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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

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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.
Co-Authors Agus Adi Nursalim Alian Jerly Soeparli Fikri Ulul Albab Firman, Abdul Gelar Prachasti Ramdhani Gunawan Gunawan Gunawan Gunawan Gunawan Husniawati, Neli Legowo, Danang Kristioko Mahendra, Aditya Ryan Martina Kuncara Muhafilah, Ilah muhtar Muhtar Muhtar Muhtar Muhtar Muktar Mulyatno Mulyatno, Mulyatno Nurjamal Pujitresnani, Arierta Soeparli, Alian Jerly Utami, Febi Puji Yani, Syahrul Abidin A.