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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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Enhancing Weather Prediction Models through the Application of Random Forest Method and Chi-Square Feature Selection Irmanda, Helena Nurramdhani; Ermatita, Ermatita; bin Awang, Mohd Khalid; Adrezo, Muhammad
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.8.3-2.2356

Abstract

This study discovers weather forecast methodologies, concentrating mainly on the climatic issues faced by Indramayu Regency and its considerable impact on agriculture, specifically rice production and national food security. The study emphasizes the crucial need for accurate weather forecasting, especially in the context of ongoing climate change, by highlighting the region's vulnerability to weather anomalies and their possible disruption of crop output. To solve these issues, the study investigates machine learning techniques, particularly ensemble learning methods such as Random Forest in conjunction with Chi-Square feature selection. The article thoroughly outlines the research approach, including data collection from Indonesia's Meteorology, Climatology, and Geophysics Agency (BMKG), data pre-processing, feature selection processes, and data splitting. Notably, the methodology integrates the Synthetic Minority Over-sampling Technique (SMOTE) to adjust imbalanced data and uses key weather attributes for model construction (humidity, wind speed, and direction). The resulting Random Forest model performs well, with an accuracy rate of 87.6% in forecasting different types of rainfall. However, the study indicates potential overfitting in some rainfall classes, implying the need for additional data augmentation or modeling technique refining. In conclusion, this study demonstrates the potential efficacy of ensemble learning techniques in weather prediction, focusing on the Indramayu Regency. It emphasizes the need for exact forecasts in the agricultural and fisheries industries and suggests possibilities for additional investigation, such as research into alternative prediction approaches such as deep learning.
Developing an AR Navigation System: Bridging Indoor and Outdoor Environments Achmad, Zacky Maulana; Sukaridhoto, Sritrusta; Zainuddin, Muhammad Agus
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3420

Abstract

Navigation systems are designed to assist people or objects in moving efficiently and accurately from one place to another by providing directions to reach a destination. There are two main types of navigation systems: Indoor Navigation, which involves navigation within indoor environments, and Outdoor Navigation, which is used in outdoor or open environments. Both have drawbacks, such as signal limitations in multi-floor buildings and hardware requirements. This study focuses on developing a Seamless Indoor-Outdoor Navigation System that integrates indoor and outdoor navigation within a single application, allowing users to seamlessly transition between environments without switching applications or requiring additional hardware. The development uses AR technology and Immersal, overlaying digital content such as 3D pinpoints and 3D paths onto the real world through smartphones to show destinations to users. Immersal SDK adds real-world location mapping, application development, and localization for indoor and outdoor environments. The system was implemented at the PENS Campus, and testing was conducted, including: 1) Navigation Testing, which demonstrated efficient route visualization in the D3, D4, and S2 buildings and PENS Road. 2) User testing with the PIECES Framework, involving 35 respondents, showed high satisfaction with a top score of 4.49 for Information. 3) Indoor-outdoor integration Testing confirmed the system’s success in navigating between environments. 4) Multilevel Floor Navigation Testing demonstrated its ability to navigate multi-floor buildings, 5) Software Testing showed the system's performance met targeted frame rates of 30 FPS for Android and 60 FPS for iOS devices.
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.
Vulnerability Liquefaction Mapping in Padang City Based on Cloud Computing Using Optical Satellite Imagery Data Razi, Pakhrur; Putri, Amalia; Sri Sumantyo, Josaphat Tetuko; Akmam, -
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.8.3-2.3480

