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All Journal Jurnal Ilmu Komputer dan Informasi Journal Medical Informatics Technology
Saut Parulian, Onesinus
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Computer Science & IT Dentistry Engineering Library & Information Science Medicine & Pharmacology Public Health

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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.
Traditional Batik Pattern Recognition with MobileNetV2 and Sampling-Based Hyperparameter Optimization Suyahman; Saut Parulian, Onesinus; Prasetyo, Deny; Anwar Fauzi, Muhammad
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1597

Abstract

Batik holds significant cultural value in Indonesia, reflecting the nation's historical and artistic heritage through its intricate patterns. Preserving these designs is essential for maintaining cultural identity and supporting artistic and economic communities. With the advancement of technology, deep learning has emerged as an effective approach for recognizing and classifying batik patterns. Convolutional Neural Networks (CNNs), particularly MobileNetV2, are widely recognized for their efficiency and accuracy in image classification. However, the performance of deep learning models is highly influenced by hyperparameter selection, which remains a challenging task. This study investigates the effectiveness of MobileNetV2 in classifying traditional Indonesian batik motifs, including Kawung, Mega Mendung, Parang, and Truntum, by applying different hyperparameter optimization methods such as Treestructured Parzen Estimator (TPE), Gaussian Process Sampler (GPS), Grid Search, and Random Search. The experimental results show that TPE achieved the best overall performance with a test accuracy of 91.94% and an F1 score of 92.09%. GPS and Grid Search obtained identical test accuracy of 90.83% with F1 scores of 90.89% and 90.87%, respectively, while Random Search produced the lowest performance with an accuracy of 88.61% and F1 score of 88.61%. These findings highlight the importance of structured hyperparameter optimization, particularly TPE, in enhancing the robustness of CNN-based batik classification. The results provide valuable insights for the development of automated batik pattern recognition systems that support cultural heritage preservation and related image classification applications.
Co-Authors Anwar Fauzi, Muhammad Jufriadif Na`am, Jufriadif Prasetyo, Deny Suyahman Suyahman