APPLICATION OF SVM FOR SENTIMENT ANALYSIS REGARDING THE EFFICIENCY OF APBN AND APBD IN 2025 - Ruliana; Nabilah Nursya'bani; Muhammad Kasim Aidid (Program Studi Statistika Fakultas MIPA UNM, 2026)
The policy on expenditure efficiency in the 2025 APBN and APBD has triggered diverse public responses on social media, necessitating sentiment analysis to identify emerging opinion trends. The analysis employs the Support Vector Machine (SVM) method, a margin-based classification algorithm that constructs an optimal separation between classes through the identification of the best hyperplane, where optimality is achieved when the separating margin is maximized. This study aims to identify sentiment patterns and classify public opinion regarding the budget efficiency policy to provide a measurable quantitative overview beyond subjective assessment. Data were collected from the X platform during the period 15 January–25 March 2025 using the keyword “efisiensi anggaran.” The results indicate that negative sentiment dominates at 53%, while positive sentiment accounts for 47%. The SVM model achieved an accuracy of 99%, indicating strong performance in classifying sentiment related to the 2025 budget efficiency policy