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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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Mapping The Relationship Between Virtual Reality and Bullying Prevention: An Analysis Of Bibliographic Coupling Maryaeni, Maryaeni; Nastiti, Vinna Rahmayanti Setyaning; Oktaviani, Chintya Tria Diana; Reikisyifa, Clarissa Sanindita; Kenanga, Larynt Sawfa; Kusuma, Wahyu Andhyka; Wahyuni, Evi Dwi
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.9.2.2183

Abstract

Bullying prevention has become a significant topic in contemporary society. The bibliographic analysis method employed is bibliographic coupling, which allows for identifying relationships among relevant scientific articles to understand the role of technology in combating bullying. The research methodology involves identifying previous publications on virtual reality and bullying prevention from Scopus. Bibliographic analysis quantifies relationships between publications based on shared references. The bibliographic data collected focuses on VR-related literature and bullying prevention from various academic sources. This article discusses the interconnections among the reviewed articles regarding the impact of these findings, such as the development of VR applications that can enhance social skills and empathy, which are crucial factors in preventing bullying. Research results indicate a significant correlation between virtual reality and bullying prevention. Relevant prior studies include topics such as using virtual reality to avoid bullying through bystanders and victims, as well as simulating bullying to enhance bystander empathy. These studies provide information on the role of virtual reality technology in effectively combating bullying. Research findings are presented as descriptive and visual analyses using VOS viewer software. Additionally, the article underscores the importance of interdisciplinary collaboration in integrating VR into effective bullying prevention strategies, making us all part of a larger community working towards a common goal. Hopefully, this article will provide a foundation for future research and the development of technology applications with the potential to combat bullying.
Maize Leaf Disease Identification with Large and Lightweight Convolutional Neural Models Lye, Mohd Haris; Fauzi, Mohammad Faizal Ahmad; Lim, Kian Ming
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.9.2.3559

Abstract

To minimize yield losses in maize plantations, control measures that include early leaf disease detection are essential. In this study, we evaluated extensive and lightweight convolutional neural network (CNN) models to accurately classify maize diseases from leaf images. To achieve a high image classification performance, existing deep learning approaches often use large models that require substantial computational resources.  Simpler and lightweight models provide faster inferences but at the expense of lower accuracy in prediction performance. To improve maize leaf disease classification performance on the lightweight SqueezeNet model, the response-based knowledge distillation method was evaluated for model training. In response-based knowledge distillation, the logit output from the last layer of the large model is used in the loss function to train the lightweight model. This enables the lightweight model to learn from the knowledge of large and complex models, thereby improving its predictive accuracy while maintaining a simpler architecture and faster inference. A six-class maize disease dataset was prepared using two publicly available datasets. The dataset was used to train and evaluate the selected large and lightweight models. The large and lightweight model demonstrated high classification accuracy when trained till 40 epochs. The trained SqueezeNet model showed promising performance for accurately identifying various maize leaf diseases with an accuracy of 96.68%. When the model is trained with the response-based knowledge distillation method, the test accuracy improves to 97.13%. Such lightweight models with high accuracy can facilitate the deployment on resource-constrained devices.
A Low-Cost Nursing Robot with Telemedicine using ESP32 and Robot Operating System-based Suharjono, Amin; Apriantoro, Roni; Supriyo, Bambang; Wardihani, Eni Dwi; Yunanto, Bagus; Hidayat, Wahyu; Prasetio, Katon; Reynaldi, Rindang; Fahrul Aji, Achmad
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.9.2.2793

