Implementasi Metode Adaptive Neuro-Fuzzy Inference System (ANFIS) terhadap Prediksi Tingkat Kemiskinan di Kabupaten/Kota Jawa Timur - Tsalsya Ni'matul Aulia; Evawati Alisah; Erna Herawati (Universitas Islam Negeri Maulana Malik Ibrahim Malang, 2026)
This study aims to evaluate the effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting poverty levels in the Regencies/Cities of East Java Province. ANFIS is a hybrid method that combines fuzzy logic and artificial neural networks, enabling it to handle complex socio-economic data with inherent uncertainty. The model uses the open unemployment rate, population growth rate, and school participation rate for ages 16–18, while the target variable is the poverty level. The data were obtained from the central statistics agency (BPS) and cover the years 2022, 2023, and 2024. Modeling was carried out using a time-based split approach, in which 2022 data were used as training data to predict the 2023 poverty level, and 2023 data were used as testing data to predict the 2024 poverty level. The training process employed a hybrid algorithm combining Least Squares Estimation (LSE) and backpropagation. Evaluation was conducted using Mean Squared Error (MSE) to assess the model’s accuracy. The results indicate that ANFIS is capable of producing accurate predictions of poverty levels, with an MSE value of 3.98 × 10⁻²⁴ in the testing phase. Therefore, ANFIS is considered effective in analyzing and predicting socio-economic phenomena at the regional level.