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All Journal Jurnal Simetris Bulletin of Network Engineer and Informatics (BUFNETS)
Sibi, Fifian Theresia
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Computer Science & IT

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DIAGNOSIS PENYAKIT KUSTA PADA ORANG DEWASA MENGGUNAKAN METODE BACKWARD CHAINING (Studi Kasus : Puskesmas Jayapura Utara) Alifyaa, Adhyndha; Sibi, Fifian Theresia; Caltrin, Yosefin; Simatauw, Jeanet D; Rusadin, Fina Alvionita; Hasan, Patmawati
Bulletin of Network Engineer and Informatics Vol. 2 No. 2 (2024): BUFNETS (Bulletin of Network Engineer and Informatics) October 2024
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/bufnets.v2i2.42

Abstract

Penyakit kusta disebabkan oleh bakteri Mycobacterium leprae dan Mycobacterium lepromatosis, yang menginfeksi kulit, saraf perifer, dan saluran pernapasan atas. Puskesmas Jayapura Utara menerima pasien dalam sehari sekitar 50 orang, di mana sekitar 5 orang di antaranya menderita penyakit kusta, yang dimana terjadi pada orang dewasa. Tidak semua kasus penyakit kusta terdiagnosis dengan cepat dan diberikan penanganan yang tepat. Dengan demikian, penyakit kusta seringkali terabaikan atau terdiagnosis terlambat, yang dapat mengakibatkan kerusakan yang lebih parah dan penyebaran yang lebih luas di masyarakat. Penelitian bertujuan membantu petugas kesehatan dalam menetapkan diagnosis dengan lebih efisien dan akurat. Dalam penelitian ini menggunakan metode Waterfall yang terdiri dari beberapa tahapan yaitu, Analisis kebutuhan : teknik pengumpulan data melalui obeservasi langsung ke lokasi penelitian kemudian melakukan wawancara langsung dengan petugas kesehatan di Puskesmas Jayapura Utara. Desain menggunakan UML dan Bahasa pemrograman PHP dengan database MySql, Implementasi : menerapkan metode backward chaining untuk diagnosis penyakit kusta pada orang dewasa, Pengujian : menggunakan blackbox, pemeliharaan :mencakup perbaikan bug. Dalam penelitian terdapat 6 jenis penyakit kusta yaitu Kusta Intermediate, Kusta Tuberkuloid, Kusta Borderline Tuberkuloid, Kusta Mid-Borderline, Kusta Borderline Lepromatous, Kusta Lepromatous. Dan memiliki gejala dari G1-G17 dimana terdapat Rule 1:G1,G2,G3 Then P1, Rule 2 : G4,G5,G6 Then P2, Rule 3 : G7, G8, G9 Then P3, Rule 4 : G10, G11 Then P4, Rule 5 : G12,G13,G14 Then P5, Rule 6 : G15, G16, G17 Then P6. Dengan demikian, penelitian ini dapat membantu dalam meningkatkan pengetahuan masyarakat tentang penyakit kusta dan membantu dalam mendiagnosis penyakit kusta dengan lebih baik.
Detection of Tinea Skin Disease Using Convolutional Neural Network (CNN) Method Hasan, Patmawati; Irjanto, Nourman Satya; Sibi, Fifian Theresia
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 17 No. 1 (2026): JURNAL SIMETRIS VOLUME 17 NO 1 TAHUN 2026
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The skin disease tinea, caused by a dermatophyte fungal infection, is a significant health concern and can affect the quality of life of sufferers. Early detection of this disease is essential to prevent its spread, especially in areas with limited specialized medical personnel. This study aims to develop a tinea skin disease detection system using the Convolutional Neural Network (CNN) method, which can classify three types of tinea, namely Tinea Pedis, Tinea Manuum, and Tinea Corporis. The dataset used consists of 1,146 images of skin lesions equally divided into three categories, with each category containing 382 images representing different stages of disease symptoms. This dataset was processed through preprocessing techniques, including image cropping, scaling, contrast adjustment, and data augmentation to improve the training quality of the model. The developed CNN model has a structure of 8 convolutional layers and was trained using 80% training data and 20% validation data. The training results showed that the model achieved 75% accuracy on the training data and 85% on the validation data after 20 epochs, with consistent loss reduction. These results show that the CNN model can detect tinea skin disease with high enough accuracy and can be used as a diagnosis aid for medical personnel, especially in areas that lack specialists. The developed web-based application allows users to upload images and receive diagnosis results directly, providing convenience in early detection of tinea skin disease. This research makes an important contribution to the development of technological solutions in the improvement of health services in areas with limited medical resources.
Co-Authors Alifyaa, Adhyndha Caltrin, Yosefin Nourman Satya Irjanto Patmawati Hasan Rusadin, Fina Alvionita Simatauw, Jeanet D