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All Journal Jurnal Teknologi Informatika dan Komputer Jurnal Kesehatan Masyarakat Perkotaan
Mulyatno
Universitas Mohammad Husni Thamrin
Computer Science & IT Electrical & Electronics Engineering Health Professions Public Health

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A Convolutional Neural Network Classifies Intestinal Diseases Using Endoscopic Images Gunawan; Muhtar; Lili Ruhyana; Danang Kristioko Legowo; Abdul Firman; Mulyatno; Muhamad Rizki Ramadhan
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3709

Abstract

Intestinal diseases such as polyps, esophagitis, and ulcerative colitis may lead to serious complications when they are not detected early. Endoscopy provides important visual information for diagnosis; however, manual interpretation still depends on clinical expertise and requires time. This study aimed to develop an automatic intestinal disease classification model based on Convolutional Neural Network (CNN). The study used a Research and Development method consisting of data collection, preprocessing, model training, evaluation, and web interface implementation. The dataset was obtained from Kaggle and consisted of 6,000 endoscopic images categorized into normal, polyps, esophagitis, and ulcerative colitis. Each class contained 1,500 images, divided into approximately 87% training data and 13% testing data. The model was trained for 15 epochs and evaluated using accuracy, loss, confusion matrix, and single-image testing. The results showed stable validation accuracy in the range of 98-99%, while single-image testing produced confidence scores from 99.91% to 100%. This system is recommended as an initial endoscopic image classification aid, with further development involving a random-image class and an examination history database.
Development of an Internet of Things-Based Medical Equipment Asset Tracking System Using ESP32 and GPS NEO-6M Danang Kristioko Legowo; Muhtar; Lili Ruhyana; Gunawan; Abdul Firman; Mulyatno; Adrian Dwichaya
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3710

Abstract

Medical assets are important components in supporting healthcare operations; therefore, their management requires a fast and integrated tracking system. This study aimed to design and test an Internet of Things-based medical equipment asset tracking system using an ESP32 microcontroller, NEO-6M GPS module, Wi-Fi connection, PHP-MySQL server, and a web-based digital map interface. The research used a Research and Development method involving needs analysis, hardware and software design, prototype implementation, and testing of connectivity, location accuracy, data transmission, database integration, and web interface performance. The results showed that the ESP32 connected to Wi-Fi within 5-10 seconds, transmitted data using HTTP GET every 10 seconds, and achieved outdoor GPS accuracy of approximately +/-3 meters. In indoor conditions or environments obstructed by conductive materials, accuracy decreased to more than 15 meters or the position was not detected. The system successfully stored location data in MySQL and displayed medical equipment markers in real time. This system is feasible as an asset monitoring prototype, with further development recommended for indoor localization, area notifications, and online server deployment.
Detection of Tuberculosis, COVID-19, and Pneumonia from X-ray Images Using CNN Muhtar; Gunawan; Lili Ruhyana; Danang Kristioko Legowo; Abdul Firman; Mulyatno; Aditya Ryan Mahendra
Jurnal Kesehatan Masyarakat Perkotaan Vol. 6 No. 1 (2026): Jurnal Kesehatan Masyarakat Perkotaan
Publisher : LPPM Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jkmp.v6i1.3541

Abstract

Tuberculosis, COVID-19, and pneumonia are lung diseases that require early detection support based on chest X-ray imaging. This study aimed to develop a detection system based on Convolutional Neural Network (CNN) to classify chest X-ray images into normal or disease conditions. This research applied a system development approach using secondary datasets from Kaggle, image processing through size and pixel-intensity normalization, CNN model training using Teachable Machine, and web-based application implementation using Python, Streamlit, OpenCV, Keras, and NumPy. The dataset was divided into 70% training data and 30% testing data. The evaluation results showed model accuracies of 99.37% for Tuberculosis, 100% for COVID-19, and 95.45% for Pneumonia. Application sample testing indicated that all test images were predicted according to the actual diagnosis, with average confidence scores of 98.88% for Tuberculosis, 100% for COVID-19, and 96.66% for Pneumonia. This system may function as a predictive tool for early chest X-ray screening; however, the results must be confirmed by health professionals and further validated using real clinical data.
Co-Authors Abdul Firman Aditya Ryan Mahendra Adrian Dwichaya Danang Kristioko Legowo Gunawan Lili Ruhyana Muhamad Rizki Ramadhan Muhtar