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All Journal Jurnal Buana Informatika POSITIF Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Pilar Nusa Mandiri Konvergensi Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) Journal of Information Systems and Informatics Jurnal Informasi dan Teknologi International Journal of Engagement and Empowerment (IJE2) Jurnal Minfo Polgan (JMP) Jurnal Informatika Teknologi dan Sains (Jinteks)
Aang Kisnu Darmawan
Universitas Islam Madura
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Library & Information Science Mathematics Social Sciences 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 Agus Hermanto Anekawati, Anik Anwari . Anwari Anwari Anwari, Anwari Arisandi Dwi Harto Bakir Busro Umam Daniel Oranova Siahaan Daryanto, Eko Dwi Kartony Abdul Jalil Dwiharto, Arisandi Efenie, Yuri Endang Tri Wahyurini Fathorrozi Ariyanto Harto, Arisandi Dwi Hilya Zada Mardhatilla Al Haadiy Hoiriyah Hoiriyah Hoiriyah Hoiriyah, . Hoiriyah, Hoiriyah Hoiriyah, Hoiriyah Hozairi Imam Arifin Indra Maulana Ismiyati Iwan Santosa Kartika Maulida Hindrayani Kartini Kartini Kartini Mardiana, Helva Masdukil Makruf, Masdukil Maudi Adella Moh. Aminollah Hamzah Mohammad Bhanu Setyawan Mohammad Waail Al Wajieh Muhammad Agus Muljanto Muhsi Muqaddas, Zaiful Nindia Prameswari Putri Cahyono Nur Umamah Qomariya, Nurul Rizky Fatkhur Rohman Rofiuddin Ronny Mardiyanto, Ronny Salman Alfarisi Setijadi, Eko Tony Dwi Susanto Tri Yandi Umam, Busro Umam, Busro Akramul Usman Abrori Walid, Miftahul Wrahatnala, Ery Sadewa Yudha