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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 48 Documents
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Search results for , issue "Vol 9, No 2 (2025)" : 48 Documents clear
Analysis of Student Perceptions on Blended Learning Using Learning Management System (LMS) for Physical Education, Sports, and Health Courses Rustam, R.; Lince, Ranak; Kusmaladewi, K.; Halim, Patmawati; Ahmar, Ansari Saleh; Rahman, Abdul; Rusli, R.
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.9.2.3235

Abstract

This study investigates student perceptions of LMS-based Blended Learning in Physical Education, Sports, and Health subjects at Public Junior High School 25 in Barru Regency, South Sulawesi, Indonesia. A descriptive quantitative design was utilized for this research. Probability sampling was employed to ensure representativeness. Data was collected through a structured questionnaire consisting of twenty- five items designed to measure four key aspects of LMS- based blended learning: e- learning knowledge, e- learning accessibility, e- learning usefulness, and e- learning usage satisfaction. The reliability of the questionnaire was confirmed via Cronbach's α, which produced a value of 0 830, and McDonald's ω, yielding a value of 0 0.850, indicating strong internal consistency and reliability of the instrument. Results showed that 82. 55% of respondents agreed or strongly agreed that e- learning knowledge is vital for supporting blended learning, suggesting awareness and confidence among students regarding the role of digital learning tools in enhancing their educational experiences. Additionally, 61. 61.41% agreed or strongly agreed that e- learning accessibility significantly aids the implementation of blended learning, emphasizing that easy access to LMS platforms is crucial for student engagement. Furthermore, 60. 16% acknowledged the importance of e- learning usefulness in the current educational landscape, highlighting a widespread recognition of digital tools' significance in education. Lastly, 53. 83% stated satisfaction with e- learning usage is a key factor influencing successful blended learning experiences. These findings indicate a favorable perception among students toward LMS-based blended learning in physical education, sports, and health subjects. The study emphasizes the importance of e- learning knowledge, accessibility, usefulness, and satisfaction for creating effective blended learning environments. Further research is suggested to examine the long-term effects of LMS-based blended learning on student outcomes across diverse educational settings.
A Better Performance of GAN Fake Face Image Detection Using Error Level Analysis-CNN Siregar, Maria Ulfah; Nurochman, Nurochman; Setianingrum, Anif Hanifa; Larasati, Dwi; Santoso, William; Stefany, Meisia Dhea
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.9.2.2698

Abstract

The use of face images has been widely established in various fields, including security, finance, education, social security, and others. Meanwhile, modern scientific and technological advances make it easier for individuals to manipulate images, including those of faces. In one of these advancements, the Generative Adversarial Network method creates a fake image similar to the real one. An error-level analysis algorithm and a convolutional neural network are proposed to detect manipulated images generated by generative adversarial networks. There are two scenarios: a stand-alone convolutional neural network and a combination of error-level analysis and a convolutional neural network. Furthermore, the combined scenario has three sub-scenarios regarding the compression levels of the error-level analysis algorithm: 10%, 50%, and 90%. After training the data obtained from a public source, it becomes evident that using a convolutional neural network combined with compression of error level analysis can improve the model’s overall performance: accuracy, precision, recall, and other parameters. Based on the evaluation results, it was found that the highest quality convolutional neural network training was obtained when using 50% error level analysis compression because it could achieve 94% accuracy, 93.3% precision, 94.9% recall, 94.1% F1 Score, 98.7% ROC-AUC Score, and 98.8% AP Score. This research is expected to be a reference for implementing image detection processes between real and fake images from generative adversarial networks.
Crypto Forecast: Integrating Web Scraping and Data Analysis for Cryptocurrency Price Prediction Gadge, Krutika; Daduria, Shreyash; Sarodaya, Abhishek; Borkar, Pradnya Sulas; Badhiye, Sagarkumar Shridhar; Agrawal, Pratik K
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.9.2.2767

Abstract

Accurately predicting cryptocurrency prices is still a difficult task because of the extremely volatile nature of the market. This study introduces a new methodology combining web scraping, data analysis, and machine learning to further improve prediction accuracy. A live cryptocurrency monitors gathers data from various sources such as trading volumes, price volatility, and sentiment in market to create a rich data set. Feature engineering is used to convert raw data into useful inputs for machine learning algorithms to further enhance prediction functions. Utilizing Python libraries including Beautiful Soup, Pandas, Scikit-learn, and deep learning libraries, the correct predictive model is designed and strictly tested for precision, performance, data quality, usability, scalability, and cost. The proposed hybrid model is a combination of traditional statistical methods with deep learning models to overcome the constraints of conventional forecasting methodologies. The output reflects the performance of the model in identifying the trends in the market and rendering data-driven insights to traders and investors. Future studies can employ different data sources, including social media sentiment analysis, financial news articles, and web-based cryptocurrency forums, to enhance predictability. Further advancement in time series forecasting through deep learning models, including transformer models, may also enhance the precision of long-term forecasting. A deeper insight into how external forces, including government intervention, macroeconomic trends, and emerging blockchain technologies, would complement our understanding of cryptocurrency market dynamics. This study contributes to complementing predictive analytics in financial markets by providing useful insights to investors, researchers, and policymakers. 
A Comparative Analysis of Naïve Bayes and Logistic Regression for Student Satisfaction Prediction in Microsoft Teams Wulandari, Dewi Arianti; Soeprobowati, Tri Retnaningsih; Nugraheni, Dinar Mutiara Kusumo
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.9.2.3197

