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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
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Search results for , issue "Vol 9, No 5 (2025)" : 50 Documents clear
Mono Background and Multi Background Datasets Comparison Study for Indonesian Sign Language (SIBI) Letters Detection using YOLOv8 Andriyanto, Teguh; Handayani, Anik Nur; Ar Rosyid, Harits; Wiryawan, Muhammad Zaki; Azizah, Desi Fatkhi; Liang, Yeoh Wen
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3462

Abstract

The research in this paper focuses on the detection of Indonesian Sign Language System (SIBI) letters using the YOLOv8 object detection model. The study compares two datasets, one with mono-background (a simple, uniform background) and another with multi-background (complex and varied backgrounds). The research aims to evaluate how the complexity of image backgrounds affects the performance of the YOLOv8 model in detecting SIBI letters This study uses a dataset consisting of 24 SIBI letters (excluding J and Z due to the complexity of their gestures), sourced from Mendeley. The dataset was processed with and without data augmentation (rotation, brightness adjustments, blur, and noise) to test the model under various conditions. Four models were trained and tested: one using mono-background images, another using augmented mono-background images, a third using multi-background images, and a final model trained on augmented multi-background images. The results showed that the YOLOv8 model performed best with the multi-background dataset, achieving a precision of 0.995, recall of 1.000, F1 score of 0.997, and mAP50 of 0.994Adding to the model made it better at generalizing, but it took longer to train. The study finds that multi-background datasets with augmentation make the model much better at finding SIBI letters in real-world settings. This makes it a promising tool for projects that aim to improve communication for deaf people in Indonesia. The study suggests that more research should be done on augmentation techniques and bigger datasets to make detection more accurate. 
Enhancing Heart Disease Classification: A Comparative Analysis of SMOTE and Naïve Bayes on Imbalanced Data Wibowo, Jonathan Juliano; Kristiyanti, Dinar Ajeng; Wiratama, Jansen
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3248

Abstract

Heart disease remains a significant health concern, and early prediction plays a crucial role in improving patient outcomes. This study examines data mining techniques for heart disease classification, with a focus on the Naïve Bayes algorithm. A common challenge in such classification tasks is data imbalance, which can negatively impact the performance and evaluation metrics of the algorithm. To address this, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced data. Using the Knowledge Discovery in Databases (KDD) framework, the research followed data selection, pre-processing, transformation, mining, and evaluation stages. We applied SMOTE to the Naïve Bayes algorithm across three data split ratios (70:30, 60:40, and 50:50) and compared performance metrics before and after the SMOTE application. For the first dataset, the 50:50 split ratio showed the most tremendous improvement, with precision increasing from 30.74% to 78.15%, recall from 42.88% to 63.89%, and the Area Under Curve (AUC) from 0.819 to 0.831, although accuracy decreased from 86.82% to 73.01%. For the second dataset, the 70:30 split ratio yielded the most significant improvements, with accuracy rising from 95.22% to 97.72%, precision from 96.33% to 99.88%, recall from 51.11% to 95.57%, and AUC from 0.969 to 0.996. These results demonstrate that SMOTE can substantially improve classification performance in heart disease prediction, particularly in precision, recall, and AUC, with varying effects on accuracy depending on the dataset.
Development Of Local History-Based Virtual Reality Media to Improve Practical History Skills Ofianto, -; Mulyani, Fini Fajri; Ningsih, Tri Zahra; Putri, Suci Kurnia
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2458