Abstract

The COVID-19 pandemic has presented unprecedented challenges to the healthcare sector, particularly frontline healthcare workers. These professionals face high infection risks and physical and mental exhaustion due to intensified workloads and staffing shortages. Robots are seen as a potential solution to this predicament, performing tasks such as delivering supplies and monitoring patients. However, widespread adoption of such robots, particularly in resource-constrained settings, has been hampered by the exorbitant costs associated with their acquisition and maintenance. To address this problem, the authors developed a low-cost nursing robot based on the ESP32 and the Robot Operating System (ROS). This robot facilitates hospital logistics and patient monitoring through telemedicine. The robot is controlled by remote control or Wi-Fi connection through the RViz Graphical User Interface (GUI) and uses odometry and PID control methods to follow specified paths autonomously. Accessible via local area networks and the Internet, the telemedicine system demonstrates robust performance with minimal X and Y axis control errors, zero packet loss, an average Round Trip Delay (RTD) of less than 150 ms, and jitter values of less than 20 ms, in line with TIPHON standards. This innovation provides a cost-effective solution to support healthcare workers during the ongoing health crisis. In future development, incorporating LiDAR, computer vision, and AI-based decision-making into the robot can facilitate obstacle detection and real-time decision-making to enable fully autonomous movement. These advancements will enhance the robot’s adaptability and accuracy in navigation and positioning.
Elderly Acceptance of Autonomous Vehicles in Malaysia: An Extended Technology Acceptance Model with Multidimensional Trust and Perceived Risk Ho, Jen Sim; Tan, Booi Chen; Lau, Teck Chai; Khan, Nasreen; Pang, Suk Min
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.9.2.3363

Abstract

The emergence of autonomous vehicle technology is propagated to address the needs of the elderly and reduce other negative externalities brought by transportation mobility. However, these benefits would not be realized without widespread acceptance. This research aimed to investigate the factors influencing the acceptance of autonomous vehicles among the elderly in Malaysia. Building on the technology acceptance model with multidimensional trust, perceived risks, and technology anxiety, a sample of 289 elderly people within Klang Valley are included in the model estimation. Results show that the mediating roles of perceived ease of use, perceived usefulness, and attitude between trust in institutions and acceptance are not supported. On the other hand, performance trust indirectly affects acceptance through perceived ease of use, usefulness, and attitude. The multidimensional perceived risks, including perceived performance risk, privacy risk, and technology anxiety, did not support the direct effect on acceptance of autonomous vehicles. These findings validate the role of multi-dimensional trusts and perceived risks in accepting autonomous cars. Trust and perceived risk in autonomous vehicles evolve; thus, a longitudinal study is recommended for future studies to understand better the elderly's acceptance of autonomous vehicles in Malaysia as the industry matures. The findings also provide important insight into industry players who design transport policies. Building trust in autonomous cars focusing on reliability and trustworthiness is vital for widespread acceptance, particularly among the elderly.
Incremental Learning Approaches for Dermoscopic Image Classification in Teledermatology Hernanda, Arta Kusuma; Asayanda, Fikra Agha Rabbani; Ait-Souar, Iliès; Rachmadi, Reza Fuad; Purnama, I Ketut Eddy
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.9.2.3016

Abstract

This study investigates the application of incremental learning techniques to enhance the classification of skin diseases in dermoscopic images. The research aims to develop a model capable of continuous adaptation to new data while retaining previously acquired knowledge. Two datasets were utilized: acne images and the HAM10000 dataset comprising various skin lesions. The methodology involved initially training a ResNet-18 convolutional neural network on 1,052 samples across eight classes, followed by an incremental learning phase incorporating 800 additional data points. Rigorous preprocessing steps were implemented to ensure data quality, including cropping, resizing, and normalization. Results demonstrate that the base model achieved 87% accuracy on the test set, which improved to 90% after the incremental learning process. Detailed analysis revealed significant improvements in precision, recall, and F1-scores for several skin disease classes, notably for challenging categories such as Basal Cell Carcinoma (bcc) and Dermatofibroma (df). Confusion matrix analysis and Grad-CAM visualizations provided insights into the model's decision-making process and its focus on clinically relevant features. The study also implemented a Streamlit application to demonstrate real-time classification capabilities and the system's adaptability in a simulated clinical environment. These findings have potential clinical applications, particularly in teledermatology systems where adaptive algorithms can accommodate new dermatological data over time. The study highlights the potential of incremental learning in creating accurate, adaptable, and clinically relevant AI models for skin disease classification in evolving medical practices.
Awareness of Cybersecurity: A Case Study in a Logistics Organizations Chuen. L, Chen; Jamaludin, N.A.A.; Abdul Rauf, U.F.; Awang, N.F.
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.9.2.3349

