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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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Articles 40 Documents
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Search results for , issue "Vol 9, No 3 (2025)" : 40 Documents clear
Investigating P300 Response In Visual Searching Of Multiple Traffic Objects During Driving Yamamoto, Yuki; Kurahashi, Kohma; Wagatsuma, Nobuhiko; Nobukawa, Sou; Inagaki, Keiichiro
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2884

Abstract

Traffic accidents associated with visual errors such as misperception and carelessness continue to account for a significant proportion of traffic accidents. In traffic scenes, several types of traffic objects exist; therefore, drivers should pay attention to these objects for their recognition and safe driving decisions. Drivers need to allocate their visual attention resources to these objects because their recognition is closely related to safe driving behavior. Recent studies unveiled that attention-related event-related potentials (ERPs), specifically the P300, were observed in drivers’ electroencephalography, and its response characteristics varied with the intensity of attention. However, the factors of information inherent in traffic objects and driving behaviors remain mysteries. To understand the attention-related ERP P300 during visual searching of multiple objects while driving, we measured the P300 responses during vehicle driving using a driving simulator. We examined its response characteristics, especially in relation to types of traffic objects, considering drivers’ actions toward them and their capacity to induce visual attention. The results showed that the occurrence of P300 during multiple visual searches depended on the types of traffic objects, indicating that certain traffic objects more easily induced P300 responses from drivers, thereby attracting their attention. Moreover, we found that traffic objects that prompt driving actions are essential factors in their capacity to induce attention. By computational simulations of visual perception during driving using a model that can reproduce visual attention, further mechanisms of visual attention and the relationship between driving maneuvers and P300 responses will be understand.
Improving Accuracy in Deep Learning-Based Mushroom Image Classification through Optimal Use of Classification Techniques Kerta, Johan Muliadi; Rangkuti, Abdul Haris; Lun Lau, Sian; Kurniawan, Albert; Gabriela, Melanie; Tandianto, Alicia
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2820

Abstract

The primary purpose of this research is to address the existing knowledge gap surrounding various lesser-known types of edible mushrooms. A common understanding exists that mushrooms are edible and possess numerous health benefits. This research is intended to advance that understanding by deploying AI technology and deep learning models specifically designed to recognize and identify various fungi. During this research, we have developed a unique derivative of deep learning. This involved testing several Convolutional Neural Network (CNN) models aimed at automatically identifying and detecting different types of mushrooms and understanding the benefits associated with each type. The research methodology was divided into several stages: Collection of mushroom images, Preprocessing of images, Feature extraction, and Classification. The preprocessing involved adjustments such as scale, image rotation, and setting the brightness range. The goal of selecting and training the CNN model was to enhance the classification accuracy of mushroom images within each class. The data was divided into training, testing, and validation sets for the experimental stage. The purpose was to process image data from test and validation images based on the training images that have been processed. We evaluated the classification layer to be shorter, but it demonstrated excellent accuracy in assessing similarity performance. Based on several experiments conducted using different CNN models, DenseNet, MobileNetV2, and InceptionResNetV2 models achieved an accuracy of more than 90%, specifically 95%, 94%, and 92%, respectively. The most accurately recognized mushroom types include Snow, Dried Shitake, King Oyster, Straw, Button, and Truffle; some CNN models could identify these up to 100%. Overall, the models and algorithms used in this research successfully facilitated the identification and detection of various types of fungi. They were fast and displayed high accuracy performance. Hopefully, this research can be extended to process images of even more diverse types of mushrooms, particularly in terms of shape, color, and texture characteristics. This will enhance the depth and breadth of knowledge in this field and further advance our understanding of the beneficial properties of various mushrooms.
Optimizing YOLOv8 for Enhanced Melon Maturity Detection with Attention Mechanisms: A Case Study from Puspalebo Orchard Umar, Ubaidillah; Sardjono, Tri Arief; Kusuma, Hendra; Yani, Mohamad; Widyantara, Helmy
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2942

Abstract

Enhancing fruit maturity detection is crucial in the agricultural industry to ensure product quality and reduce post-harvest losses. However, commonly used maturity detection methods still rely on human visual inspection, which is prone to errors and assessment variability. Challenges like lighting variations, complex backgrounds, and diverse environmental conditions often complicate accurate and efficient detection. This study aims to develop and evaluate an optimized YOLOv8 model with attention mechanisms to detect melon maturity. The dataset was obtained from Puspalebo Orchard in East Java, Indonesia, comprising over a thousand melon images divided into three subsets: 70% for training, 20% for validation, and 10% for testing. The YOLOv8 model was modified to support the integration of attention mechanisms to enhance focus on significant features and detection accuracy. Data augmentation techniques were applied to capture environmental condition variations, improving the model's robustness. Evaluation on the validation subset showed a precision of 0.979 for all classes, recall of 0.962, mAP@50 of 0.981, and mAP@50-95 of 0.941. The model also demonstrated high efficiency for real-time applications with a preprocessing time of 0.1ms, inference time of 0.9ms, and post-process time of 0.9ms per image. The results of this study show advantages in detection detail, adaptability, and real-time efficiency compared to other studies in the past five years. Some weaknesses were identified, such as implementation complexity and the need for a large dataset. The developed YOLOv8 model improves melon maturity detection performance, offering a more accurate, efficient, and adaptive solution for the agricultural industry.
Exploring M-Learning User Information Systems through the Development of a Comprehensive Technology Acceptance Model Fiati, Rina; Widowati, -; K. N, Dinar Mutiara
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3258

