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All Journal Journal of Natural Sciences and Mathematics Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Unisda Journal of Mathematics and Computer Science (UJMC) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) JSiI (Jurnal Sistem Informasi) Jurnal ICT : Information Communication & Technology International Journal of Engagement and Empowerment (IJE2) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) JEECS (Journal of Electrical Engineering and Computer Sciences) Alpha Epsilon Journal of Mathematics
Fathorrozi Ariyanto
Universitas Islam Madura
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Physics Other

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APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS (CNN) FOR HEPATITIS C VIRUS (HCV) DISEASE DETECTION Fathorrozi Ariyanto; Indra Maulana; Moh. Aminollah Hamzah; Aang Kisnu Darmawan
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10542

Abstract

Hepatitis C is a disease that attacks the liver and can progress to more serious conditions, such as cirrhosis or liver cancer, if not diagnosed and treated properly. Conventional diagnostic methods for Hepatitis C often face challenges in terms of efficiency and accuracy, so an innovative AI-based approach is needed to improve early detection. In this study, we apply a 1D Convolutional Neural Network (CNN) to classify Hepatitis C patients, using a dataset from Kaggle consisting of 615 samples with various medical parameters. The dataset goes through a series of preprocessing stages, including data cleaning, normalization, and feature transformation, before being applied to a 1D CNN model. The model is trained using the Adam optimizer, with ReLU activation functions in the convolution layer and sigmoid in the output layer. Model performance is evaluated through accuracy, precision, recall, and F1-score metrics. The results show that the developed 1D CNN model achieves an accuracy of 75% in detecting Hepatitis C. Although these results show promising potential, there is still room for improvement through exploration of more complex architectures or the use of larger datasets. Thus, this research is expected to make artificial intelligence an effective tool in the diagnosis of Hepatitis C, increasing accuracy and efficiency in the process. Keywords: Hepatitis C, 1D CNN, Deep Learning, Disease Classification, Medical Diagnosis
Co-Authors Aang Kisnu Darmawan Aang Kisnu Darmawan Anwari Darmawan, Aang Darmawan, Aang Kisnu Darmawan Daryanto, Eko Doni Ferdiansyah dzaki diana Dzaki Mutammadien Illiyin Emi Yunita Endang Tri Wahyurini Ferdiansyah, Doni Indra Maulana Jamilatul Isti’adah Moch. Haikal Moh. Aminollah Hamzah Moh. Badri Tamam Mohamad Tafrikan Muqaddas, Zaiful Prasanti Mia Purnama Rica Amalia Rofiuddin Saiful Hadi Tony Yulianto Ulfatul Husna Zuhrotul Hasanah