Pemodelan dan Prediksi Tingkat Kemiskinan Provinsi Sumatera Barat Menggunakan Support Vector Machine - Putri, Melani Septina; Junaidi, Satrio; Mardiyah, Ainil (Universitas Muhammadiyah Riau, 2026)
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.