Article Per Year (5 Year)
p-Index From 2021 - 2026
2.461
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Kesehatan Jurnal Teknologi Informatika dan Komputer Jurnal Fakultas Teknik UNISA Kuningan Jurnal Tika Mestro Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Jurnal Kesehatan Masyarakat Perkotaan Cahaya Pengabdian
Lili Ruhyana
Universitas Mohammad Husni Thamrin
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Nursing Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Design and Construction of a Digital Microscope for Automatically Counting Escherichia Coli Bacteria Using Artificial Intelligence (AI) Ruhyana, Lili; Muhtar; Gunawan; Legowo, Danang Kristioko; Firman, Abdul
Jurnal Kesehatan Masyarakat Perkotaan Vol. 5 No. 2 (2025): Jurnal Kesehatan Masyarakat Perkotaan
Publisher : LPPM Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jkmp.v5i2.3239

Abstract

Detection and counting of Escherichia coli (E. coli) bacteria is an important indicator in assessing water quality and food safety, particularly in the field of environmental health. Conventional methods still require a long time, skilled personnel, and have the potential to cause subjectivity in microscopic observations. This study aims to design and build a digital microscope system equipped with an automatic bacterial counting system based on Artificial Intelligence (AI). The use of this technology is expected to accelerate microbiological analysis, improve detection accuracy, and reduce subjectivity in the manual counting process. The study was conducted using an experimental approach with the System Development Life Cycle (SDLC) method. The dataset consists of 3,011 bacterial images divided into 74% training data, 13% validation data, and 13% test data. The object detection model uses YOLOv11 integrated with Roboflow for annotation and dataset management. Test results show that the model achieved a detection accuracy of 94.1% on the test data, indicating good performance in identifying and counting E. coli colonies. The system is also equipped with a Streamlit-based interface to facilitate users in visualizing detection results in real-time. Thus, the design of this AI-based digital microscope can be an effective and efficient solution to accelerate and improve the accuracy of microbiological analysis, especially in the detection of E. coli bacteria in the fields of environmental health and food safety.
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 Agus Adi Nursalim Alian Jerly Soeparli Danang Kristioko Legowo Fikri Ulul Albab Firman, Abdul Gelar Prachasti Ramdhani Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Husniawati, Neli Legowo, Danang Kristioko Mahendra, Aditya Ryan Martina Kuncara Muhafilah, Ilah Muhamad Rizki Ramadhan Muhtar muhtar Muhtar Muhtar Muhtar Muhtar Muktar Mulyatno Mulyatno Mulyatno, Mulyatno Nurjamal Pujitresnani, Arierta Soeparli, Alian Jerly Utami, Febi Puji Yani, Syahrul Abidin A.