Optimization of Briquette Classification Using Deep Learning

Abstract

Indonesia needs energy to meet its needs. Energy sourced from waste is called biomass briquettes. The manufacture of briquettes is still done in a traditional way, so there are product quality problems. A computational approach has been taken to base the quality of the product on certain characteristics so that the types of briquettes that are made can be classified or sorted. The research objective of this work is to determine the quality of briquettes. The approach uses deep learning methods and convolutional neural network (CNN) architecture. Classification is based on good and bad briquette products, and testing is based on the level of performance accuracy. The dataset formed consists of 5,280 images. As training data, 85% of data is used and 15% is used as test data using 300 epoch parameters, 32 batch sizes, and learning speed up to 0.001. The results of testing and evaluating the performance of briquette products with the general CNN architecture have a level of accuracy that is not optimal. The optimization results of testing the MobileNetV2 architecture with a ratio of 70:30 obtains predictions with 0.99 as the highest accuracy value and 0.73 as the lowest. The average predicted value of the model is 0.95. The computational approach is able to provide traditional communities with solutions for the process of making briquettes.