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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INOVTEK Polbeng - Seri Informatika
Giat Karyono
Amikom Purwokerto University
Computer Science & IT Engineering

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Analysis of the Effect of RetinexNet-Based Image Preprocessing on Object Detection Performance Using YOLOv8 Under Low-Light Conditions Bihandoyo Masdhi; Giat Karyono; Azhari Shouni Barkah
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6tnkh649

Abstract

The decline in performance in object detection systems based on deep learning is skewed to be meaningfully inclined when the system is used in low-lighting conditions, especially due to the decrease in visual quality of the image. In this study, the focus is directed to evaluate the effect of the application of RetinexNet-based image preprocessing on object detection performance using YOLOv8 in low-light environments. The experimental process was carried out to compare the detection results between models that used preprocessing and those that did not use preprocessing, based on evaluation metrics such as precision, recall, and mean average precision (mAP). The results indicate that improving the visual quality of the sword image is always followed by an increase in detection accuracy, because these changes can cause a shift in the distribution of visual features that have an impact on the model's generalization ability. In addition, the phenomenon of domain shift resulting from image changes using RetinexNet was also found, which had an effect on the consistency of YOLOv8 performance. The main contribution of this study is to provide empirical evidence that preprocessing strategies for low-light conditions not only need to focus on improving visual quality but also need to be adapted to the characteristics of the detection model in order to obtain a more adaptive pipeline under extreme lighting conditions.
DETEKSI SENTIMEN MULTIBAHASA: INDOBERT VS ROBERTA BERBASIS TERJEMAHAN NLLB-200 Fendi Elyon Ramadhan; Giat Karyono; Purwadi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8498

Abstract

Sentiment classification in Indonesian-language social media text remains a challenge because informal spelling, code-mixing, culture-specific expressions, and class imbalance can reduce model reliability. Using a dataset containing 1,336 labeled X posts related to the 2024 Indonesian Presidential Election, this research compares two transformer workflows for three-class sentiment classification, which are fine-tuning IndoBERT on Indonesian text and a translation-based workflow, where NLLB-200 translates the same text into English before the RoBERTa training process. The dataset was divided via stratified sampling into 1,069 training examples, 133 validation examples, and 134 test examples. Both models were trained for five epochs with identical optimization settings and evaluated using accuracy, weighted precision, weighted recall, weighted F1, classwise scores, and confusion matrices. IndoBERT achieved 82.09% accuracy and 80.31% weighted F1, compared with 80.60% and 75.69% for the NLLB-200 plus RoBERTa pipeline. The largest difference occurred in the neutral class, for which IndoBERT obtained 0.26 recall and RoBERTa only 0.05. Error analysis indicates that translation artifacts and majority-class bias jointly reduced sensitivity to neutral and context-dependent expressions. Direct monolingual fine-tuning was more reliable for this dataset, although translation-based transfer remained competitive for the dominant positive class. Future work should use larger independently annotated datasets, repeated runs, translation-quality analysis, and class-aware training objectives.
Co-Authors Azhari Shouni Barkah Bihandoyo Masdhi Fendi Elyon Ramadhan Purwadi Purwadi