Implementasi Sistem Klasifikasi Sampah Organik dan Anorganik dengan Metode Jaringan Saraf Tiruan Backpropagation - Fungki Pandu Fantara; Dahnial Syauqy; Gembong Edhi Setyawan (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Organic and inorganic waste have different decomposition time. Organic waste has longer decomposition time than inorganic waste. So, organic and inorganic waste have a different ways of handling recycling. Sorting garbage before being accommodated to landfills (TPA) is very important to reduce the amount of garbage dump that keeps increasing every year. This research examines the implementation of classification system of organic and inorganic waste by using artificial neural network method backpropagation. Artificial neural network architecture applied is 3 neurons in the input layer, 1 layer hidden with 4 neurons, and 1 neurons on the output layer. Data training is not performed on built systems but on additional systems to search for weights, so the built system only predicts data directly from sensor readings. Based on this research, the system can be built using 3 sensors which are used as input data, they are: Light Dependent Resistor (LDR), inductive proximity, and capacitive proximity and a servo output which can open the lid automatically based on the classification result done by system. The system has 90% accuracy with the performance of each prediction takes 42.9ms average time.