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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo Systematics Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Jurnal Computer Science and Information Technology (CoSciTech) AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Komtekinfo Jurnal Informatika Ekonomi Bisnis Journal Medical Informatics Technology
Jufriadif Na`am, Jufriadif
Universitas Putra Indonesia YPTK
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health

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Journal : journal medical informatics technology

 1 
Segmentation in Identifying the Development of Ground Glass Opacity on CT-Scan Images of the Lungs Na`am, Jufriadif
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.1

Abstract

Ground Glass Opacity (GGO) in the image of the lungs is an object that is white in color. The image was recorded using a Computerized Tomography Scan (CT-Scan). This object has very similar color features to other objects in the lung image, making it very difficult to identify precisely. Likewise by observing the development of this object every time from recording continuously. This study aims to segment the GGO on CT-Scan images that are examined repeatedly due to an increase in complaints against patients. The processed image is an image of the lungs from the CT-Scan equipment. Patients were recorded twice at different time intervals. The processed image is an axial slice of the data cavity as a whole, totaling 12 images for each patient in each recording. The tool used for recording is a CT-Scan with the General Electric (GE) brand model D3162T. The method used is parallel processing with a combination of Image Enhancement techniques, Convert to Binary Image, Morphology Operation, Image Inverted, Active Contour Model, Image Addition, Convert Matrix to Grayscale, Image Filtering, Convert to Binary Image, Image Subtraction and Region Properties. The results of this study can identify the development of the GGO pixel size well, where the increasing number of patient complaints, the larger the GGO area. The extent of development of GGO is irregular with respect to time and examination. Each patient experienced an expansion of GGO by an average of 0.54% to 1.89%. This study is very good and can correctly identify ARF, so it can be used to measure the level of development of ARF in patients with accuracy.
Classification of Myopia Levels using Deep Learning Methods on Fundus Image Bismi, Waeisul; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.8

Abstract

Disorders of the eye or also known as eye disease is a condition that can affect vision for some people in their lifetime. There are 40 types of eye disorders or eye diseases, one of which is Myopia. Myopia is a visual disturbance that causes objects that are far away to appear blurry, but there is no problem seeing objects that are near. Myopia or nearsightedness is also known as minus eye. From this description, it is very important to conduct research in detecting eye diseases before the increase in eye minus and blindness. This study aims to classify myopic eye disease using the Deep Learning method with several different architectures, namely the VGG16, VGG19 and InceptionV3V3 models. Where the first is to distinguish normal and abnormal while the other is to classify with Augmented myopia image dataset and non augmented myopia image dataset obtained from the Retinal Fundus Multi-Disease Image Dataset (RFMID). In the implementation of the Deep Learning method using 20 Epochs. The results of the accuracy of the classification of eye diseases using the non augmented myopia image dataset are 66.0% for the VGG16 architectural model, then 95.99% for the VGG19 architectural model and 93.99% for the InceptionV3 architectural model and the accuracy results using the Augmented myopia image dataset are 97.53% for the VGG16 architectural model, 97.53% for the VGG19 architectural model and 99.50% for the InceptionV3 architecture model.
Advanced Filtering and Enhancement Techniques for Diabetic Retinopathy Image Analysis Saut Parulian, Onesinus; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 2 No. 3, September 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i3.40

Abstract

Diabetic retinopathy is a leading cause of visual impairment and blindness in diabetes sufferers. Early detection is crucial to prevent severe outcomes. This study presents an image processing method for retinal images to aid early detection. The method involves four steps: image enlargement, preprocessing, enhancement, and convolution. First, an algorithm enlarges the retinal image to increase resolution and reveal finer details. Preprocessing uses a min-max filtering algorithm to reduce noise and improve image quality. Next, specific pixel range enhancement techniques further refine the image and highlight relevant features. Finally, convolution with customized kernels detects and emphasizes areas indicating diabetic retinopathy, such as aneurysms and hemorrhages. Experimental results show improvement in image clarity and detail, enabling more accurate detection of diabetic retinopathy features. The correlation results are as follows: Filtering (0.35275, 0.20157, 0.4345), Enhancement (0.3214, 0.15823 0.34674), and Convolution (0.33542, 0.15758, 0.36826). The proposed algorithm enhances early detection and diagnosis by improving retinal image quality. Future work can optimize the algorithm and validate results with larger datasets, aiming to refine the determination of areas or pixel values relevant to diabetic retinopathy.
Optimizing Image Quality for Dog Skin Disease Diagnosis: Bacterial, Fungal, and Hypersensitivity Cases with MATLAB Puspitaningtyas, Mery Oktaviyanti; Na`am, Jufriadif
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.54

Abstract

Skin diseases in dogs, such as hypersensitive dermatitis, fungal infections, and bacterial dermatoses, present diverse clinical signs that complicate diagnosis in veterinary practice. This study employs MATLAB as an image-processing tool to enhance diagnostic accuracy through a structured pipeline. A dataset of 500 canine skin images obtained from Kaggle was processed using enlargement, histogram equalization, Gaussian filtering, and Sobel convolution. These methods improved image quality by enhancing contrast, reducing noise, and clarifying lesion boundaries. The experimental results demonstrate that the processed images allow veterinarians to more easily detect key diagnostic features, including changes in lesion texture, color, and shape. Enhanced visual clarity supports faster identification of disease patterns and reduces diagnostic ambiguity in clinical settings. This study highlights the potential of MATLAB-based image processing as an effective decision-support tool for veterinary dermatology, enabling quicker and more reliable treatment planning. Future work may integrate deep learning classification to further automate disease recognition.
Co-Authors Adiddo Restiady ADIF, RIANDY MARDHIKA Agung Ramadhanu Agus Perdana Windarto AHMADI Ainul Mardiah Alya Shafra Hewiz Apriade Voutama Asep Kurniawan Bob Subhan Riza Bob Subhan Riza Catur Suharinto Catur Suharinto Dika Putri Metalica Dodi Andre Putra Dolly Indra Dwiza Riana Elfina Novalia Elvin Syahrin Eri Prasetyo Wibowo, Eri Prasetyo Faruq Aziz Feriantano Sundang Pranata Feriantano Sundang Pranata Gunadi Bin Senitio Gunadi Nurcahyo Gunadi Widi Nurcahyo Iman Priyadi Irawadi Putra Johan Harlan Johan Harlan Johan Harlan Julius Santony Julius Santony Julius Santony Julius Santony Kurnia, Hezy Larissa Navia Rani Mardison Mardison Maulia Rahman Mesran, Mesran Montesna Muhammad Abrar Masril Mutiana Pratiwi Nasir Hamzah Novizar Nazir Nur, Rofil M Puspitaningtyas, Mery Oktaviyanti Raju Wandira Rakhmad Kuswandhie Riandy Mardhika Adif Romi Hardianto Rosda Syelly Sahari Sahari Saputri, Daniati Uki Eka Saputri Sarifuddin Madenda Sarjon Defit Saut Parulian, Onesinus Sri Hadianti Sugiarti, Sugiarti Sumidjan Sumidjan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Suryadi Putra Liawatimena Syafri Arlis Teguh Herwanto Waeisul Bismi Wanto, Anjar Yanto Saputra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yundari, Yundari