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All Journal JOURNAL OF ICT APLICATIONS AND SYSTEM
Aldi Antoni
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : JOURNAL OF ICT APLICATIONS AND SYSTEM

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Systematic Literature Review: Advancements in Skin Cancer Diagnosis Using Convolutional Neural Networks and Dermatoscopic Imaging muhajirin, Ahmad; Achmad Alwi Hasibuan; Aldi Antoni; Ali Amran NST; Ns. Romy Wahyuny
Journal of ICT Applications System Vol 3 No 2 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v3i2.390

Abstract

Skin cancer is one of the most prevalent types of cancer worldwide, requiring early detection for effective treatment and improved patient outcomes. Traditional diagnostic methods, such as biopsies, are time-consuming, costly, and uncomfortable for patients. In response to these challenges, this study systematically reviews the application of Convolutional Neural Networks (CNNs) in automated skin cancer diagnosis using dermatoscopic images. CNNs have demonstrated remarkable performance in image processing tasks due to their ability to extract complex features and ensure high classification accuracy. This review analyzes various CNN architectures, such as GoogLeNet, ResNet, and YOLOv8, in terms of their effectiveness in distinguishing between benign and malignant skin lesions. Results from existing literature indicate that CNN-based systems achieve an accuracy of up to 97.73%, making them a promising solution for automated diagnostic tools. The findings emphasize the importance of data augmentation, parameter optimization, and diverse datasets to improve model generalizability. This study concludes that integrating CNN-based diagnostic systems with clinical workflows has significant potential to enhance early detection, optimize medical resources, and raise public awareness of skin cancer prevention
Advanced Classification of Oil Palm Fruit Ripeness Deep Learning for Enhanced Agricultural Efficiency Hasibuan, Achmad Alwi; Ali Amran Nst; Aldi Antoni; Ray Handika; Budi Yanto; Akhmad Zulkifli
Journal of ICT Applications System Vol 3 No 2 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v3i2.395

Abstract

The classification of oil palm fruit ripeness is a critical factor in optimizing palm oil production. Traditional methods of ripeness assessment, based on the percentage of detached fruits and changes in skin color, are prone to human error due to subjective judgment. This study proposes an advanced approach utilizing deep learning with the ResNet50 model to classify oil palm fruit ripeness into four levels: unripe, under-ripe, ripe, and overripe. The research evaluates the model's performance under various data allocations, optimizers, and learning rates while incorporating data augmentation techniques to enhance accuracy. Experimental results indicate the optimal configuration includes a 90/10 data split, Adam optimizer, and a learning rate of 0.0001, achieving precision of 96%, recall of 98%, F1 score of 97%, and accuracy of 97%. These findings highlight the potential of ResNet50 in delivering reliable, real-time classification for agricultural applications, providing a practical solution for farmers and industries. The study concludes that large and diverse training datasets are essential for achieving robust classification results
Co-Authors Achmad Alwi Hasibuan Ahmad Muhajirin Akhmad Zulkifli Ali Amran NST Budi Yanto Hasibuan, Achmad Alwi Ns. Romy Wahyuny Ray Handika