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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Format : Jurnal Imiah Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-SAKTI (Jurnal Sains Komputer dan Informatika) JIKA (Jurnal Informatika) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Pengabdian Masyarakat Bangsa Journal of Advanced Health Informatics Research
Peryanto, Ari
Universitas Ahmad Dahlan
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Journal : Journal of Advanced Health Informatics Research

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Classification of Skin Disease Images Using K-Nearest Neighbour (KNN) Ari Peryanto; Susanto, Dwi; Jihad, Bagus Hayatul
Journal of Advanced Health Informatics Research Vol. 2 No. 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i3.300

Abstract

The skin is the outermost part of the human body that is often exposed to the environment, so it is easy to experience disease disorders. Some of the skin diseases that are often contracted in humans are ulcers, herpes, and warts. Untreated skin diseases will be very annoying because of the sensation of itching so it can cause irritation and inflammation. The ability to classify skin diseases using technology is one solution. This study uses the K-Nearest Neighbour (KNN) method to detect images of skin diseases. KNN is one of the machine learning methods with a calculation method based on the proximity of k. KNN was chosen because it is fast and has high-accuracy results. The results of the research that has been carried out have obtained results of accuracy of 63%, precision of 63%, recall of 63%, and F1 Score of 63%. From the results of the study, it can be concluded that disease detection using KNN has been successfully applied and can be used in classification.
Expert System For Diagnosis Of Gerd Disease Forward Chaining Methods Dhinur Aini, Fadhilah; Peryanto, Ari
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
Co-Authors Achmad Nugrahantoro Aditya, Anggie Yudistira Aini, Fadhilah Dhinur Aji Nugroho Anton Yudhana Betrand Erlangga Dhinur Aini, Fadhilah Dwi Susanto Dwi Susanto Dwi Susanto Jihad, Bagus Hayatul Kharismajati, Gema Lukmanul Hakim Rusydi Umar Rusydi Umar Susanto, Dwi Yuwono Fitri Widodo