Pemodelan dan Prediksi Tingkat Kemiskinan Provinsi Sumatera Barat Menggunakan Support Vector Machine
DOI:
https://doi.org/10.37859/coscitech.v7i1.11207
Abstract
This research is motivated by the problem of poverty distribution in West Sumatra Province, which still varies between regions. The objectives of this study are to build a prediction model using the Support Vector Machine (SVM) algorithm, evaluate the model's performance, and implement the prediction results in the form of an interactive dashboard to support local government decision-making. The study uses secondary data from the Central Statistics Agency (BPS) of West Sumatra Province for the period 2015–2024, covering 19 districts/cities. The dependent variable is the percentage of poor people (P0), while the independent variables consist of seven socio-economic indicators. The method used refers to the CRISP-DM stages. In the data preparation stage, missing values are handled using median imputation, outliers are handled using winsorizing, standardization is carried out using Z-Score, and the addition of a one-period lag variable (P0_lag1). The data is divided into training data (2015–2022) and test data (2023–2024), with parameter optimization using GridSearchCV and TimeSeriesSplit. The results showed that the Support Vector Regression (SVR) model with a radial basis function (RBF) kernel provided the best performance with parameters C=1000, epsilon=0.05, and gamma=0.001. This model produced an MAE value of 0.32, RMSE of 0.36, and R² of 0.98. The implementation of the prediction results in the Streamlit dashboard for the 2025–2030 period showed a downward trend in poverty levels in most regions. This model is considered effective as a basis for planning and evaluating data-based poverty alleviation policies.
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References
H. Mukhtar, R. Gunawan, A. Hariyanto, and W. Mulyana, “Peramalan Kedatangan Wisatawan ke Suatu Negara Menggunakan Metode Support Vector,” J. Comput. Sci. Inf. Technol., vol. 3, no. 3, pp. 274–282, 2022, doi: 10.37859/coscitech.v3i3.4211.
Syaharuddin, E. Pujiana, I. P. Sari, V. M. Mardika, and M. Putri, “Analisis Algoritma Back Propagation Dalam Prediksi Angka Kemiskinan di Indonesia,” Pendek. J. Pendidik. Berkarakter, vol. 3, no. 1, pp. 11–17, 2020.
E. Retnoningsih and R. Pramudita, “Mengenal Machine Learning Dengan Teknik Supervised dan Unsupervised Learning Menggunakan Python,” Bina Insa. ICT J., vol. 7, no. 2, pp. 156–165, 2020.
M. R. Qisthiano, “Klasifikasi Terhadap Prediksi Kelulusan Mahasiswa Dengan Menggunakan Metode Support Vector Machine (SVM),” in Proceeding STEKOM 2022, 2022, pp. 204–208.
Herlangga, M. W. Pangestika, and S. P. A. Alkadri, “Prediksi Harga Mobil Bekas Menggunakan Algoritma Support Vector Regression,” J. Comput. Sci. Inf. Technol., vol. 6, no. 3, pp. 399–404, 2025, doi: 10.37859/coscitech.v6i3.10545.
D. Kurniawan, S. Apriani, and Pujianto, “Prediksi Angka Kemiskinan Di Kabupaten Ogan Komering Ulu Menggunakan Metode Data Mining Berbasis SVM, Neural Network & SGD,” Semin. Nas. Teknol. Inf. dan Bisnis, pp. 133–138, 2025.
A. Karim, “Perbandingan Prediksi Kemiskinan di Indonesia Menggunakan Support Vector Machine ( SVM ) dengan Regresi Linear,” J. Sains Mat. dan Stat., vol. 6, no. 1, pp. 107–112, 2020, doi: 10.24014/jsms.v6i1.9259.
Fauziah, M. A. Tiro, and Ruliana, “Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua,” J. Math. Appl. Sci., vol. 2, no. 2, pp. 83–91, 2022, doi: 10.35877/mathscience741.
F. H. Zulfallah, “Implementasi Algoritma KNN Dalam Mengukur Ketepatan Kelulusan Mahasiswa UIN Syarif Hidayatullah Jakarta,” Universitas Islam Negeri Syarif Hidayatullah, 2022.
R. Efendi, A. Junaidi, and A. M. Rizki, “Penentuan Pusat Klaster Secara Otomatis Pada Algoritma Density Peaks Clustering Berbasis Metode Inter Quartile Range,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4997.
A. E. Putri, Y. MZ, and J. E. Bororing, “Implementasi K-Means Clustering dan Model CRISP-DM Untuk Pengelompokkan Daerah Rawan Tindak Pidana Narkoba Di DIY,” J. Inform. Teknol. dan Sains, vol. 7, no. 2, pp. 606–615, 2025.
Hendriyana, I. M. K. Karo, and S. Dewi, “Analisis Perbandingan Algoritma Support Vector Machine, Naive Bayes, dan Regresi Logistik Untuk Memprediksi Donor Darah,” J. Teknol. Terpadu, vol. 8, no. 2, pp. 121–126, 2022, [Online]. Available: https://journal.nurulfikri.ac.id/index.php/jtt
E. Aiziyah, A. H. Lukmana, and A. M. Abadi, “Prediksi Rate of Penetration pada Pengeboran Minyak Bumi dengan Elman Recurrent Neural Network,” Multimed. Artif. Intell. Netw. Database J., vol. 10, no. 2, pp. 145–161, 2025, doi: 10.26760/mindjournal.v10i2.145-161 |.
C. M. Sitorus, A. Rizal, and M. Jajuli, “Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 254–265, 2020, doi: 10.28932/jutisi.v6i2.2672.
E. Rahmawati, “Penerapan Metode Support Vector Regression (SVR) Menggunakan Kernel Linear, Polinomial, dan Radial Dengan Grid Search Optimization,” Universitas Lampung, 2023.
Z. Maisat, E. Darmawan, and A. Fauzan, “Implementasi Optimasi Hyperparameter GridSearchCV Pada Sistem Prediksi Serangan Jantung Menggunakan SVM Implementation of GridSearchCV Hyperparameter Optimization in Heart Attack Prediction System Using SVM,” J. Ilm. Sist. Inf., vol. 13, no. 1, pp. 8–15, 2023, doi: 10.26594/teknologi.v13i1.3098.
W. Wulandari, “Metode SVR Untuk Memprediksi Jumlah Pemakaian Air PDAM (Studi Kasus : Sub Unit Muntilan),” Universitas Muhammadiyah Magelang, 2025.
