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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.
Arjuna Subject : -
Articles 1,172 Documents
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Gamification Project-Based E-learning in Character Education: A Study in Senior High School Merliana, Ni Putu Eka; Widyantara, I Made Oka; Wirastuti, Ni Made Ary Esta Dewi; Saputra, Komang Oka; Setyohadi, Djoko Budiyanto
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.9.5.4033

Abstract

E-learning in education faces challenges in improving students' engagement, specifically regarding character education effectiveness. Gamification is among the strategies that can be applied to increase student engagement in the project-based learning process. Therefore, this study aimed to develop a Gamification Framework for Project-Based E-learning in Character Education (GaPolCE) as an innovative solution to improve engagement and character education at the senior high school level. A quantitative study was carried out using a quasi-experimental method, where data collection was carried out through a pre-post test and log data analysis to measure the effectiveness of gamification in achieving character education and student engagement. The results showed that the implementation of GaPolCE improved aspects of character education measured using the N-Gain score, where moral knowledge was in the high category (0.70) while moral feeling (0.49) and moral action (0.51) were in the moderate category. Student engagement increased significantly by 67%, 8%, and 25% for behavioral, emotional, and cognitive engagement. However, the effectiveness of in-depth character formation requires long-term evaluation. In addition, the assessment of the application of gamification in project learning for character education is still done manually, thus increasing teachers' workload. In this regard, further research is needed with a longitudinal approach to ensure the sustainability of its influence. In addition, it is necessary to develop an automatic assessment system based on artificial intelligence to increase the efficiency of character education evaluation. 
Evaluation of Sentiment Analysis Methods for Social Media Applications: A Comparison of Support Vector Machines and Naïve Bayes Leandro, Jose Octavian; Fianty, Melissa Indah
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2905

Abstract

This study compares the effectiveness of the Support Vector Machine (SVM) and Naïve Bayes methods in sentiment analysis of TikTok application reviews in Indonesia. The primary objective is determining which method better classifies positive and negative sentiments. The dataset consists of TikTok reviews collected from Indonesian users. SVM and Naïve Bayes methods are evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results show that SVM outperforms Naïve Bayes in detecting positive sentiments, with higher precision, significant recall, and a more robust F1-score. SVM’s AUC further highlights its ability to differentiate between positive and negative reviews. While Naïve Bayes offers some advantages in specific cases, SVM is recommended for applications requiring more precise sentiment detection on social media platforms like TikTok. The practical implications of this research are considerable. First, the findings can help developers and data analysts improve automated sentiment analysis tools, leading to better accuracy in classifying user feedback. Second, content moderation systems can leverage SVM to identify and mitigate harmful content, enhancing users' overall safety and experience more effectively. Third, businesses can utilize these insights to optimize their marketing strategies, tailoring campaigns based on real-time sentiment analysis. These applications will improve user engagement, reputation management, and customer satisfaction. Future research should explore additional machine learning techniques and further refine sentiment analysis models for enhanced performance.
Segmentation of Plain CT Image of Ischemic Lesion based on Trans-Swin-UNet Luo, Zhiqiang; Lim, Tek Yong; Hua, Xia
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3028

Abstract

The present study aims to build a hybrid convolutional neural network and transformer UNet-based model, Trans-Swin-UNet, to segment ischemic lesions of the plain computed tomography (CT) image. The model architecture is built based on TransUnet and has four main improvements. First, replace the decoder of TransUNet with a Swin transformer; second, add a Max Attention module into the skip connection; third, design a comprehensive loss function; and last, speed up the segmentation performance. The present study designs two experiments to evaluate the performance of the built model using both the self-collected and public plain CT image datasets. The model optimization experiment evaluates the improvements of Trans-Swin-UNet over TransUnet. The experimental results show that each improvement of the built model can achieve a better performance than TransUNet in terms of dice similarity coefficient (DSC), Jaccard coefficient (JAC), and accuracy (ACC). The comparison experiment compares the built model with four existing UNet-based models. The experimental results show that the built model had a DSC of 0.72±0.01, a JAC of 0.78±0.04, an ACC of 0.75±0.03 using the self-collected plain CT image dataset and a DSC of 0.73±0.02, a JAC of 0.79±0.03, an ACC of 0.76±0.02 using the public plain CT image dataset, achieving the best segmentation performance among five UNet-based neural network models. The two experimental results conclude that the built model could accurately segment ischemic lesions of the plain CT image. The limitations and future work of this study are also discussed.
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 Current Methods and Trends in Text Summarization: A Systematic Mapping Study Ahmad Raddi, Muhammad Faris Faisal; Hassan, Rohayanti; Zakaria, Noor Hidayah; Sahid, Mohd Zanes; Omar, Nurul Aswa; Firosha, Ardian
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.1654

