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All Journal JOIV : International Journal on Informatics Visualization
Arrova Dewi, Deshinta
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Computer Science & IT

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Systematic Literature Review on Persuasive System Design Framework for Managing Curriculum Performance Saifunnizam, Syamir Thaqif; Md Fudzee, Mohd Farhan; Hanif Jofri, Muhamad; Kasim, Shahreen; Arrova Dewi, Deshinta; Arshad, Mohamad Safwan; Yulherniwati, -
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3663

Abstract

Integrating digital resources into educational assessment has led to the widespread adoption of e-portfolios as tools for documenting and evaluating student achievement, thereby transforming traditional evaluation methods. However, the existing frameworks primarily focus on assessing academic performance, often neglecting the comprehensive monitoring of student’s co-curricular activities. To overcome current gaps in comprehensive student evaluation, this study introduces a conceptual framework incorporating persuasive system design (PSD) into an e-portfolio to facilitate efficient co-curricular performance monitoring in Malaysian secondary schools. To ensure a thorough approach to educational evaluation, it is essential to effectively monitor and manage academic and extracurricular performance to understand student progress comprehensively. By adding Physical Activity, Sports, and Co-curriculum Assessment (PAJSK) – specific categories and key PSD elements- primary task support, dialogue support, system credibility support, and social support- that are all designed to improve user engagement and system dependability in an educational environment, the framework builds on the Oinas-Kukkonen and Harijumaa PSD Model. This study adapts and discusses the persuasive design elements to meet the goals of educational assessment frameworks by comparing PSD implementation in e-health, e-tourism, e-commerce, and e-learning. The results offer an overview of developing a practical, engaging e-portfolio framework that facilitates comprehensive student evaluation, especially in educational environments focusing on co-curricular achievement.
Extraction Model for Musical Elements of Javanese Traditional Songs from Gendhing Music Sheets based on Kepatihan Notation Kurniawati, Arik; Arrova Dewi, Deshinta; Satria Erlangga, Bima; Damayanti, Fitri; Oktavia Suzanti, Ika
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3573

Abstract

Traditional Javanese gamelan music, particularly its songs, is an integral part of Indonesian culture and identity. However, gamelan music notation remains manual, disorganized, and difficult to access. This poses challenges to balanced education, community sustainability, and digital preservation. This study introduces an automated data extraction and gamelan notation transcription process for transforming Javanese gamelan notation in PDF format into a structured CSV. The innovation process involves parsing PDF-based Kepatihan notation, symbol-to-number conversion, musical section recognition (e.g., buka, lagu, suwuk), and organization in gatra units—each of four melodic notes. The process produces detailed metadata, such as song title, tuning (laras), mode (pathet), and gendhing classification. To evaluate extraction accuracy, the validation period also included a comparison of the converted gatra with the original PDF. The results show that the system achieved 100% accuracy on a sample size of 10 gatra and reduced processing time by 97.5% compared with manual methods. The completed dataset consists of 31 gendhing songs, providing an analyzable and scalable collection for future musicological research and education training. This study contributes to the fields of Music Information Retrieval (MIR) and Digital Humanities by enabling the efficient, standardized digitization of historical music notation. This structured dataset empowers the development of automatic notation generators, making inclusive learning tools accessible to novices and facilitating the documentation of cultural heritage through technology.
Classification of Rice Disease Using Deep Learning Object Detection Yolov8 Dwi Satoto, Budi; Rosa Anamisa, Devie; Yusuf, Muhammad; Kautsar Sophan, Mohammad; Kembang Hapsari, Rinci; Irmawati, Budi; Arrova Dewi, Deshinta
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3578

Abstract

Rice plant pests and diseases are among the primary threats to agricultural production, particularly in rice-growing regions, which can result in a significant decrease in crop yields and food production. Therefore, technology is essential for accurately detecting and classifying pests and diseases. In this research, the author proposes using deep learning-based object detection for moving objects. This is because observations are made on relatively large land areas. Images are captured by drone cameras as videos, which are then used to create ground-truth markers and identification targets during training. YOLO v8 is the latest object detection model on moving media. This model offers advantages in speed and accuracy, making it well-suited for applications that require precise results on agricultural land. The dataset comprises videos of rice plants infested with pests and diseases. After completing labeling and training, the YOLO v8 model can detect and classify pests and diseases in real time using markers in the form of frames with identification labels. Farmers can identify pest and disease attacks earlier by implementing this system, enabling more effective, timely pest control measures. The study's results showed that the training accuracy was 91.5%. The F1-Confidence measurement value obtained was 0.84, the Precision-Recall Curve was 0.891, and the Recall Confidence Curve was 0.97. The trial results, based on experimental data, achieved confidence accuracy of 80% to 95%.
Co-Authors Arik Kurniawati Arshad, Mohamad Safwan Budi Dwi Satoto Budi Irmawati Fitri Damayanti Hanif Jofri, Muhamad Kautsar Sophan, Mohammad Kembang Hapsari, Rinci Mohd Farhan MD Fudzee, Mohd Farhan Muhammad Yusuf Oktavia Suzanti, Ika Rosa Anamisa, Devie Saifunnizam, Syamir Thaqif Satria Erlangga, Bima Shahreen Kasim, Shahreen Yulherniwati, -