An exploratory study of high-educated poverty through machine learning approach: a case study of East Java, Indonesia
Abstract
Purpose – the purpose of the article is to compare the importance factors for high-educated poverty modeling in the four cultural regions of East Java.
Research methodology – using data from the Indonesian National Survey, this study employs Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and K-Nearest Neighbor (KNN) algorithms as classification methods in machine learning.
Findings – the analysis results show that all agorithms has high accuracy with XGboost as the best performing model. The number of household members and the use of internet technology are among the most important variables affecting high-educated poverty in each cultural region in East Java.
Research limitations – the research was exclusively carried out in East Java, Indonesia, potentially restricting its applicability to other geographical areas or nations. Additionally, it relied on cross-sectional data, implying that a causal relationship between the independent and dependent variables cannot be inferred.
Practical implications – based on the results of this analysis, it is recommended that the government and related parties focus more on improving access to education and the internet in cultural areas in East Java.
Originality/Value – this study addresses a significant knowledge gap by exploring regional variations in the factors affecting high-educated poverty, emphasizing the importance of tailored strategies for each cultural region in East Java.
Keyword : higher education, machine learning, poverty
How to Cite
Satria, D., Permani, R., Winarno, K., Kaluge, D., Indraswari, C. R., & Handrito, R. P. (2025). An exploratory study of high-educated poverty through machine learning approach: a case study of East Java, Indonesia. Business, Management and Economics Engineering, 23(1), 92–107. https://doi.org/10.3846/bmee.2025.20808
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Weldearegay, S. K., Tefera, M. M., & Feleke, S. T. (2021). Impact of urban expansion to peri-urban smallholder farmers’ poverty in Tigray, North Ethiopia. Heliyon, 7(6), Article e07303. https://doi.org/10.1016/j.heliyon.2021.e07303
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Chankseliani, M., & McCowan, T. (2021). Higher education and the sustainable development goals. Higher Education, 81, 1–8. https://doi.org/10.1007/s10734-020-00652-w
Datzberger, S. (2018). Why education is not helping the poor. Findings from Uganda. World Development, 110, 124–139. https://doi.org/10.1016/j.worlddev.2018.05.022
Djurović, S., Lazarević, D., Ćirković, B., Mišić, M., Ivković, M., Stojčetović, B., Petković, M., & Ašonja, A. (2024). Modeling and prediction of surface roughness in hybrid manufacturing–milling after FDM using artificial neural networks. Applied Sciences, 14(14), Article 5980. https://doi.org/10.3390/app14145980
Dogan, E., Madaleno, M., Inglesi-Lotz, R., & Taskin, D. (2022). Race and energy poverty: Evidence from African-American households. Energy Economics, 108, Article 105908. https://doi.org/10.1016/j.eneco.2022.105908
Erlando, A., Riyanto, F. D., & Masakazu, S. (2020). Financial inclusion, economic growth, and poverty alleviation: Evidence from eastern Indonesia. Heliyon, 6(10), Article e05235. https://doi.org/10.1016/j.heliyon.2020.e05235
Flores, K. M. G., & Morejón, V. M. M. (2022). Fundamentals of human capital and education elements of the entrepreneurship ecosystem. CrossCultural Management Journal, 2022(2), 149–157.
Galperin, H., & Viecens, M. F. (2017). Connected for development? Theory and evidence about the impact of Internet technologies on poverty alleviation. Development Policy Review, 35(3), 315–336. https://doi.org/10.1111/dpr.12210
Gautam, R. S., Rastogi, S., Rawal, A., Bhimavarapu, V. M., Kanoujiya, J., & Rastogi, S. (2022). Financial technology and its impact on digital literacy in India: Using poverty as a moderating variable. Journal of Risk and Financial Management, 15(7), Article 311. https://doi.org/10.3390/jrfm15070311
Guo, Y. (2023). Research on the application of ANN in enterprise financial risk evaluation information system with digital empowerment. Proceedings of the 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023), 672–680. https://doi.org/10.2991/978-94-6463-246-0_81
Hofmarcher, T. (2021). The effect of education on poverty: A European perspective. Economics of Education Review, 83, Article 102124. https://doi.org/10.1016/j.econedurev.2021.102124
Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82(129), 657–673. https://doi.org/10.1016/j.landusepol.2018.12.030
Hu, S., Ge, Y., Liu, M., Ren, Z., & Zhang, X. (2022). Village-level poverty identification using machine learning, high-resolution images, and geospatial data. International Journal of Applied Earth Observation and Geoinformation, 107, Article 102694. https://doi.org/10.1016/j.jag.2022.102694
Ibrahem Ahmed Osman, A., Ahmed, A. N., Chow, M. F., Huang, Y. F., & El-Shafie, A. (2021). Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12(2), 1545–1556. https://doi.org/10.1016/j.asej.2020.11.011
Itoo, F., Meenakshi, & Singh, S. (2021). Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. International Journal of Information Technology, 13(4), 1503–1511. https://doi.org/10.1007/s41870-020-00430-y
Jacobus, E. H., Kindangen, P., & Walewangko, E. N. (2018). Analisis faktor-faktor yang mempengaruhi kemiskinan rumah tangga di Sulawesi Utara [Analysis of factors affecting household poverty in North Sulawesi]. Journal of Regional Economic and Finance Development, 19(3). https://doi.org/10.35794/jpekd.19900.19.7.2018
