Cleanliness Image Classification of Packaged Drinking Water Using Convolutional Neural Network (CNN)
Keywords:
Bottled Drinking Water, CNN, OptimizerAbstract
Water is a natural resource that is very important for humans to consume in everyday life, such as Bottled Drinking Water (AMDK). The requirements for water suitable for consumption must be clean or not cloudy, odorless, tasteless and of course also meet the requirements as written in the regulations of the Indonesian Minister of Industry as criteria for quality bottled water according to SNI (Indonesian National Standard). The large number of types of water can be a problem because sometimes there are people who are not suitable for drinking bottled water with certain brands. This study aims to determine the quality level of bottled water in terms of the quality of the water turbidity values of various types of bottled water. This study uses image processing technology with the Convolutional Neural Network (CNN) method to classify AMDK based on image. The image taken only focuses on 5 types of water, namely Aqua (138 ppm), Vit (181 ppm) , Nestle pure life (175 ppm), Crystalin (142 ppm) and Le mineral (156 ppm). In this study, 500 data were used and three stages of testing were carried out on the CNN model, namely testing the epoch, testing the learning rate, and testing the optimizer. So that the best results were obtained with an accuracy of 90% at epoch 30, a learning rate of 0.01, on the RMSprop optimizer with a test time of 8 minute 58 seconds.
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