Abstract

This paper presents a systematic mapping study of the current methods and trends in text summarization, a challenging task in natural language processing that aims to condense information from one or multiple documents into a concise and coherent summary. The paper focuses on applying text summarization for the Malay language, which has received less attention than other languages. The paper employs a three-phased quality assessment procedure to filter and analyze 27 peer-reviewed publications from seven prominent digital libraries, covering 2016 to 2024. The paper addresses two research questions: (1) What is the extent of research on text summarization, especially for the Malay language and the education domain? and (2) What are the current methods and approaches employed in text summarization, with a focus on addressing specific problems and language contexts? The paper synthesizes and discusses the findings from the literature review and provides insights and recommendations for future research directions in text summarization. The paper contributes to advancing knowledge and understanding of the state-of-the-art techniques and challenges in text summarization, particularly for the Malay language.
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.
Rule-Based Chatbot for Early Self-Depression Indication: A Promising Approach Wan Ab.Rahman, Wan Nurhayati; Abdul Hamid, Nurul Munirah
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.1628

Abstract

Depression is a prevalent mental health condition worldwide, often characterized by persistent sadness, loss of interest or pleasure, and feelings of worthlessness. Depression is the leading cause of mental health issues worldwide, and it is becoming more severe without self-awareness, early screening, and further medication. Early detection and intervention are critical in mitigating its adverse effects. Leveraging advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), chatbots have emerged as potential tools for early depression indication. Chatbots are beneficial tools in the mental health domain, such as in assisting mental health risk users. This paper presents the development of a rule-based chatbot aimed at detecting early signs of depression through conversational interactions by screening symptoms of depression. Predefined rules are developed to ensure the assessment can generate reliable results. The rule-based chatbot is developed to assist in depression indication assessment for mental health-risk individuals at an early stage and provide the risky patient with appropriate support and resources. The chatbot assessment has adopted the Depression Anxiety and Stress Scale 21 (DASS21) instrument. Based on the System Usability Scale (SUS) results, the rule-based chatbot has been accepted by all 30 respondents with good acceptance of an average SUS score of 77.2. Thus, the outcome of this chatbot can be utilized as a professional platform to encourage self-disclosure of mental depression indications for users, and it can be beneficial as the initial reference before recommending further action before the earlier help-seeking.
ChatGPT in Science Education: A Visualization Analysis of Trends and Future Directions Festiyed, -; Tanjung, Yul Ifda; Fadillah, Muhammad Aizri
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2987

Abstract

ChatGPT, as one of the products of artificial intelligence (AI)-based technology, has shown significant potential in science education. This study aims to analyze the development trends and focal points of ChatGPT research, especially in science education from the Scopus database in 2022-2024. This study used Bibliometric analysis which is a quantitative and qualitative evaluation technique of documents in a database. The search method was carried out with the Dimensions and Publish or Perish (PoP) databases using Scopus and data visualization using VOSviewer. Searches were conducted on article titles, abstracts, and keywords at once (TITLE-ABS-KEY) with the keywords "ChatGPT" and "science education". The results of the bibliometric analysis showed a significant increase in the number of ChatGPT-related publications in the field of science education, with several key topics taking center stage, such as pedagogical adaptation, AI-based learning, and evaluation of technology effectiveness in the teaching and learning process. Visual analysis using VOSviewer identified a clustering of research covering the integration of ChatGPT in the science education curriculum, the role of AI in facilitating collaborative learning, and the impact of using ChatGPT on student motivation and learning outcomes. This suggests that the use of AI, particularly ChatGPT, in science education is a growing area of research with significant potential impact. This research provides a comprehensive overview of recent developments in the use of ChatGPT in the field of science education and provides insights for future research.

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