Jiang, L., Wang, H., Tong, A., Hu, Z., Duan, H., Zhang, X., & Wang, Y. (2020). The measurement of green finance development index and its poverty reduction effect: Dynamic panel analysis based on improved entropy method. Discrete Dynamics in Nature and Society, 2020, Article 8851684. https://doi.org/10.1155/2020/8851684
Lechman, E., & Popowska, M. (2022a). Harnessing digital technologies for poverty reduction. Evidence for low-income and lower-middle income countries. Telecommunications Policy, 46(6), Article 102313. https://doi.org/10.1016/j.telpol.2022.102313
Liu, W., Li, J., & Zhao, R. (2023). The effects of rural education on poverty in China: A spatial econometric perspective. Journal of the Asia Pacific Economy, 28(1), 176–198. https://doi.org/10.1080/13547860.2021.1877240
Nili, S. (2018). Global poverty, global sacrifices, and natural resource reforms. International Theory, 11, 48–80. https://doi.org/10.1017/S1752971918000209
Marques, G., Pitarma, R., Garcia, N. M., & Pombo, N. (2019). Internet of things architectures, technologies, applications, challenges, and future directions for enhanced living environments and healthcare systems: A review. Electronics, 8(10), Article 1081. https://doi.org/10.3390/electronics8101081
Meo, M. S., Kumar, B., Chughtai, S., Khan, V. J., Dost, M. K. B., & Nisar, Q. A. (2020). Impact of unemployment and governance on poverty in Pakistan: A fresh insight from non-linear ARDL co-integration approach. Global Business Review, 24(5), 1007–1024. https://doi.org/10.1177/0972150920920440
Mora-Rivera, J., & García-Mora, F. (2021). Internet access and poverty reduction: Evidence from rural and urban Mexico. Telecommunications Policy, 45(2), Article 102076. https://doi.org/10.1016/j.telpol.2020.102076
Nayeem, M. J., Rana, S., Alam, F., & Rahman, M. A. (2021, February 27–28). Prediction of hepatitis disease using K-nearest neighbors, naive bayes, support vector machine, multi-layer perceptron and random forest. In Proceedings of the 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) (pp. 280–284). Dhaka, Bangladesh. IEEE. https://doi.org/10.1109/ICICT4SD50815.2021.9397013
Olopade, B. C., Okodua, H., Oladosun, M., & Asaleye, A. J. (2019). Human capital and poverty reduction in OPEC member-countries. Heliyon, 5(8), Article e02279. https://doi.org/10.1016/j.heliyon.2019.e02279
Omar, M. A., & Inaba, K. (2020). Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis. Journal of Economic Structures, 9, Article 37. https://doi.org/10.1186/s40008-020-00214-4
Patil, A. A., Desai, S. S., Patil, L. N., & Patil, S. A. (2022). Adopting artificial neural network for wear investigation of ball bearing materials under pure sliding condition. Applied Engineering Letters, 7(2), 81–88. https://doi.org/10.18485/aeletters.2022.7.2.5
Roblek, V., Meško, M., Bach, M. P., Thorpe, O., & Šprajc, P. (2020). The interaction between internet, sustainable development, and emergence of society 5.0. Data, 5(3), Article 80. https://doi.org/10.3390/data5030080
Satria, D. (2023). Predicting banking stock prices using RNN, LSTM, and GRU approach. Applied Computer Science, 19(1), 82–94. https://doi.org/10.35784/acs-2023-06
Si, S., Ahlstrom, D., Wei, J., & Cullen, J. (2020). Business, entrepreneurship and innovation toward poverty reduction. Entrepreneurship and Regional Development, 32(1–2), 1–20. https://doi.org/10.1080/08985626.2019.1640485
Siddiqa, A. (2021). Determinants of unemployment in selected developing countries: A panel data analysis. Journal of Economic Impact, 3(1), 19–26. https://doi.org/10.52223/jei3012103
Spada, A., Fiore, M., & Galati, A. (2023). The impact of education and culture on poverty reduction: Evidence from panel data of European countries. Social Indicators Research, 175, 927–940. https://doi.org/10.1007/s11205-023-03155-0
United Nations. (2022). The sustainable development goals report. https://unstats.un.org/sdgs/report/2022/The-Sustainable-Development-Goals-Report-2022.pdf
Uyen, V. T. N., & Thu, P. X. (2023). The multi-criteria decision-making method: Selection of support equipment for classroom instructors. Applied Engineering Letters, 8(4), 148–157. https://doi.org/10.18485/aeletters.2023.8.4.2
Weldearegay, S. K., Tefera, M. M., & Feleke, S. T. (2021). Impact of urban expansion to peri-urban smallholder farmers’ poverty in Tigray, North Ethiopia. Heliyon, 7(6), Article e07303. https://doi.org/10.1016/j.heliyon.2021.e07303
World Bank. (2023). The World Bank in Indonesia. https://www.worldbank.org/en/country/indonesia/overview
Yao, Y., Zhou, J., Sun, Z., Guan, Q., Guo, Z., Xu, Y., Zhang, J., Hong, Y., Cai, Y., & Wang, R. (2023). Estimating China’s poverty reduction efficiency by integrating multi-source geospatial data and deep learning techniques. Geo-Spatial Information Science, 27(4), 1000–1016. https://doi.org/10.1080/10095020.2023.2165975
Yoon, J. (2021). Forecasting of real GDP growth using machine learning models: Gradient boosting and random forest approach. Computational Economics, 57, 247–265. https://doi.org/10.1007/s10614-020-10054-w
Zahra, S. A., Liu, W., & Si, S. (2023). How digital technology promotes entrepreneurship in ecosystems. Technovation, 119, Article 102457. https://doi.org/10.1016/j.technovation.2022.102457
Zhou, Y., & Liu, Y. (2022). The geography of poverty: Review and research prospects. Journal of Rural Studies, 93, 408–416. https://doi.org/10.1016/j.jrurstud.2019.01.008