Classification of Poverty Reduction Program Recipients with Neural Network Algorithm in East Kotawaringin Communities - Nurahman; Seabtian, Dwi Tjahjo (Politeknik Dharma Patria Kebumen, 2021)
Indonesia has a poverty rate of 24.79 million. Kotawaringin Timur is inhabited by 27.4 thousand people with an income of less than Rp. 416,777/month. The provision must be right on target, and recipients of assistance must use the assistance following the rules determined by the government. This research is to formulate a conceptual model of the Neural Network Algorithm structure that can be used to predict the use of assistance funds. This research applies the Knowledge Discovery Data methodology with Neural Network Algorithm for classification. The research has shown that the application of the Neural Network Algorithm with feature selection can improve performance with values AUC=0.974, CA=0.977, F1=0.977, Precision=0.977, Recall=0.977. The level of performance value for accuracy of Neural Network Algorithm in classifying is the excellent classification category. The recommended Neural Network parameter models are Neurons in hidden layers 100, Activation ReLu, Solver Adam, Regularization, Î± = 0.0001, and a Maximal number of iterations 200.