Abstract

The acquisition of historical thinking skills is crucial for students, particularly in the areas of interpreting, documenting, and communicating information from diverse historical sources. These competencies are essential in educational media that supports learning. As such, this project aims to develop a virtual reality (VR) platform that will augment students' critical thinking abilities by presenting a range of historical sources. The research and development (R&D) process followed four key steps: defining, designing, developing, and disseminating. The study involved six teams of professional validators, two history professors, and 150 class XI students who comprised the subject sample. These stakeholders evaluated the viability of the product and the effectiveness of the media being developed. Data was gathered through teacher interviews, expert validation, and descriptive exams. Descriptive analysis was employed to assess the validity and practicality of the media, while the t-test and N-Gain were used to measure its effectiveness. Expert validation yielded an average score of 4.28, indicating good quality. The average score from practical testing by teachers and students was 4.40, demonstrating that VR media can be effectively utilized in history education. Notably, students exhibited a 69% N-Gain score, signifying increased proficiency in using VR material. These findings underscore the potential of VR media as a feasible, reliable, practical, and successful tool for enhancing practical historical skills in history education.
Optimizing Mangrove Classification with Data Fusion: Machine Learning Approaches for Enggano Island, Indonesia Pratama, Boby Bagja; Pratama, Wahyunda; Rudiastuti, Aninda Wisaksanti; Sugara, Ayub; Nugroho, Feri
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3360

Abstract

Mangrove ecosystems are crucial in coastal protection, carbon sequestration, and biodiversity. Accurate mapping is vital for the conservation and sustainable management of these species, especially in vulnerable areas like Enggano Island, Indonesia. This study evaluates the performance of machine learning algorithms in GEE to model mangrove distribution on Enggano Island, Indonesia, using multi-source data, including optical data (Sentinel-2), radar data (Sentinel-1), and elevation data filtering (FABDEM). Three input configurations were developed to explore the best combination of data: (1) visual and infrared bands from Sentinel-2, (2) Sentinel-2 band ratios and spectral indices, and (3) a fusion of Sentinel-2 optical data with Sentinel-1 SAR data. Several machine-learning algorithms, including Random Forest (RF), Classification and Regression Trees (CART), Minimum Distance (MD), Gradient Tree Boost (GTB), K-Nearest Neighbor (KNN), and Support Vector Machines (SVM), were assessed using accuracy, precision, recall, and F1 score. Results showed that the third configuration, which combined Sentinel-2 optical bands, band ratios, and Sentinel-1 radar polarimetric, provided the best performance with the highest overall accuracy (OA 95.19%) using the Random Forest algorithm. This approach demonstrated superiority in overcoming mangrove classification challenges, such as cloud cover, seasonal variability, and spectral similarity with non-mangrove vegetation. These results support the importance of mangrove monitoring in small islands and tropical regions, contributing to ecosystem conservation and coastal disaster mitigation.
K-Nearest Neighbor and Weight K-Nearest Neighbor Classification of Cork Fish Using Gray-Level-Co-Occurrence Matrix Algorithm Approach Fitriani Dewi, Euis Nur; Rachman, Andi Nur; Nur Shofa, Rahmi; Tarempa, Genta Nazwar
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2745

Abstract

Ornamental cork fish is a type of fish that is in great demand among the public as an ornamental fish. Ornamental cork fish have various types and colors; each variation has its own name and is a selling point among ornamental cork fish lovers. With a good motif, ornamental cork fish will have an expensive market value. However, for the most part, there are still many who do not know for sure what type of ornamental cork fish is included in the variation type classification because the colors are varied and seem similar. Because of this, this research created a system that can classify types of ornamental cork fish automatically based on data while still paying attention to the level of accuracy of the classification. The algorithm used for the initial classification process is KNN, which is chosen for its accuracy comparison level value. This algorithm does not consider the weight of each data point to be classified. The data processing process carried out only looks at the highest number of classes, which becomes the benchmark for labels from the classification results. In the classification process method using the KNN algorithm, there are still shortcomings in the classification process, so this research carried out a process of comparing classification accuracy using the Weight-KNN algorithm to increase the classification accuracy value. The process of the Weight-KNN algorithm stages is to carry out classification based on nearest neighbors first but still paying attention to the weight of each data. So that the classification process of determining the type of ornamental cork fish variation will be more accurate. Based on the results of experiments conducted, this research will focus on comparing the classification results between the KNN and Weight-KNN algorithms on ornamental cork fish. The results obtained state that the Weight-KNN algorithm has a higher level of accuracy with a weight of 83.6%, whereas using the KNN algorithm, it is only 80.6%.
Lecopelese - a Novel Evaluation Model for Measuring Educational Aspects of Game-based Learning Fatta, Hanif Al; Maksom, Zulisman; Zakaria, Mohd Hafiz
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3135