Abstract

Digital technology brings a paradigm shift to the education quality ecosystem. Mobile learning provides an innovative space and motivation for information system users. The purpose of the research is to identify users who are adopting technology and support effective and efficient digital-based learning processes so that they can be improved in the future. It will also support an effective and efficient digital-based learning process, thereby increasing its usefulness in the future. The Technology Acceptance Model employs a method to evaluate technology acceptance based on the behavioral perception of information system users. Completion and data analysis using structural equation modeling validate the system that integrates satisfaction and academic performance values. Research materials were distributed through participant questionnaires targeting Mobile learning users via online forms. The study was conducted through a survey of students distributed through a questionnaire. A total of 510 participants were obtained. Based on a demographic survey, it was found that 54.24% used smartphones. The results showed that satisfaction and user behavior attitudes impact the intention to continue using mobile technology. The ease of the system has a positive impact on improving academic performance. The influencing factors are user satisfaction, continuation intention, and user behavioral attitude. So, it can be concluded that system usability and subjective norms influence the continuation intention of M-learning implementation. Future research implications can expand the variables from the perspective of motivation and economic factors in using mobile to improve online learning.
Domain-Independent True Fact Identification from Knowledge Graph Govindapillai, Sini; Lay-Ki, Soon; Su-Cheng, Haw
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3690

Abstract

The trustworthiness of information in the Knowledge Graph (KG) is determined by the trustworthiness of information at the fact level. KGs are incomplete and noisy. Yet, most existing error detection approaches were applied to specific KGs. A large percentage of error detection approaches work well on DBpedia, particularly. However, we do not have a single KG containing all the information regarding the entity relations of a specific entity from any random class. The main objective of this research is to increase the trustworthiness of entity relations from KGs. In this paper, we propose a framework for identifying fact entity information that combines two independent approaches from knowledge graphs, ensuring the accuracy of triples. The first approach detects true facts of entity information from various KGs by integrating Linked Open Data (LOD), string similarity measures, and semantic similarity measures. Next, we propose an error detection and correction approach using RDF Reification on the integrated environment, independent of any particular KG. The research was conducted on related and diverse knowledge graphs, DBpedia, YAGO and Wikidata. In addition, the effectiveness of RDF reification for identifying true facts is evaluated on Wikidata on selected entities. The proposed framework provides a flexible framework for improving data quality across multiple KGs, enabling broader applicability in data integration and semantic search domains. Future work will explore extending this approach to deep learning models with additional features like entity type and path for error detection and correction in real-time KG applications.
Exploring Strategies to Improve Digital Literacy Assessment Using Log Data Analysis Yoo, Sujin; Seo, Jeong-Hee; Kim, Hansung
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.4264

Abstract

The purpose of this study is to propose improvement strategies for national digital literacy assessment tools based on the analysis of log data from the first performance-based evaluation conducted in 2023. To achieve this, we analyzed log data from a total of 32,804 primary and middle school students. For the analysis, eleven types of data, including problem domain, problem type, and total number of logs, were utilized for the analysis. Students were assessed on their digital literacy level through 26 items, with primary school students given 40 minutes and middle school students given 45 minutes for the assessment. The key findings indicate that primary school learners generated 1.5 million log entries spanning four modules, whereas middle school participants produced 3.2 million log data points. Both primary and middle school students showed an increasing tendency to skip questions without answering as they progressed through the latter part of the assessment. Additionally, the tendency to skip questions increased when the minimum number of clicks required to solve a problem increased or when the problem length was longer. In the future, it is necessary to clearly define which parts of the log should be recorded in advance so that logs are consistently recorded. To accurately perform analyses such as student response type and pattern analysis, and error type analysis, a design for appropriate log data recording should be prioritized. This will enhance the reliability and validity of the tools and serve as a basis for future digital literacy policy development.
A Comparative Analysis of Building Hidden Layer, Activation Function, and Optimizer on Neural Network Sentiment Analysis Sanjaya, Samuel Ady; Kristiyanti, Dinar Ajeng; Irmawati, Irmawati; Hadinata, Faustine Ilone; Karaeng, Cristin Natalia
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2906