Abstract

Student satisfaction reflects educational quality, influences retention, and enhances institutional reputation. This study examines the impact of student performance and motivation in online learning using Naïve Bayes and Logistic Regression. Data from 316 respondents at PLN Institute of Technology, collected during the COVID-19 pandemic via Microsoft Teams, were divided into 80% training and 20% testing. The process included questionnaire distribution, data labeling, parameter determination, and normalization to ensure completeness and reliability.  Questionnaire data is stored in Excel format and was processed using Python for programming, Pandas for data manipulation, and Kaggle for dataset management, before being analyzed with Naïve Bayes and Logistic Regression. Finally, the processed data is tested for accuracy using confusion matrix. The results show high precision, recall, f1-score, and accuracy for both methods, with Naïve Bayes achieving an accuracy 93.75% to 97.44% and Logistic Regression achieving 98.95%. In summary, Naïve Bayes can be optimized with threshold adjustments, but Logistic Regression is more reliable than consistent, maintaining high accuracy across different thresholds. Institutions can update their strategies using the latest data to enhance learning experiences. From those results, it can be concluded that Naïve Bayes method should be enhanced, while Logistic Regression is proven reliable. In the future, researchers are encouraged to use more diverse datasets while also considering external factors such as technological infrastructure and psychological support.
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.
A Comprehensive Visualization for Music Education and Artificial Intelligence Sularso, Sularso; Wadiyo, Wadiyo; Cahyono, Agus; Suharto, Suharto; Pranolo, Andri
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.9.2.2500

Abstract

Artificial intelligence (AI) has revolutionized traditional methods and improved decision-making and automation. AI has also been used to enhance teaching methods, student learning, and research in music education. This study will examine literature on music education and AI. This study aims to investigate significant themes, trends, and achievements in this burgeoning discipline. This study will examine scholarly articles, conference papers, and other relevant literature to explore AI's applications, issues, and future in music education. Machine learning, natural language processing, computer vision, and deep learning are utilized in music education. These techniques are used in music composition, performance evaluation, instructional support, and individualized learning. Adaptive training, real-time feedback, and intelligent music production demonstrate the transformative potential of AI. This study will illuminate the obstacles AI faces in music education. Ethical considerations, data privacy, algorithmic bias, and human competence must be thoroughly investigated. In addition, the analysis would identify knowledge deficits for future research and development. This research could assist educators, researchers, and policymakers utilize AI in music education by conducting a comprehensive literature review. This work can assist in the development of AI-based instruments, the improvement of pedagogy, and the promotion of music education.
Computational Visualization and Informatics Interaction Analysis of Daidzein Compound from Soybean (Glycine max L.) on Maltase-Glucoamylase Protein for Predictive Study of Intestinal Disaccharidase Deficiency Zainul, Rahadian; Elkhool, Tarek A.; Ahmed, Shafique; Goh, Khang Wen; Muhardi, -
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.9.2.3487

Abstract

This study explores the potential of daidzein, a bioactive compound derived from soybean (Glycine max L.), as a maltase-glucoamylase protein inhibitor to address intestinal disaccharidase deficiency, utilizing in silico methodologies. The research supports Sustainable Development Goal 3: Good Health and Well-being by evaluating the binding interactions, physicochemical properties, and therapeutic potential of daidzein. Structural data of daidzein and maltase-glucoamylase were analyzed using PyMOL, PyRx, Protein Plus, and Lipinski’s Rule of Five to predict interaction mechanisms and drug-likeness. The methodological framework consisted of molecular docking and physicochemical analysis, including binding affinity and Root Mean Square Deviation (RMSD) evaluations. The docking results demonstrated strong and stable interactions between daidzein and the target protein, with binding affinities of -2.5 and -2.4 kcal/mol. Additionally, key physicochemical parameters—such as molecular weight (254) and log P (2.713)—indicated favorable drug-likeness and oral bioavailability. RMSD values supported the stability of daidzein within the enzyme’s active site. These findings suggest that daidzein may serve as a promising natural therapeutic agent for digestive disorders associated with enzyme deficiencies. The study also illustrates the efficiency of computational tools in the early stages of drug discovery, reducing reliance on laboratory testing. It is recommended that future research includes in vitro validations and preclinical studies to further assess the safety, efficacy, and pharmacokinetics of daidzein. Structural optimization to enhance target binding is also encouraged. Ultimately, this research contributes to the sustainable development of plant-based therapies for managing non-communicable diseases and improving digestive health.
A Review of Livestock Smart Farming for Sustainable Food Security Zaabar, Liyana Safra; Yacob, Adriana Arul; Nathan, Deventhren Kamala; Hing Yip, Emmerich Wong; Mat Razali, Noor Afiza
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.9.2.2794

Abstract

Maintaining food security via sustainable farming methods is a significant problem as the global population grows. This study aims to examine the impact of smart farming methods on enhancing farm animal output to satisfy rising demand while fostering sustainability. Smart livestock farming incorporates automation, Internet of Things (IoT) sensors, and machine learning algorithms to improve production, efficiency, and resource utilization. With an emphasis on essential factors including automated feeding, environmental monitoring, and health tracking, this study takes a methodical approach to reviewing IoT-based livestock farming. The efficiency of several sensor technologies, including motion, temperature, humidity, and biometric sensors, is examined in gathering data and making decisions in real time. The potential of machine learning methods like pattern identification, anomaly detection, and predictive analytics to maximize the production and health of farm animals is assessed. According to the results, IoT-driven livestock farming improves illness diagnosis, minimizes resource waste, and optimizes feeding practices, increasing production efficiency. These developments minimize the impact on the environment while promoting steady food production. Additionally, less human interference results from automation in livestock production, which lowers costs and improves decision-making. This study demonstrates how smart agricultural technology may be used to address issues related to food security. Further research is needed to increase real-time data processing, hone machine learning models, and investigate affordable options for broadly adopting these ideas into practice. Livestock management may be transformed, guaranteeing a robust and sustainable agricultural environment.

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