Pemilahan Sampah Menggunakan Model Klasifikasi Support Vector Machine Gabungan dengan Convolutional Neural Network
DOI:
https://doi.org/10.30865/jurikom.v10i1.5468Keywords:
CNN, Feature Extraction, Machine Learning, SVM, Waste ManagementAbstract
Waste sorting is a vital process in waste management. The problem with the waste sorting process is that humans feel uncomfortable with the smell of garbage for too long. The problem can be solved by creating a machine learning system to identify the waste type. The purpose of this research is to solve waste management problems using machine learning using the most accurate classification model. The types of wastein this research are limited to only two types: organic and inorganic. Data was collected and revised from the Kaggle dataset. Data were imported into the system using Python. Data was trained and used for classifying the waste based on the image source. Waste images will be determined in their category using the Support Vector Machine model with feature extraction using the Convolution layer. The system successfully performs waste classification using the Support Vector Machine model combined with the Convolutional Neural Network with an accuracy of 96,16% and a loss of 7,25% on the overall category
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