Abstract

Liquefaction is a significant geological hazard in earthquake-prone locations like Padang City, Indonesia. The phenomenon happens when saturated soil loses strength owing to seismic shaking, resulting in substantial infrastructure damage. Accurate identification of sensitive locations is critical to catastrophe mitigation. This study aims to map water distribution using optical satellite data and estimate its importance as a crucial element in determining liquefaction vulnerability. The Normalized Difference Water Index (NDWI) was used to assess water and vegetation indexes, taking advantage of its sensitivity to water content in varied land surfaces. We recommended using the NIR (near-infrared) and SWIR (short wave infrared) bands with 832.8 nm and 2202.4 nm, respectively, which are sensitive to soil water content. High-resolution satellite data were used to create NDWI maps, highlighting locations with high water saturation. These findings were combined with geological and seismic data to identify liquefaction-prone zones. The study found that locations with high water content, as measured by NDWI, are highly associated with greater liquefaction susceptibility. The findings highlight the importance of water distribution in determining soil behavior during seismic occurrences. This study highlights the value of NDWI as a low-cost and efficient tool for measuring liquefaction vulnerability at the regional level. The technique offers insights into Padang City's urban planning, catastrophe risk reduction, and community preparedness. By identifying high-risk zones, the study aids in making informed decisions to reduce the impact of future earthquakes. Most of the water content change occurred along the coastal line and in the low-lying areas of Koto Tanggah and North Padang sub-districts. The model can be used in other places with similar geological challenges, providing a scalable solution for liquefaction risk assessment.
An Effective Open ERP System for Automation in Financial Reporting for SMEs based on Service Oriented Architecture Saputra, Muhardi; Fadlila, Rafa Fadlila
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2367

Abstract

Small-Medium Enterprises (SMEs) are currently in high demand and highly developed in many countries. However, several SMEs still do not yet have an information system to support their business processes. This causes the absence of a system that helps integrate data from each process and sector in SMEs. All data exchanges and transaction report generation are done manually and recorded using physical documents that reduce effectiveness and increase costs. This research uses the accounting module in Open ERP or Odoo Version 11.0 software and Service Oriented Architecture (SOA) methods to design ERP systems for general types of SMEs in the finance sector, specifically on the sales and purchase process. So the solution for recording financial transactions and making financial reports for SMEs is to design an ERP system based on Open Source using the Service Oriented Architecture methodology, namely Smart SMEs, by implementing the Automatic Reporting feature to create best practices on an integrated system, especially in recording transactions in the sales and purchase processes so that financial reports can be generated automatically and are real-time. The result of this research is the design of ERP systems in the finance section connected occurs especially in the financial recording of the purchase of goods, the sale of goods, and the creation of financial statements automatically. This system can help the finance sector record transactions and make automatic financial reports that can be generated in real-time.
Analysis of the Alignment of Bauran System Features Based on Outcome-Based Education Rules Using Feature-Oriented Domain Analysis Wicaksono, Galih Wasis; Saleh, Abd; Wahyuni, Evi Dwi; Setiawan, Erwin Budi
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.8.3-2.2895

Abstract

Implementing information systems in higher education curriculum design is a crucial tool for academics, enabling them to design, develop, and evaluate the curriculum more dynamically, responsively, and structurally. However, it is not just about having a tool. It is about ensuring that the tool aligns with curriculum design standards. This study, therefore, measures and analyses the conformity of the Bauran system as a curriculum management information system with the established stages and standards of curriculum design. The analysis is based on the Indonesia National Standards for Higher Education (Standar Nasional Pendidikan Tinggi (SN DIKTI)) by referring to the Guidebook for Higher Education Curriculum Development in Indonesia and best practices in the implementation of Outcome-Based Education (OBE) curriculum design. The method used in this research is feature-oriented domain analysis (FODA), which includes context analysis, domain modeling, and architecture modeling. Experts in the field of OBE curriculum then validate the results of feature measurement and mapping. The study compares 27 Bauran features to 10 stages in the curriculum design guidebook and nine stages in the OBE curriculum design flow. The analysis results show that the Bauran system has implemented 10 out of 10 stages (100%) of curriculum design according to the curriculum design guidebook. However, Bauran has only implemented 8 out of 9 stages (89%) in the OBE curriculum flow. These findings not only provide feature recommendations for future Bauran development and other higher education curriculum management systems but also highlight the potential of the Bauran system for future development.
Trends in Research on the Digital Divide among Disadvantaged Groups in South Korea: A Systematic Literature Review Go, HakNeung; Lim, Suhun; Kim, Seong-Won
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.9.3.4214