Abstract

Sweeping digital transformation provides the logistics and supply chain industry with technology to overcome disruptive norms and simultaneously attract cybercriminals with opportunities and threats to infiltrate organization data. Cybersecurity and information security are crucial platforms for logistics and supply chains as they are required from time to time to keep up with the evolving cyber threats, and the best way to achieve such resilience is through a cyber-aware workforce. This study aims to understand employees’ cybersecurity awareness in a logistics organization based on human factors such as employees’ level of awareness, attitude, and knowledge and examine the high and very significant correlation between demographic profiles. An online survey questionnaire was constructed and distributed to employees via web-based tools and physical copy handouts. This approach was used to gather data for the objectives of the study. The participants were divided into the support group and the distribution group, totaling 50 respondents who had completed the survey. Statistical Product and Service Solutions (SPSS) is used to analyze the data gathered from the online survey and physical copies. The method uses descriptive analysis and cross-tabulation to determine the most significant and correlated variables. Based on the results obtained, although most respondents had positive results, it is concerning when a small percentage demonstrates a lack of awareness, knowledge, and a high-risk attitude toward cybersecurity within the organization. As for future implications, it is suggested that a mix of quantitative and qualitative approaches be used to support and increase the reliability of the research.
A Comprehensive Analysis of Break Bulk Port Efficiency Using an Analytic Network Process Model Dinh, Gia Huy; Hoang, Phuong Nguyen; Nguyen, Lam Canh; Le Huu, Bao Tu; Dang Khoa, Pham Nguyen; Thuy Van, Nguyen Thi; Tai, Le Phuoc; Thuy Vi, Vu Thi; Xuan Huong, Nguyen
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.9.2.3881

Abstract

Ports significantly contribute to economic development, especially in developing nations. Although containerization prevails in cargo transportation, break bulk freight remains vital for items that cannot be accommodated in ordinary containers. This paper examines the successful techniques of breaking bulk terminals, concentrating on Vietnam, where these ports are essential to commerce. Employing qualitative and exploratory methodologies within the Entrepreneurial Action Theory (EAT) framework, data from 16 port executives at Vietnam's principal break bulk ports were examined to identify critical success factors, internal competencies, and external influences impacting port performance. The Analytic Network Process (ANP) ranks success factors, emphasizing cargo throughput and ship call stability as paramount, succeeding by profitability, operational efficiency, and customer happiness. External influences, such as the escalation of vessel sizes, port competition, and changes in international trade, profoundly influence port operations, necessitating adaptability and infrastructure investment. Government influence, developments in port technology, labor shortages, and cost changes are significant factors, whereas leadership vision and green initiatives, despite their lower ranking, possess long-term strategic importance. Moreover, internal success variables, including port infrastructure, labor proficiency, and technological integration, are crucial for sustaining efficiency. This study presents a conceptual framework for decision-makers in resource allocation and strategic planning by merging ANP findings with EAT. The findings enhance maritime research by providing insights for optimizing break-bulk operations and guaranteeing resilience in the face of growing industry challenges.
Enhancing The Server-Side Internet Proxy Detection Technique in Network Infrastructure Based on Apriori Algorithm of Machine Learning Technique Maskat, Kamaruzaman; Mohd Isa, Mohd Rizal; Khairuddin, Mohammad Adib; Kamarudin, Nur Diyana; Ismail, Mohd Nazri
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.9.2.3410

