Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal INOVTEK Polbeng - Seri Informatika
Tinaliah
Multi Data Palembang University
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Classification of Diabetic Retinopathy Using ShuffleNet V2 and Real-ESRGAN with CLAHE Image Enhancement Nicholas Edison; Tinaliah
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/g1xj7p28

Abstract

Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if not detected and treated early. Manual DR grading from fundus images is time-consuming and highly dependent on expert availability, motivating the need for automated and efficient decision-support systems. This study proposes a lightweight DR severity classification model using ShuffleNet V2 combined with a preprocessing pipeline consisting of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Real-ESRGAN-based super-resolution. Unlike prior works that mainly employ these enhancement techniques with deeper or computationally expensive networks, this study explicitly investigates their synergistic integration with ShuffleNet V2 to improve lesion visibility while preserving computational efficiency for resource-constrained environments. Experiments conducted on the APTOS 2019 dataset demonstrate that the proposed combination significantly improves classification performance, achieving a best accuracy of 90.70%, with balanced precision, recall, and F1-score when optimized using Adam. Comparative analysis with the SGD optimizer further reveals a trade-off between accuracy and inference speed. The results confirm that combining CLAHE and Real-ESRGAN with ShuffleNet V2 offers an effective and efficient solution for automated diabetic retinopathy grading, highlighting its suitability for large-scale screening and low-resource clinical deployment
Assessing the Impact of Image Preprocessing on Convnext Performance for Waste Classification Ivander Destian Luis; Tinaliah
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/fsa3at15

Abstract

Waste has become an increasingly urgent environmental issue in everyday life. The waste is constantly increasing due to population growth, urbanization, and consumption. The increasing amount of waste needs more intelligent systems to help with the management of waste, especially with the sorting of waste. Unfortunately, the absence of the public's awareness of the importance of waste management has led to the ineffective collection of waste. Thus, there is a need for classifying the waste into technological systems based on various waste types. This research has computing waste types using ConvNetX. The research methodology is based on the collection and preprocessing of data that includes different image enhancement techniques such as CLAHE and bilateral filtering. This study employed the ‘Garbage Classification Dataset’ found on Kaggle. The dataset is split into 80% of it as training data, 10% of it as testing data, and the last 10% of it as validation data. The ConvNeXt model was trained using one of the training sets after the data was split and was subsequently measured using the validation and test sets for the training of the model. This research analyzed the effects of image preprocessing by using a baseline, which was no preprocessing (Scenario 1), and then using preprocessing (Scenario 2). The results from the experiments showed Scenario 2 had a higher accuracy of 94% compared to the baseline of 90%. The use of CLAHE and bilateral filtering positively impacted the F1 score by increasing it to Glass (96%) and Plastic (92%) and having a full recall (100%) for Metal. Scenario 2 resulted in a total training time of 20.86 minutes, and Scenario 1 was 11.83 minutes, which means that Scenario 2 had a lower computational efficiency. Nevertheless, the additional time was well spent for the considerable consistency improvement in the classification of all categories. This makes it evident that substantial image preprocessing is necessary for the model to be able to generalize and classify images with complex visual details.
Application of EfficientNet Deep Learning with Wiener Filter for Freshwater Fish Disease Image Classification Christofer Evan Setiawan; Tinaliah
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/k1xeb958

Abstract

Challenges pertaining to the timely and accurate diagnosis of diseases in freshwater fish have adversely impacted the productivity of the aquaculture industry. Image classification using deep learning techniques has the potential to overcome such challenges. However, this potential has not been realized due to such problems as image noise, motion blur, and small dataset sizes. Most prior studies in this area employ the same Convolutional Neural Network (CNN) architectures and, while using the same or similar techniques generic to the studies, preprocess the images. The focus of this study is to compare and benchmark the image classification performance of the EfficientNet architectures (B0 to B7) using the Wiener filter as a preprocessing technique for the classification of diseases in freshwater fish. The experiments used a publicly available dataset of 1,750 images of seven diseases in fish, while maintaining identical training parameters to yield sixteen different experimental configurations. Metrics such as accuracy, precision, recall, and F1 score were exercised while evaluating model performance. The data show that medium-scale architectures surpass both smaller- and larger-sized variants. The optimal performance was achieved by EfficientNet-B4 and Wiener Filter with an accuracy of 94.89%, a precision of 95.15%, a recall of 94.92%, and an F1-score of 94.89%. The results confirm that preprocessing with a Wiener filter improves performance on classification tasks using medium-sized models and further elucidate the applicable value of the model developed in this study in aquaculture and its related interventions.
Co-Authors Christofer Evan Setiawan Ivander Destian Luis Nicholas Edison