Analisis Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia Kabupaten/Kota di Indonesia Menggunakan Algoritma GUIDE
DOI:
https://doi.org/10.17509/jcp2k776Keywords:
GUIDE, Human Development Index, Regression tree, Social welfareAbstract
Human Development Index (HDI) is an important indicator to measure the quality of human development across regions. This study aims to analyze factors influencing the Human Development Index of regencies/cities in Indonesia using the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm. The data used consists of socio-economic variables related to education, health, and economic conditions. The GUIDE algorithm is applied to identify significant predictor variables and interaction patterns that affect HDI values without bias in variable selection. The results show that several key factors, such as life expectancy, mean years of schooling, and expenditure per capita, play a dominant role in determining HDI across regions. The resulting regression tree provides an interpretable structure that illustrates the relationship between explanatory variables and HDI. This study demonstrates that the GUIDE algorithm is effective in modeling HDI data and can be used as an alternative analytical method for regional development analysis in Indonesia.
References
Alviani, L. O. (2021). Penggunaan regresi data panel pada analisis Indeks Pembangunan Manusia. Jurnal Riset Matematika, 1(2), 99-108.
Isnani, Rahmi., I. H. G., and Asdi, Y. (2019). Penerapan metode pohon regresi stepwise linear dengan algoritma guide dalam menanalisis pengaruh kinerja program gizi terhadap prevelensi underweight di Indonesia. Jurnal Matematika UNAND, 8(1), 110-119.
Jena, M. and Dehuri, S. 2020. Decision tree for classification and regression: A state-of-the art review. Informatica, 44(4), 405-420.
Liu, Y., Ma, X., Zhang, D., Geng, L., Wang, X., Zheng, W., and Chen, M. -H. 2019. Look before you leap: Systematic evaluation of tree-based statistical methods in subgroup identification. Journal of Biopharmaceutical Statistics, 29(6), 1082-1102.
Loh, W. -Y., Cao, L., and Zhou, P. (2019a). Subgroup identification for precision medicine: A comparative review of 13 methods. WIREs Data Mining Knowledge Discovery, 9(5), e1326.
Loh, W. -Y., Eltinge, J., Cho, M. J., and Li, Y. (2019b). Classification and regression trees and forests for incomplete data from sample surveys. Statistica Sinica, 29(1), 431-453.
Loh, W. -Y., Man, M., and Wang, S. (2019c). Subgroup from regression trees with adjustment for prognostic effects and postselection inference. Statistics in Medicine, 38(4), 545-557.
Loh, W. -Y., Zhang, Q., Zhang, W., and Zhou, P. (2020). Missing data, imputation and regression trees. Statistica Sinica, 30(4), 1697-1722.
Mu’minah, S. and Tjenreng, M. B. Z. (2025). Desentralisasi dan ketimpangan pembangunan antar daerah. Scientific Journal of Reflection: Economic, Accounting, Management and Business, 8(1), 342-351.
Satria, T. A. I, Imro’ah, N., and Huda, N. M. (2023). Penerapan model log linier dalam menganalisis tabel kontingensi dua arah. Buletin Ilmiah Matematika, Statistika dan Terapannya (BIMASTER), 12(6), 545-552.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Syifa Maulia, Yasmin Erika Faridhan, Maya Widyastiti (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
