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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Journal of Advanced Health Informatics Research
Ari Peryanto
Universitas Madani
Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health Other

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Expert System For Diagnosis Of Gerd Disease Forward Chaining Methods Fadhilah Dhinur Aini; Ari Peryanto
Journal of Advanced Health Informatics Research Vol. 3 No. 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v3i1.332

Abstract

This study presents the development of an expert system for diagnosing gastric diseases using the forward chaining method. The system is designed to assist patients in identifying possible conditions such as Gastroesophageal Reflux Disease (GERD), dyspepsia, and peptic ulcer based on reported symptoms through a web-based interface. The diagnosis process relies on a rule-based knowledge system that maps symptoms to disease categories and provides preliminary results along with simple treatment recommendations. The implementation demonstrates that the system can facilitate early screening and improve patient awareness. Nonetheless, it remains limited to common gastric diseases and depends on subjective symptom reporting. Accordingly, the system is intended as a supporting tool for early detection and patient guidance, rather than a substitute for clinical examination
Pemanfaatan Deep Learning untuk Klasifikasi Citra Penyakit Kulit Menggunakan MobileNetV3 Ari Peryanto; Dwi Susanto
Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Vol 16, No 2 (2025): Desember
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jsit.v16i2.4480

Abstract

Skin diseases are a global health problem affecting more than 900 million people annually, with a prevalence of 15–25% in primary healthcare visits in Indonesia. Limited access to dermatologists and the concentration of 70% of specialists in urban areas often lead to delayed diagnoses. To address this issue, this study develops a skin disease detection system based on deep learning using the MobileNetV3 architecture, focusing on computational efficiency on mobile devices and improved accuracy through knowledge distillation techniques. The dataset consists of four categories of skin diseases collected independently, with the model trained using transfer learning and fine-tuning, and further optimized with knowledge distillation to enhance performance without increasing complexity. Evaluation results show excellent performance with an overall accuracy of 97%, surpassing the initial target of >85%. The average precision, recall, and f1-score reach 0.97, demonstrating consistent performance across all categories. In particular, the ringworm class achieved 100% recall, while other classes reached values above 93%. The research outputs include a well trained MobileNetV3 model for high accuracy skin disease classification and a scientific publication on model optimization. This system is expected to provide an affordable and accessible diagnostic support solution, particularly for healthcare workers and communities in underserved areas.
Co-Authors Dwi Susanto Fadhilah Dhinur Aini