Klasifikasi tingkat kematangan dan kemasakan buah durian dengan model neural network

This study was aimed to develop the model to predict the maturity and ripeness of durian based on its physical and chemical characteristics using neural network. The physicochemical and acoustic characteristics measurement was fed into the model as the inputs, which provided the levels of maturity and ripeness as the output of the model. The results suggested that the physico-chemical properties and the acoustic characteristic decreased with the increase of both maturity and ripeness level of durian. The total solid soluble, the water content and the total sugar increased according to the fruits maturity. The total acids increased in the beginning of durian maturing proces and then decreased when the maturity and ripeness level reached the mature-over ripened stage. Data training were done by model of neural network: model 4 output, with various node in the hidden layer 4, 6, 8 and 10 nodes. The results recommended that the best model to be applied was model 4 output with 4 nodes in the hidden layer and iteration 1000 and 5000 with the model accuration 87.5% -100%. Keywords: neural network, durian, maturity and ripenes

Klasifikasi Tingkat Kematangan dan Kemasakan Buah Durian dengan Model Neural Network

This study was aimed to develop the model to predict the maturity and ripeness of durian based on its physical and chemical characteristics using neural network. The physicochemical and acoustic characteristics measurement was fed into the model as the inputs, which provided the levels of maturity and ripeness as the output of the model. The results suggested that the physico-chemical properties and the acoustic charcteristic decreased with the increase of both maturity and ripeness level of durian. The total solid soluble, the water content and the total sugar increased according to the fruits maturity. The total acids increased in the beginning of durian maturing process and then decreased when the maturity and ripeness level reached the mature-over ripened stage. Data training were done by model of neural network: model 4 output, with various node in the hidden layer 4, 6, 8 and 10 nodes. The results recommended that the best model to be applied was model 4 output with 4 nodes in the hidden layer and iteration 1000 and 5000 with the model accuration 87.5 % - 100%