Abstract

As digital technology has advanced, the digital divide is of growing concern, with disadvantaged groups with limited access to digital resources and skills disproportionately affected. This divide exacerbates social and educational inequalities, making it increasingly important to understand its scope and implications. While numerous studies have examined digital disparities within specific populations, there has been insufficient comprehensive analysis of research trends. To address this gap, this study systematically reviews trends in research on the digital divide from 2001 to 2024 by focusing on publication trends, research methodologies, research topics, target populations, and the inclusion of disadvantaged groups. This study analyzes academic publications from 2001 to 2024, categorizing research by method, topic, and target population. A frequency analysis was conducted to identify key trends and assess the extent to which disadvantaged groups were included. The findings indicate a sharp increase in digital divide research after 2020, with a growing emphasis on disadvantaged groups. Quantitative and qualitative approaches were used in nearly equal proportions, while studies on awareness and perception dominated. However, impact analysis and intervention studies remain scarce. Elementary and middle school students were the most frequently studied groups, while university students and adults were underrepresented. Among disadvantaged groups, economic factors have been the most studied, while physical and sociocultural factors have received less attention. This study underscores the importance of broader inclusion of disadvantaged populations and a greater emphasis on policy-driven and intervention-based research to bridge the digital divide. By identifying key research trends, this study offers valuable insights for future research and informed policy development in digital inclusion efforts.
Clustering Analysis of Food Security, Waste and Loss: Malaysia Agricultural Insights Chen Sheng Hii, Enoch; Lim, Siew Mooi; Loo, Seng Xian; Yeap, Sheng Kit; Tan, Ching Yee
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.8.3-2.3152

Abstract

As the world population grows rapidly nowadays, the demand for food has come to rise. The escalating demand for food has caused substantial wastage and loss, which not only hampers food security efforts but also aggravates greenhouse gas (GHG) emissions, intensifying the environmental crisis. Among numerous countries, Malaysia, with its diverse agricultural profile, emerges as a good fit for our case study. This study chooses the clustering technique to examine food sector data in Malaysia and investigate the link between the clustering results on food data and the data on GHG emissions. This case study aims to find crops depending on their production efficiency, underline those that match major waste, and estimate their contribution to greenhouse gas emissions. Three clustering techniques, Gaussian Mixture Modelling (GMM), Birch, and Density Peak clustering, are applied in the Production and Supply Utilisation Accounts (SUA) datasets, help to identify and cluster crops based on their similar traits to acquire uncovered patterns between the food sector and environmental issues. Using cutting-edge clustering algorithms and visualization tools, this study investigated in-depth the complex interactions among food production, waste, and greenhouse gas emissions in Malaysia. By addressing food production efficiency and waste reduction, the outcome will be a cascade of benefits that not only improve food security but also help to lessen negative environmental effects. This study illuminates the multifaceted dynamics of food production, waste, and environmental impact, offering valuable insights and pathways toward a more sustainable future for Malaysia and potentially other nations.
Virtual Reality in Algorithm Programming Course: Practicality and Implications for College Students Dewi, Ika Parma; Ambiyar, -; Mursyida, Lativa; Effendi, Hansi; Giatman, Muhammad; Efrizon, -; Hanafi, Hafizul Fahri; Ali, Siti Khadijah
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.8.3-2.3113

Abstract

Virtual Reality (VR) is one of the revolutionary technologies in education that can simulate learning interactively and realistically. The reliability of VR supports various variations in learning, including learning programming algorithms. This research aims to design and develop VR in learning programming algorithms. The ADDIE model is applied in this study to develop products through the stages of analysis, design, development, implementation, and evaluation.  The analysis of learning problems shows the unavailability of interactive learning media that can support various learning styles of students in programming algorithm materials. Therefore, this VR learning design integrates various elements of students' learning styles, including visual, audiovisual, kinesthetic, and reading.  The product developed is in the form of a VR application that presents learning by having a variety of learning styles. Audiovisual displays are presented in a VR environment, while kinesthetic interactions are obtained through user movements, and material information and readings are presented in a VR environment.  The data collected included the results of a practicality and effectiveness test involving sixty-six students. The results of the practicality test show that this VR product is practical to use based on the indicators of convenience, attractiveness, efficiency, and benefits. Meanwhile, the effectiveness test shows that this VR product can improve learning outcomes better than traditional learning methods.  Overall, the VR products developed have proven to be practical and effective in learning programming algorithms. The presence of VR, which presents a variety of realistic displays in a virtual environment, opens opportunities for further research related to distance learning through VR technology as well as an analysis of the impact of VR on health.
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.

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