Abstract

The increasing diversity of opinions on social media offers a rich source for sentiment analysis, especially on controversial issues like the potential recession in Indonesia. This study aims to examine social media sentiment by utilizing three Deep Learning methods: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The main objective is to configure key hyperparameters, including the number of hidden layers, activation functions, and optimizers, to optimize performance. A dataset of 38,000 cleaned Twitter posts was used for this study. The preprocessing steps involve various techniques to prepare analysis, including case folding to standardize text, removal of punctuation to eliminate noise, stemming to reduce words to their root forms, and sentiment labeling using advanced tools like VADER and BERT to ensure accurate classification. Each deep learning model is trained using a diverse range of configurations for activation functions, such as Sigmoid and Swish, as well as optimizers like Adam and others to fine-tune performance. Among the models, the CNN, configured with 15 hidden layers, a Sigmoid activation function, and the Adam optimizer, outperformed the others, achieving the highest accuracy of 0.870 and a low loss of 0.316. The results highlight that while the number of hidden layers influences model performance, the choice of activation function and optimizer has a more significant impact on accuracy. Furthermore, the findings offer implications for future research, suggesting that activation functions and optimizers should be prioritized over hidden layers when aiming for improved sentiment analysis performance in various contexts.
Comparison of Machine Learning as an Inference Engine to Improve Expert Systems in Dengue Disease Istiadi, -; Marisa, Fitri; Joegijantoro, Rudy; Suksmawati, Affi Nizar; Rahman, Aviv Yuniar
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3192

Abstract

Dengue disease remains a significant public health challenge in tropical and subtropical regions, with rising incidence and mortality rates over the past few decades. While expert systems have been developed for early detection, traditional approaches often rely on rigid rule-based inference engines, which are limited by their dependence on expert-defined structures and lack adaptability to evolving knowledge sources. This study introduces a novel approach to enhance the flexibility and adaptability of expert systems by integrating machine learning (ML) techniques into the inference engine, leveraging the growing availability of medical record data as a dynamic knowledge source. Using a dataset of 90 medical records, balanced to 126 items via the Synthetic Minority Over-sampling Technique (SMOTE), we evaluated the performance of multiple ML algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), against traditional models like Naive Bayes (NB) and K-Nearest Neighbors (KNN). The DT, SVM, and ANN models demonstrated exceptional performance, achieving average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively. The key innovation of this research lies in developing an adaptive inference engine that can dynamically learn from medical data, reducing reliance on static rule bases and enabling the expert system to evolve with new knowledge. This approach improves diagnostic accuracy and provides a scalable and flexible framework for addressing other infectious diseases. By bridging the gap between expert systems and machine learning, this study paves the way for more intelligent, data-driven healthcare solutions with significant implications for public health and disease management.
Exploring Digital Competency as a Fundamental Job Competency in Higher Education Choi, Seongyune; Jang, Yeonju; Kim, Hyeoncheol
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.4253

Abstract

In the era of artificial intelligence, emerging digital technologies have revolutionized the nature of workplaces, making digital competency (DC) an increasingly essential competency in the modern job market. However, there are discrepancies between the existing and required levels of DC among employees, highlighting the need for proper and early educational interventions to foster this competency in higher education. In response to this need, this study aims to explore the degree of commitment (DC) among university students in work contexts. This study first developed an instrument to assess the level of DC and conventionally stressed job competencies—cognitive, interpersonal, and self-leadership—and applied it to 4,297 first-year university students. The study first compared the students' DC levels with other job competencies and found that their DC levels were lower than those of other competencies. Additionally, the study investigated the relationship between DC and other job competencies, identifying the prerequisite role of DC in affecting other competencies. Finally, the study also explored factors that promote DC and found that students' interest in emerging information and communication technologies is the most prominent indicator of their DC level. We also examined the effect of experience and attitude toward learning programming on the DC level and found that they were also significant factors. In particular, learning both block-based and text-based programming languages was the most effective means to improve DC. Accordingly, the practical implications for future studies and stakeholders regarding students' DC in higher education were discussed.
Online Counseling on Global Issues: Systematic Literature Review Ifdil, Ifdil; Zatrahadi, Muhammad Fahli; Darmawati, Darmawati; Istiqomah, Istiqomah; Bakar, Abu Yazid Abu
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2448

Abstract

The integration of expertise in counseling with a deep comprehension of contemporary technology is essential. Developing a sustained method is crucial for creating a practical framework to address the psychological ramifications associated with the escalating complexities of global challenges. Therefore, this study was conducted to explore the use and challenges of online counseling (e-counseling) for global issues using the systematic literature review (SLR) method. The search was carried out in the Scopus database to obtain 637 documents after limitations in the year of publication, starting in 2020–2023. Another limitation was the use of the English language, and after quality assessment, a 25-article document analysis was conducted. The results showed that e-counseling was critical in addressing challenges and impacted many individuals in different regions. According to NVivo analysis, the practical implementation of online counseling (e-counseling) encountered several challenges, such as using potentially vulnerable technology, constraints within interpersonal relationships, and incorporating different methods.

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