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All Journal ELINVO (Electronics, Informatics, and Vocational Education)
Ardiansyah, Dhista Dwi Nur
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Computer Science & IT Education Electrical & Electronics Engineering

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A Comparison of OpenNMT Sequence Model for Indonesian Automatic Question Generation Indrihapsari, Yuniar; Jati, Handaru; Nurkhamid, N.; Wardani, Ratna; Setialana, Pradana; Mahali, Muhamad Izzudin; Wijaya, Danang; Ardiansyah, Dhista Dwi Nur; Ardy, Satya Adhiyaksa; Tiala, Maria Bernadetha Charlotta Wonda; Al-khawarizmi, Andi Hakim; Ardiyanto, Widya
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 1 (2023): Mei 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i1.56491

Abstract

Evaluation of learners is a crucial aspect of the educational system. However, creating evaluation instruments is a process that demands teachers' time and energy. The researcher developed the Indonesia Automatic Question Generator in this study using an architecture modified from past studies. The primary goals of this project are (1) to construct an AQG tool utilizing the OpenNMT series and (2) to analyze and compare the model's performance. As a data source, this study employs the SQuAD 2.0 dataset and numerous sequence techniques, including BiGRU, BiLSTM, and Transformer. The researcher trained the models using OpenNMT-py and Google Collaboratory. This approach generates questions that are relevant to the context of the source. This study found that the model was acceptable.
Optimizing YOLO Models for Enhanced Road Damage Detection: A Performance Comparison of YOLOv5 and YOLOv8 Indrihapsari, Yuniar; Wijaya, Danang; Ardy, Satya Adhiyaksa; Siswanto, Ikhwan Inzaghi; Ardiansyah, Dhista Dwi Nur; Ardianto, Widya
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025 (In-Press)
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.88919

Abstract

Accurate road damage detection is vital for ensuring road safety and infrastructure maintenance. This study evaluates and compares the performance of four YOLO models—YOLOv5-S, YOLOv5-M, YOLOv8-S, and YOLOv8-M—for detecting road damage types such as Alligator Cracks, Longitudinal Cracks, Transverse Cracks, Potholes, and Lateral Cracks. The models were trained on a combined dataset from GRDDC 2020 and the Ministry of Public Works and Housing (PUPR) Republic of Indonesia, addressing challenges like class imbalance and diverse road conditions. Results show that YOLOv8-M achieved the highest mAP@0.5 (0.412), excelling in precision and recall for prominent damage types, making it the most reliable for high-accuracy applications. YOLOv5-M balanced precision and recall, while YOLOv5-S prioritized recall, making it suitable for detecting widespread damage. However, all models struggled with less prominent types, such as Lateral Cracks, due to class imbalance. Misclassifications were common, with the "Background" class absorbing predictions from other categories. This study highlights the strengths and limitations of each model, offering insights into improving road damage detection through better feature extraction, expanded datasets, and optimized architectures. These findings provide a foundation for deploying automated deep learning-based road damage detection systems to enhance infrastructure management.
Co-Authors Al-Khawarizmi, Andi Hakim Ardianto, Widya Ardiyanto, Widya Ardy, Satya Adhiyaksa Handaru Jati Mahali, Muhamad Izzudin Nurkhamid, N. Ratna Wardani Setialana, Pradana Siswanto, Ikhwan Inzaghi Tiala, Maria Bernadetha Charlotta Wonda Wijaya, Danang Yuniar Indrihapsari