Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Rima Dias Ramadhani; Afandi  Nur Aziz Thohari; Condro  Kartiko; Apri  Junaidi; Tri  Ginanjar Laksana; Novanda  Alim Setya Nugraha

doi:10.29207/resti.v5i2.2754

Rima Dias Ramadhani Institut Teknologi Telkom Purwokerto
Afandi Nur Aziz Thohari Politeknik Negeri Semarang
Condro Kartiko Institut Teknologi Telkom Purwokerto
Apri Junaidi Institut Teknologi Telkom Purwokerto
Tri Ginanjar Laksana Institut Teknologi Telkom Purwokerto
Novanda Alim Setya Nugraha Institut Teknologi Telkom Purwokerto

DOI: https://doi.org/10.29207/resti.v5i2.2754

Keywords: CNN, dropout, model, padding, waste, stride

Abstract

Waste is goods / 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 government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated 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 inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization 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 stride. 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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