Abstract

This study aimed to establish a model for assessing the pedagogical quality of mobile game-based learning (GBL), which seeks to convey educational content to users. Evaluating the educational effectiveness of GBL necessitates a robust model tailored for this purpose. Current models can be improved to better address various educational challenges associated with mobile GBL. The LECOPELESE (LEarning COntent, PEdagogy and LEarning StyLE) model was developed by integrating relevant constructs identified in existing literature. To validate this model, a qualitative research approach was employed, drawing a sample from 270 undergraduate students. The analysis utilized Structural Equation Modeling (SEM) and resulted in a final model based on rigorous factor analysis. The findings indicated that the proposed model effectively measures educational quality in game-based learning. This new model includes more comprehensive constructs and items, addressing the educational aspects of game-based learning. Specifically, the model introduces a pedagogy construct to evaluate game-based learning quality, reflecting criteria for outstanding educational content and delivery through mobile applications. It assesses how effectively GBL provides real-world learning experiences. Additionally, the research highlights that the quality of pedagogy is influenced by two key factors: the GBL's ability to accommodate learners' unique characteristics (learning styles) and the quality of the learning content that adapts to learners' needs. Ultimately, the study demonstrates that both learning content and style significantly impact the pedagogy construct, suggesting that enhancing these areas can improve the overall pedagogical quality of game-based learning.
Enhancing IT Governance Based on Risk and Security Analysis in a Private School: A COBIT 2019 Approach Setyadi, Resad; Abdul Rahman, Aedah
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3414

Abstract

Information Technology (IT) has now become an inevitable necessity for every organization, both private and non-profit Schools. IT integration directly contributes to achieving Sustainable Development Goal (SDG) 4. By effectively utilizing IT, schools can improve the quality of learning, operational efficiency, and accessibility to education. However, private schools, particularly in the Bekasi area, have not researched IT Governance (ITG) to analyze the alignment between IT strategy and business strategy. This alignment is crucial to ensuring that the investments made in procuring IT systems in schools support the goals of quality education. This research aims to identify IT governance in private schools. COBIT 2019 is used as an approach and reference for both qualitative and quantitative analysis. The study, based on initial observations, focuses on the Risk and Security domains as the main areas of concern. The analysis results indicate that several schools in Bekasi have a capability level of APO12 and APO13 at level 2, which means that IT management processes have been carried out, but are not yet optimal and have not been quantitatively measured. The recommendations highlight the need for better risk management to drive improvement. System security must be managed through adequate security controls and by enhancing human resources in the IT field via routine training. With these measures, good IT governance can support SDG 4 by creating a safe, effective, and quality education environment. These recommendations are expected to serve as a reference for several private schools in Bekasi, enabling them to achieve higher educational standards.
Automated Detection of Molting Crabs Using YOLO: Enhancing Efficiency in Soft-Shell Crab Aquaculture Saputra, Dany Eka; Rangkuti, Abdul Haris; Dwi Putra, Sulistyo Emantoko; Daru Kusuma, Purba; Kurniawan, Albert; Gabriela, Melanie
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3468

