Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Ramadhani, Rima Dias and Thohari, Afandi Nur Aziz and Kartiko, Condro and Junaidi, Apri and Laksana, Tri Ginanjar and Nugraha, Novanda Alim Setya (2021) Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah. JURNAL RESTI. ISSN 2580-0760

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Abstract

Waste is good/materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian goverment in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which has 64 million tons. Waste is differentiaied based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method. which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91,2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67,6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stide. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

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