statistics, 6(2), 140. https://doi.org/10.13057/ijas.v6i2.79365

[5] Badan Pusat Statistik, 2025. Statistik Indonesia 2025. Badan Pusat Statistik, 1101001, 790.

[6] Bakir, S. T., & Eubank, R. L. (2000). Nonparametric Regression and Spline Smoothing. Technometrics, 42(3), 318. https://doi.org/10.2307/1271102

[7] Bharti, E., 2021. The Effect of Education on Poverty. Georgia Institute of Technology.

[8] BPS Sumatera Barat., 2023. Sumatera Barat Dalam Angka 2023. Padang, 54, 1429.

[9] BPS Sumbar., 2025. Provinsi sumatera barat dalam angka 2025. Badan Statistik Provinsi Sumatera Barat, 54, 282–283. https://sumbar.bps.go.id/publication/2020/04/27/0bde2141fda787c1f0e923bf/provinsi-sumatera-barat-dalam-angka-2020.html

[10] De Hoyos, R. E., & Medvedev, D., 2011. Poverty effects of higher food prices: A global perspective. Review of Development Economics, 15(3), 387–402. https://doi.org/10.1111/j.1467-9361.2011.00615.x

[11] Devi, A. R., Mukid, M. A., & Yasin, H., 2014. Analisis Inflasi Kota Semarang menggunakan Metode Regresi Nonparametrik B-Spline . Jurnal Gaussian, 3(2), 193–202. http://ejournal-s1.undip.ac.id/index.php/gaussian

[12] Erdoğan, M. S., 2025. On nonparametric estimating ROC curve based on non-uniform rational B-Spline . PLOS ONE, 1–16. https://doi.org/10.1371/journal.pone.0330175

[13] Faharuddin, F., Yamin, M., Mulyana, A., & Yunita, Y., 2023. Impact of food price increases on poverty in Indonesia: empirical evidence from cross-sectional data. Journal of Asian Business and Economic Studies, 30(2), 126–142. https://doi.org/10.1108/JABES-06-2021-0066

[14] Fajar, M., & Fajariyanto, E., 2023. Penerapan Smoothing B-Spline s Pada Hubungan Antara Pertumbuhan Ekonomi Dan Tingkat Kebahagiaan. Jurnal Gaussian, 11(4), 605–615. https://doi.org/10.14710/j.gauss.11.4.605-615

[15] Farizi, G. I., Permana, D., & Salma, A., 2024. Modeling of Employment Participation Rate Against the Percentage of Poor Population in East Java in 2023 Using B-Spline Method. UNP Journal Of Statistics and Data Science, 2, 381–385.

[16] Laroussi, I., 2024. B-Spline Estimate of the Regression Function Under General Censorship Model. Jordan Journal of Mathematics and Statistics, 17(1), 179–197. https://doi.org/10.47013/17.1.11

[17] Madden, D., & Smith, F., 2000. Poverty in Ireland, 1987-1994: A stochastic dominance approach. Economic and Social Review, 31(3), 187–214.

[18] Montaño Moreno, J. J., Palmer Pol, A., Sesé Abad, A., & Cajal Blasco, B., 2013. El índice R-MAPE como medida resistente del ajuste en la previsiońn. Psicothema, 25(4), 500–506. https://doi.org/10.7334/psicothema2013.23

[19] Nasir, R. S., Wahid, M. A., Padang, D. R. A., Islamiyati, A., & Raupong, R., 2024. Analisis Kemiskinan di Sulawesi Selatan dengan Regresi Nonparametrik Berbasis B-Spline . Indonesian Journal of Applied Statistics, 7(1), 27. https://doi.org/10.13057/ijas.v7i1.80716

[20] Nur, M. T., Khoirotunnisa, D., Widyaningsih, W., & Nohe, D. A., 2022. Regresi Data Panel Untuk Memodelkan Persentase Kemiskinan Di Kalimantan Timur. Prosiding Seminar Nasional Matematika Dan Statistika, 2, 13. http://jurnal.fmipa.unmul.ac.id/index.php/SNMSA/article/view/901

[21] Rahasia, Z., Resmawan, R., & Isa, D. R., 2020. Pemodelan Data Time Series dengan Pendekatan Regresi Nonparametrik B-Spline . AKSIOMA : Jurnal Matematika Dan Pendidikan Matematika, 11(1), 9–16. https://doi.org/10.26877/aks.v11i1.4903

[22] Rahman, M. A., Samsiah, N., Id, S., Hamdan, R., & Othman, Z. A., 2021. A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group. PLOS ONE, 1–25. https://doi.org/10.1371/journal.pone.0255312

[23] Rodríguez-álvarez, M. X., & Gonzalez, F., 2023. Multidimensional adaptive P-splines with application to neurons ’ activity studies. Biometrics, Cc. https://doi.org/10.1111/biom.13755

[24] Rohman, A. R., Pudjihardjo, M., & Susilo, S., 2023. How Labor Force Participation Mediating Poverty Rate by Others Factors. International Journal of Environmental, Sustainability, and Social Science, 4(5), 1650–1659. https://doi.org/10.38142/ijesss.v4i5.907

[25] Sasmita, Y., Budiman Johra, M., Jatmiko, Y. A., Lubis, D. A., Rahmad, R., & Sohibien, G. P. D., 2025. Quantile Regression with Constrained B-Spline s for Modelling Average Years of Schooling and Household Expenditure. Jurnal Aplikasi Statistika & Komputasi Statistik, 17(1), 23–28. https://doi.org/10.34123/jurnalasks.v17i1.793

[26] Sihombing, P. R., & Famalika, A., 2022. Penerapan Analisis Regresi Nonparametrik dengan Pendekatan Regresi Kernel dan Spline. Jurnal Ekonomi Dan Statistik Indonesia, 2(2), 172–181. https://doi.org/10.11594/jesi.02.02.05

[27] Siregar, I. R., Nugraha, A., & Notodiputro, K. A., 2025. The Comparison Of Long Short-Term Memory and Bidirectional Long Short-Term Memory For. Barekeng: Journal of Mathematics and Its Applications, 19(1), 245–258.

[28] Tipka, J., & Ramly, F., 2025. the Effect of Economic Growth, Labor Force Participation Rate, and Human Development Index on Poverty in Maluku (2010-2022): Multiple Linear Regression Approach. Variance: Journal of Statistics and Its Applications, 7(1), 61–72. https://ojs3.unpatti.ac.id/index.php/variance/

[29] Wahyuningsih, T. D., Handajani, S. S., & Indriati, D, 2018. Penerapan Generalized Cross Validation dalam Model Regresi Smoothing Spline pada Produksi Ubi Jalar di Jawa Tengah. Indonesian Journal of Applied Statistics, 1(2), 117–125.

[30] Witte, D. De, Abad, A. A., Neyens, T., Verbeke, G., & Molenberghs, G., 2024. A joint penalized spline smoothing model for the number of positive and negative COVID- 19 tests. PLOS ONE, 1–21. https://doi.org/10.1371/journal.pone.0303254

[31] Wulandary, S., & Purnama, D. I., 2020. Perbandingan Regresi Nonparametrik Kernel Dan B-Spline s Pada Pemodelan Rata-Rata Lama Sekolah Dan Pengeluaran Perkapita Di Indonesia. Jambura Journal of Probability and Statistics, 1(2), 89–97. https://doi.org/10.34312/jjps.v1i2.7501