Abstract

Crab molting detection is a crucial process in aquaculture, particularly to produce soft-shell crabs, which are considered a delicacy in many markets. Traditional methods of manually monitoring crabs for molting are labor-intensive and susceptible to human error. To address this challenge, this study examines the application of the YOLO (You Only Look Once) object detection model for automating the detection of molting crabs. YOLO is renowned for its capability to perform real-time object detection, making it an ideal choice for this application. Our research focuses on developing a YOLO-based system that accurately identifies molting crabs from videos or images captured in farming environments. The model was trained on a comprehensive dataset comprising images of crabs at various stages of molting, ensuring robustness against environmental variations and different lighting conditions commonly encountered in aquaculture settings. The results indicate that the YOLO model achieves high accuracy in detecting molting crabs, significantly enhancing the efficiency and reliability of the detection process compared to manual observation and other machine learning approaches. These advancements facilitate timely intervention and harvesting, which are critical for optimizing the quality and yield of soft-shell crabs. In our experiments, the recognition of the crab molting process was categorized into three classes: the molting crab, the crab skin, and the newly molted crab. Overall, the YOLOv8 and YOLOv11 models demonstrated impressive performance, achieving an average accuracy of 96% to 98%. This research on molting crab detection has proven successful and can be further extended to include other types of crabs.
Assessing Data Imbalance in Financial Distress Prediction: A Comparative Approach of Machine Learning and Economic Models Rahayu, Dyah Sulistyowati; Suhartanto, Heru; Husodo, Zaäfri Ananto
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3397

Abstract

This study aims to compare the effectiveness of machine learning models and economic models in predicting corporate bankruptcy, with a focus on addressing the issue of data imbalance. In this context, the number of companies experiencing financial difficulties is significantly smaller than that of healthy companies, which can lead to bias in predictions. The method used is an experiment with various data handling techniques and involves several classification models, namely Decision Tree, Neural Network (NN), K-Nearest Neighbors (KNN), Case-Based Reasoning (CBR), Support Vector Machine (SVM), and Merton Structural Model, which are tested on several data scenarios with resampling techniques, including Random Oversampling (ROS), Random Undersampling (RUS), and a combination of both. The evaluation results show that the Decision Tree, excluding ROA variables, and the Neural Network provide the best performance, with the Decision Tree achieving 86% accuracy and an AUC of 77.75, and the Neural Network achieving 86.76% accuracy and an AUC of 90.5. Other models, such as KNN and SVM, exhibit lower performance, achieving around 80% accuracy and a lower AUC. Based on these results, Decision Tree without ROA and Neural Networks are the best choices for predicting corporate bankruptcy. This study also demonstrates that financial models, such as the Merton Structural Model, are not significantly affected by data imbalance. The ultimate goal of this study is to provide recommendations for more reliable prediction models that enable financial institutions, investors, and companies to make more informed strategic decisions, as well as reduce financial risks through the early detection of companies at risk of failure.
Human Facial Pattern Shape Classification Using a Retraining Strategy and Convolutional Neural Network Architecture Hidayat, Tonny; Istiqomah, Dewi Anisa; Arifianto, Teguh
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3471

Abstract

Many shapes and patterns on the human body might be considered a person's uniqueness or feature since they differ significantly from one another, one of which is the shape of the face. In computer vision, the shape of a face is divided into five fundamental shapes. The experiment in this paper provides a model based on the final layer of the results of retraining InceptionV3, a Convolutional Neural Network (CNN) architecture for classifying human face photos. Inspired by human neural networks, this method generally works well for face recognition and computer vision research. This research begins with the stages of data acquisition, data exploration, classification, and evaluation. Retraining is performed to improve accuracy using the distance and angle of facial landmarks. The results are compared to other classification methods, including linear discriminant analysis (LDA), support vector machine with a linear kernel (SVM-LIN), support vector machine with a radial basis function kernel (SVM-RBF), artificial neural networks or multilayer perceptrons (MLP), and k-nearest neighbors. The facial dataset used consists of 747 photos, divided into five categories: oval, round, square, heart, and oblong. The Canny edge detector approach is utilized to enhance CNN accuracy, which has been effectively improved through training and testing. The maximum accuracy achieved was 91.7% based on training and testing at 85%-98%. This demonstrates that the outcomes of inceptionV3 retraining may appropriately adapt training data and outperform alternative classification techniques without the need to specify the function of certain features during the model training process.

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