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All Journal Jurnal Teknik Informatika (JUTIF) Decode: Jurnal Pendidikan Teknologi Informasi Mohuyula : Jurnal Pengabdian Kepada Masyarakat
Habie, Khairul Fathan
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Journal : Jurnal Teknik Informatika (JUTIF)

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Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images Habie, Khairul Fathan; Murinto, Murinto; Sunardi, Sunardi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4685

Abstract

Automatic detection of skin diseases using digital images is a growing field in the application of deep learning in the medical world, especially to help the early diagnosis process. One of the most widely used models is MobileNetV1 because it is lightweight and efficient in image processing. However, the performance of the model is greatly affected by the training configuration, including the type of optimizer used. This study aims to compare the effectiveness of six types of optimizers, namely SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, and Nadam in training MobileNetV1 models for human skin disease image classification. The model was trained on annotated skin image dataset with predetermined training parameters: batch size 32, learning rate of 0.0001, and 10 epochs. Performance evaluation was performed using accuracy metrics. The results obtained demonstrate that RMSprop performs best, with 99.10% accuracy, 99.14% precision, 99.10% recall, and a 99.10% F1-score. Adadelta showed the lowest performance consistently, with only 22.22% accuracy, 20.34% precision, 22.22% recall, and 18.42% F1-score. This finding confirms that the type of optimizer affects the effectiveness of model training, especially in medical image classification tasks. This research provides empirical insights that are useful in selecting the optimal optimizer for MobileNetV1 model implementation in the healthcare domain.
Co-Authors Arfiani Nur Khusna Herman Yuliansyah, Herman Imam Riadi Murinto Murinto Putro, Aldibangun Pidekso Sunardi Sunardi