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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Sabara, Gally
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Computer Science & IT

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Development of a Raspberry Pi 4-Powered Internet of Things System for Acne-Prone Skin Health Monitoring Kurniawan, Aprila; Sari, Dewi Permata; Wijanarko, Yudi; Sabara, Gally
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.36997

Abstract

This research developed an Internet of Things (IoT)-based facial skin health monitoring system, with a focus on acne-prone skin. Facial skin is categorized into three main types: normal, oily, and dry, as well as four types of acne: blackheads, papules, pustules, and nodules. The system is designed to enhance the accuracy of skin condition monitoring through facial image analysis, utilizing a dataset of 4,092 images. The high number of acne cases, especially in 12-24 year olds with 40-50 million cases in the United States, is the background of this research. Conventional skin analyzers are considered less capable of providing accurate quantitative data. Therefore, a Smart Skin Analyzer Detector was developed that uses a Raspberry Pi as a data processor. Images are taken through a webcam, analyzed, and then the results are sent to the cloud. The system is also integrated with Telegram to provide users with real-time notifications regarding their skin type and acne condition. This approach enables more effective, faster, and more affordable skin monitoring. The results demonstrate that IoT technology has significant potential in enhancing personalized and sustainable skin care.
Acne Skin Detection System Using You Only Look Once (YOLOV8) Based on Artificial Intelligence Sabara, Gally; Abdurrahman, Abdurrahman; Sari, Dewi Permata; Kurniawan, Aprila
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37217

Abstract

Acne is one of the most common skin problems among teenagers and young adults, and early detection is essential to prevent progression and long-term skin damage. This study aims to develop a real-time acne detection system utilizing the YOLOv8 deep learning algorithm, integrated with a Raspberry Pi and webcam, and supported by Telegram-based notifications for user monitoring. The dataset comprises 4,092 annotated facial images representing three types of acne: papule, pustule, and nodule. Model training was conducted in Google Colab with appropriate hyperparameter adjustments. The evaluation results show that the model performs well in detecting papule and pustule acne types, with correct predictions of 258 and 222 samples, respectively, in the confusion matrix, although misclassification remains high for comedones and background classes. The Precision–Confidence Curve indicates that the model achieves a perfect precision score of 1.00 at a confidence threshold of 0.929, while the F1–Confidence Curve shows an optimal F1-score of 0.73 at a confidence level of 0.39, demonstrating the best balance between precision and recall. Real-time testing further confirms that the system can detect papules with high confidence (88%), but confidence levels for comedones (31%) and nodules (29%) remain low due to visual similarity and non-ideal lighting conditions. Overall, the results indicate that the YOLOv8-based system is capable of performing real-time acne detection with acceptable accuracy. However, further improvements in dataset diversity and annotation quality are required to enhance performance, particularly for comedone detection.
Co-Authors Abdurahman Abdurrahman Dewi Permata Sari Kurniawan, Aprila Yudi Wijanarko, Yudi