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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi
Aritonang, Riki Martua
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Electrical & Electronics Engineering

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Human Skin Disease Detection using Convolutional Neural Network Method with Hyperparameter Tuning to Determine the Best Parameter Combination Aritonang, Riki Martua; Florestiyanto, Mangaras Yanu; Yuwono, Bambang
Telematika Vol 20 No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.9161

Abstract

Purpose: Obtaining the best hyperparameter combination for optimization of the Convolutional Neural Network method, for classifying skin diseases.Design/methodology/approach: Using the CNN method with hyperparameter tuning in determining the best hyperparameter combination. System development is performed with the Python programming language.Findings/result: The best combination of hyperparameter tuning results is RMSprop optimizer, APL dropout value is 0.05, dropout is 0.5 , dense layer is 64, and produces an accuracy of 97,81%.Originality/value/state of the art: This study has differences in terms of the types of skin diseases classified, the architecture of the CNN model, the hyperparameters tested and the combination results obtained compared to previous studies.
Co-Authors Bambang Yuwono Mangaras Yanu F