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Universitas Mohammad Husni Thamrin
Computer Science & IT Electrical & Electronics Engineering

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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.
Co-Authors Abdul Firman Adrian Dwichaya Danang Kristioko Legowo Lili Ruhyana Muhamad Rizki Ramadhan Muhtar Mulyatno