Prediction of Poverty Levels in Indonesia Using the Tsukamoto Fuzzy Logic Method - Aklima Laduna Ramadya; Tiara Dwi Lestari Purba; Ega Wahyu Andani; Baginda Faustine Sinaga; Victor Asido Elyakim P (Yayasan Literasi Sains Indonesia, 2025)
Poverty remains a fundamental issue and a primary focus in Indonesia's development. Conventional analysis often fails to provide an accurate picture due to the complexity of its underlying factors. This study aims to build a prediction model for poverty levels in Indonesia using the Tsukamoto fuzzy logic method, based on macroeconomic data from the Central Statistics Agency (BPS) for the years 2022 to 2024. Input variables include inflation rates, unemployment, and economic growth, with the output being the predicted poverty level in percentage. The fuzzy inference process involves fuzzification, rule base formation, fuzzy logic inference, and defuzzification. Data on the percentage of the poor population from BPS shows a decrease from 9.57% in 2022 to 9.27% in 2024. However, significant regional disparities and economic vulnerabilities persist due to global factors like inflation. Fuzzy logic, especially the Tsukamoto fuzzy method, is an adaptive approach capable of handling uncertainty and linguistic variables, while producing numerical outputs. The research results indicate that the fuzzy Tsukamoto model successfully predicts poverty levels with high accuracy, showing an average difference of less than 0.1% from actual data. This finding suggests that the Tsukamoto fuzzy method can be an effective predictive alternative in addressing socio-economic data uncertainties and supporting the formulation of more targeted policies.