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All Journal Indonesian Journal of Computing, Engineering, and Design Scientific Journal of Informatics
Yumna, Irsalina
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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SOCA-YOLO: Smart Optic with Coordinate Attention Model for Vision System-Based Eye Disease Detection Rianto, Rianto; Purwayoga, Vega; Aradea; Mikail, Ali Astra; Yumna, Irsalina
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.29293

Abstract

Purpose: The purpose of this research is to identify eye diseases using a modified YOLOv9. In particular, we modified YOLOv9 with the addition of Coordinate Attention (CA) for better eye disease detection performance, the use of Programmable Gradient Information (PGI), and Generalized Efficient Layer Aggregation Network (GELAN) for higher computational efficiency and accuracy. Methods: This study consists of several stages, including the acquisition of eye disease data obtained from the Roboflow website, data annotation, image augmentation, modeling using a modified YOLOv9, and model evaluation. Result: SOCA-YOLO model achieved an F1 score of 87,2% and mAP50 of 92,9%, outperforming YOLOv9-e by 1,7%. It also surpassed YOLOv6-L6 by 11,1%, YOLOv10-X by 0,8% in mAP50, and YOLOv8-X by 1,1% in recall, showcasing its superior detection accuracy and recall performance. Novelty: This research contributes by introducing the SOCA-YOLO model in improving the performance of the YOLOv9 by modifying the addition of Coordinate Attention (CA) for better eye disease detection performance, alongside Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for better computational efficiency and accuracy.
Sentiment Analysis of Multi-Brand Skincare Product Reviews Using IndoBERT Fine-Tuning Yumna, Irsalina; Rahmatulloh, Alam
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 8 No. 1 (2026): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v8i1.562

Abstract

The Female Daily platform predominantly features consumer reviews characterized by informal Indonesian and English code-mixing. This specific linguistic complexity presents a significant impediment to traditional classification methods, such as classical machine learning algorithms (e.g., Naive Bayes and SVM) and lexicon-based approaches, which often fail to accurately capture semantic nuances and contextual dependencies in unstructured text. To fill this research gap, this study uses a deep learning method with fine-tuned IndoBERT for multi-brand sentiment analysis. Using a dataset of 12,418 reviews across five popular skincare brands, the model achieved an accuracy of 85%, an F1-score of 0.84, a precision of 0.85, and a recall of 0.84. A key contribution of this research is the multi-brand analysis, which reveals distinct consumer perception patterns: Wardah and Emina achieved the highest proportions of positive sentiment, while Skintific and Garnier demonstrated a more balanced distribution between positive and negative reviews. In contrast, MS Glow exhibited more varied and diverse consumer opinions. These findings confirm that IndoBERT’s self-attention mechanism is highly effective and adaptive in processing the informal, code-mixed vocabulary of the beauty community, outperforming traditional methodologies in both robustness and contextual understanding.
Co-Authors Alam Rahmatulloh Aradea Mikail, Ali Astra Purwayoga, Vega Rianto, Rianto