MODEL NEURAL NETWORK UNTUK IDENTIFIKASI VARIABEL KEMISKINAN RUMAH TANGGA KECAMATAN ARANIO - Erwin Arry Kusuma (STMIK Banjarbaru, 2018)
Abstrak Kemiskinan sering kali dipahami sebagai gejala rendahnya tingkat kesejahteraan. Berbagai penelitian yang telah dilakukan mengenai kemiskinan dan banyak sekali faktor yang mempengaruhi kemiskinan. Oleh karena itu, dilakukan penelitian mengenai klasifikasi status kesejahteraan rumah tangga di Kecamatan Aranio Kabupaten Banjar, Kalimantan Selatan. Penelitian ini dilakukan untuk mengkaji hasil akurasi berbagai model Neural Network dalam mengenai klasifikasi status kesejahteraan rumah tangga di Kecamatan Martapura tersebut. Dari berbagai hasil pengujian, didapatkan kesimpulan bahwa model Neural Network yaitu Training Cycle = 200, Learning Rate = 0,1, Momentum = 0,2, Number of Validation=6, dan Sampling Type = Strarified, menghasilkan tingkat akurasi lebih baik daripada model lainnya. Model yang didapatkan tersebut menghasilkan nilai Accuracy=89,97% +/- 3,46%. Kata kunci: status kesejahteraan, tingkat kesejahteraan, Neural Network Abstrak Poverty is often understood as a symptom of low levels of well-being. Various studies have been conducted on poverty and many factors that influence poverty. Therefore, a study was conducted on the classification of household welfare status in Aranio District, Banjar Regency, South Kalimantan. This research was conducted to examine the results of the accuracy of various Neural Network models in the classification of household welfare status in the Martapura District. From various test results, it was concluded that the Neural Network model namely Training Cycle = 200, Learning Rate = 0.1, Momentum = 0.2, Number of Validation = 6, and Sampling Type = Strarified, resulting in a better level of accuracy than other models . The model obtained produces Accuracy value = 89.97% +/- 3.46%. Keywords: welfare status, welfare level, Neural Network