Classifying Disadvantaged Districts/Cities in Indonesia: A Support Vector Machine Approach
DOI:
https://doi.org/10.36456/jstat.vol18.no1.a10569Keywords:
Disadvantaged regions, Classification, Kernel, Support Vector Machine (SVM)Abstract
The terms "underdeveloped" and "non-underdeveloped" regions highlight the gap between regions in Indonesia. The government determines the status of underdeveloped regions every five years. Presidential Decree No. 63 of 2020 determines 62 districts/cities in Indonesia as underdeveloped regions. This study aims to classify disadvantaged district status using the Support Vector Machine (SVM) algorithm with three kernel types: linear, polynomial, and Radial Basis Function (RBF). SVM was selected for its effectiveness in handling high-dimensional data and non-linear classification tasks. The dataset, sourced from BPS and JDIH in 2022, comprises 20 variables covering socioeconomic, infrastructure, and public service indicators. The data distribution is imbalanced, with only 62 out of 514 districts labeled as disadvantaged. Optimal parameters were determined experimentally: linear (C = 0.1), polynomial (C = 1, d = 3), and RBF (C = 1, γ = 0.1). Based on evaluation results, the linear kernel achieved the best performance on the given dataset, with an accuracy of 0.94, precision of 0.91, recall of 0.81, and F1-score of 0.85. The model classified 45 districts as disadvantaged and 469 as non-disadvantaged. A total of 29 districts showed discrepancies compared to the official classification. These differences may indicate either changing ground conditions or limitations in policy criteria, highlighting the potential of data-driven approaches to support more targeted and equitable regional development planning.
References
[1] Pemerintah Indonesia. Peraturan Presiden Republik Indonesia No 131 Tahun 2015 Tentang Penetapan Daerah Tertinggal Tahun 2015-2019. Sekretariat Negara. Jakarta. 2015.
[2] Pemerintah Indonesia. Peraturan Presiden Republik Indonesia Nomor 63 Tahun 2020 tentang Penetapan Daerah Tertinggal Tahun 2020–2024. Jakarta: Sekretariat Negara Republik Indonesia. 2020.
[3] E. A. Sari, Meilani T, I. A Shariati, S. Sofyan, R. A. Baihaqi, R. Nooraeni. Klasifikasi Kabupaten Tertinggal Di Kawasan Timur Indonesia Dengan Support Vector Machine. JIKO (Jurnal Informatika dan Komputer). Vol. 3, No. 3, pp 188-195. 2020.
[4] Direktorat Jenderal Pembangunan Desa dan Perdesaan. Indeks Desa Membangun. Jakarta. 2020.
[5] Hamidi, H., Harioso, & Huda. Indeks Desa Membangun. Kementrian Desa, Pembangunan Daerah Tertinggal dan Transmigrasi, Jakarta. 2015.
[6] R. Primartha, Belajar Machine Learning Teori dan Praktik. Bandung : Informatika Bandung, 2018
[7] Mariyam, P. Ana, Ratnawati, D. Eka and Wahyu, Widodo Agis. Klasifikasi Penyakit Gigi dan Mulut Menggunakan Metode Support Vector Machine. Pengembangan Teknologi Informsi dan Ilmu Komputer. Vol. 2, pp. 802-810. 2018.
[8] W. C. Hsu, C. C. Chang and C. J. Lin. A Practical Guide to Support Vector Machine., Taipei: Departement of Computer Science National Taiwan University. 2014.
[9] Domingos, P. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Book, New York. 2015.
[10] Palisoa, N. F., Sinay, L. J., & Matdoan, M. Y. Penerapan Support Vector Machine (SVM) untuk Klasifikasi Kabupaten Tertinggal di Provinsi Maluku. Jurnal Matematika, Statistika dan Terapannya, vol. 02, pp. 79–86. 2023.
[11] Lidya, W., Yozza, H., & Yanuar, F. Klasifikasi Daerah Tertinggal Di Indonesia Menggunakan Metode Naive Bayes Classifier Yanuar. Jurnal Matematika UNAND, IX (1), pp 23–29. 2020.
[12] Wizner, W. Python programming for beginners: 3 books in 1. Springer, London. 2020.
[13] Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. Classification and Regression Trees. Chapman & Hall, London. 2020.
[14] T. Jo, Machine learning foundations: Supervised, unsupervised, and advanced learning. Korea: Springer International Publishing. 2021.
[15] C. C. Aggarwal, Data Classification Algorithms and Applications. New York: CRC Press, 2015.
[16] Yalsavar, M., Karimaghaee, P., Sheikh-Akbari, A., Khooban, M.-H., Dehmeshki, J., Al- Majeed, S. Kernel parameter optimization for support vector machine based on sliding mode control. IEEE Access 10, 17003–17017. 2022.
[17] J. A. K. Suykens, M. Signoretto, and A. Argyriou. Regularization, Optimization, Kernels, and Support Vector Machines. 2014.
[18] I Nyoman Setiawan, Robert Kurniawan, Budi Yuniarto, Rezzy Eko Caraka, Bens Pardamean, Parameter Optimization of Support Vector Regression Using Harris Hawks Optimization, Procedia Computer Science, vol. 179, pp. 17-24. 2021.
[19] Wiryawanto T. M. P., Hawani Z., and Ramadhani M. A. Comparison of Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) Methods for Predicting Air Quality Using Python and KNIME”, J Statistika, vol. 16, no. 1, pp. 384–394, Jul. 2023
[20] M. Athoillah, E. Purnaningrum, and R. K. Putri, “Modified Multi-Kernel Support Vector Machine for Mask Detection,” CommIT (Communication and Information Technology) Journal, vol. 16, no. 2, pp. 159–166, 2022.
[21] Cambell, C., & Ying, Y. Learning with Support Vector Machines : Synthesis Lecturers on Artificial Intelligence and Machine Learning. Morgan & Claypool. 2011.
[22] Al-Azies, H., & Anuraga, G. Klasifikasi Daerah Tertinggal di Indonesia Menggunakan Algoritma SVM dan k-NN. Jurnal Ilmu Dasar, 22(1), 31–38. 2021.
[23] James, G., Witten, D., Hastie, T., & Tibshirani, R. An Introduction to Statistical Learning. Springer, London. 2017.
[24] Roshafara F. Forecasting Average Rice Prices at Milling Level According to Quality Using Support Vector Regression ”, J Statistika, vol. 17, no. 1, pp. 664–671, Jul. 2024.
