Implementation of the Support Vector Machine Algorithm for Predicting Voter Participation Based on Village Development Index Data in the Tapal Kuda Region
Penerapan Algoritma Support Vector Machine dalam Memprediksi Tingkat Partisipasi Pemilu Berdasarkan Data Indeks Desa Membangun pada Wilayah Tapal Kuda
DOI:
https://doi.org/10.21070/ups.9670Keywords:
Support Vector Machine, Machine Learning, Village Development Index, Voter ParticipationAbstract
The level of voter participation reflects not only the strength of democracy but also the social awareness of the community. In East Java’s Tapal Kuda region, this participation varies significantly between villages, influenced by diverse development conditions. This study explores the use of the Village Development Index (Indeks Desa Membangun/IDM) to predict voter participation through the Support Vector Machine (SVM) algorithm. Using the 2024 IDM dataset consisting of thousands of village-level records, several indicators were selected to represent social, economic, and infrastructural aspects. The SVM model produced an accuracy of 76%, with precision and recall values of 0.75 and 0.76 respectively. These results indicate that the model is more responsive in detecting high participation patterns, suggesting that village development and access to basic services contribute significantly to civic engagement. Therefore, the application of SVM on IDM data can be utilized as a data-driven approach to support strategies aimed.
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