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All Journal International Journal of Electrical and Computer Engineering International Conference on Engineering and Technology Development (ICETD) MUSTEK ANIM HA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian Masyarakat (ABDIRA) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Inovasi Teknologi Masyarakat
Heru Ismanto
Universitas Musamus Merauke
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Social Sciences Other

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Classification of Tuberculosis and Pneumonia Lung Diseases in X-Ray Images Using the CNN Method with VGG-19 Architecture Sri Murdhani, I Dewa Ayu; Ismanto, Heru; Suprihanto, Didit
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.269

Abstract

Tuberculosis (TB) and Pneumonia continue to be among the world’s leading causes of morbidity and mortality, particularly in low- and middle-income countries where access to advanced diagnostic tools remains limited. Conventional radiological interpretation, while effective, heavily depends on the experience and precision of radiologists, resulting in potential subjectivity and diagnostic variability. This study proposes a fully automated classification framework for lung disease detection using a Convolutional Neural Network (CNN) based on the VGG-19 architecture. The model aims to enhance diagnostic accuracy and reliability by leveraging deep learning techniques capable of capturing subtle radiographic patterns that may not be readily identifiable by human observers. A dataset of 3,623 chest X-ray images—divided into Normal, Pneumonia, and Tuberculosis classes—was compiled from Kaggle and Mendeley Data repositories. Preprocessing techniques including Contrast Limited Adaptive Histogram Equalization (CLAHE), cropping, resizing, and normalization were employed to enhance contrast and minimize noise. The model was trained and tested under four data-split configurations (80:20, 70:30, 60:40, and 50:50) to assess generalization capability. The 70:30 configuration achieved optimal performance, recording 96% accuracy, 97% precision, 95% recall, and a 96% F1-score. These findings demonstrate that the VGG-19 model can accurately distinguish between TB, Pneumonia, and Normal cases, providing a reliable foundation for AI-driven medical diagnosis. Future research will focus on dataset expansion, interpretability enhancement using Explainable AI (XAI), and the integration of this model into clinical decision-support systems.
Co-Authors Agus Prayitno Agus Qomaruddin Munir Amelia, Dara Andri Syafrianto Anindita Septiarini, Anindita Azhari Azhari Budi Rahmani Budi Rahmani Daud Andang Pasalli, Daud Andang Didit Suprihanto, Didit Dina Fitri Septarini, Dina Fitri Diningrum, Maynella Dwi Gultom, Josua Sonniwinter Gunawan, Muhammad Riswandi Hadiwijaya, Noor Alam Hamdani Hamdani Hamdani Hamdani Haqiqi, Aprilika Herry Ramadhani HERU Hugo Aprilianto Jamil, Muh. Jayanti, Rafika Juliyanti Lincolin Arsyad Merly, Sendy Lely Pratiwi, Putri Rinda Purwanti, Silviana Ramadhan, Fakhri Ramadhan, Muhammad Ady Setiyawan Rasidia, Fikri Senina, Alya Aulya Nurullita Silas Tanggu Redu Sri Murdhani, I Dewa Ayu Suharto Suharto Tuhumena, Jeremias Untari Untari