Abstract

The widespread use of proxy servers has introduced challenges in managing and securing internet connections, particularly in detecting non-transparent proxies that obscure the originating IP address. Proxy servers, while beneficial for bandwidth management and anonymity, can be exploited for malicious purposes, such as bypassing geo-restrictions or concealing cyberattacks. This study aims to address the gap in identifying proxy usage by providing an organized review of existing detection techniques and proposing a hybrid server-side detection framework. The objectives of the research include identifying and comparing proxy detection methods, developing a hybrid approach using machine learning, and evaluating its effectiveness in enhancing network security. The methodology involves collecting primary data through controlled environments simulating direct and proxy-based connections. A machine learning model, based on the Apriori algorithm, is employed to analyze network traffic patterns and identify characteristics indicative of proxy usage. Attributes such as IP addresses, port numbers, and round-trip times are used to train the model. The proposed framework is tested for its robustness, accuracy, and speed against existing detection methods. The results demonstrate the feasibility of the hybrid approach in improving the detection of non-transparent proxies, particularly those not easily identifiable using conventional techniques. The findings have significant implications for securing server-side infrastructure, aiding in cyber threat mitigation, and enforcing organizational policies. Future research can expand on this framework by testing it against broader proxy types and integrating real-world data to enhance its reliability and scope. This study contributes to advancing cybersecurity practices by addressing a critical challenge in proxy detection.
Causal Inference in Observational Studies: Assessing the Impact of Lifestyle Factors on Diabetes Risk Witarsyah, Deden; Almohab, Hadi; A A Abushammala, Haneen
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.9.2.1295

Abstract

The global prevalence of type 2 diabetes has escalated in recent decades, prompting an urgent need for effective prevention strategies. Physical activity has emerged as a significant modifiable risk factor for mitigating diabetes risk, yet the precise causal relationship remains a subject of debate, particularly in observational studies. This research leverages advanced causal inference methods to rigorously estimate the effect of physical activity on the risk of developing type 2 diabetes. By employing Propensity Score Matching (PSM), we address confounding biases inherent in observational data, ensuring more reliable estimates of treatment effects. Additionally, we integrate machine learning techniques, including causal forests, to explore heterogeneous treatment effects (HTEs) across different population subgroups. Our findings highlight that the benefits of physical activity in reducing diabetes risk are not uniform but are more pronounced among individuals with higher body mass index (BMI), further underlining the necessity of tailored interventions. The application of advanced causal inference models allows us to account for confounders such as diet, socioeconomic status, and pre-existing health conditions, offering a more comprehensive understanding of the relationship between physical activity and diabetes prevention. This study contributes to the growing literature by demonstrating that physical activity significantly reduces diabetes risk, with particular benefits for high-risk subgroups. Our findings provide evidence for public health policies that emphasize physical activity as a cornerstone of diabetes prevention, promoting individualized approaches to intervention.
Exploring the Role of Machine Learning and Big Data Analytics in Enhancing Decision-Making Processes: A Systematic Literature Review Prawira, Nicholas; Wella, -; Natalia, Friska
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3244

Abstract

This Systematic Literature Review (SLR) analyzes the influence of Machine Learning (ML) and Big Data Analytics (BDA) on decision-making processes in several industries. The study aims to explore the potential of machine learning and big data analytics in enhancing decision-making, examining the tools and platforms used, and identifying the challenges encountered during deployment. Employing the PRISMA technique, 31 publications published from 2019 to 2024 were meticulously selected through a stringent screening process, using Scopus as the principal database. The results indicate that machine learning and big data analytics substantially enhance predictive accuracy, operational efficiency, and data privacy measures, while facilitating seamless integration with current systems. Furthermore, these technologies are becoming progressively accessible to Small and Medium Enterprises (SMEs). In the healthcare sector, machine learning models have exhibited a diagnosis accuracy of 99% in detecting breast cancer. Nonetheless, the report underscores other research deficiencies, particularly the necessity for more cost-effective solutions designed for SMEs. These limitations signify opportunities for future study to investigate ML and BDA applications in underexamined areas, such as logistics and manufacturing. This research highlights the necessity of creating economical, scalable, and industry-specific machine learning and big data analytics solutions to address existing difficulties. This systematic literature review (SLR) seeks to elucidate the function of machine learning (ML) and big data analytics (BDA) in decision-making, thereby assisting researchers and practitioners in enhancing the utilization of these technologies across many industrial applications.

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