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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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The Impact of Online Learning on NDUM Students During COVID-19 Iskandar, Sari Nashikim Radin; Adib, Mohammad Khairuddin; Isa, Mohd Rizal Mohd; Ali, Sharifah Aishah Syed; Shukran, Mohd Afizi Mohd; Maskat, Kamaruzaman
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1238

Abstract

One of the impacts of the COVID-19 outbreak was the closure of numerous education facilities, including schools and universities. Due to the closing of these institutions, the method used for teaching and learning changed from physical face-to-face lecturing to online contactless learning. This helps curb the spread of infections while ensuring that teaching and learning continue as usually as possible. However, questions arise not only about the effectiveness of online learning but also about the impact of online learning on education stakeholders, namely students and educators. This study aims to assess the effects of the lockdown during COVID-19 on National Defense University of Malaysia (NDUM) students. A link pointing to a custom-built questionnaire was forwarded to students through email and WhatsApp. At the end of the survey period, 445 students responded to the questionnaire. The simple percentage distribution was employed to evaluate the student's learning status and their expectations. Based on the analysis, during the lockdown, students faced issues involving technical, time management, social interactions, and surrounding (home-related) issues. In contrast, during the lockdown, students were also keen to learn new technological skills and favorable towards the ability to replay lectures and class materials. These valuable insights on the impact of online learning on students are essential due to the advancement of technology in education, not only in Malaysia but in other nations as well.
Comparative Analysis of the Implementation of Technology Trends, Pedagogy Trends and Education Trends of Science and Non-Science Program Students in Sulawesi Patmasari, Andi; Ahmar, Dewi Satria; Anggreni, Afrillia; Azzajjad, Muhammad Fath; Ningsih, Purnama; Ahmar, Ansari Saleh
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.8.3-2.3213

Abstract

As a result of their increased exposure to technology, today's students are skilled users of a wide range of digital gadgets in their daily lives. To solve difficulties, they can freely access knowledge across a variety of digital platforms; this ability has to be included into contemporary learning principles. The purpose of this study is to investigate how lecturers might modify innovative teaching methods to better suit the needs of their students. 125 people participated in the survey that we did; 59 of them were from scientific programs and 66 were from non-science programs. The study used observation sheets, interview guides, and questionnaires. The questionnaire was split into two sections: one measured students' opinion of the technology and pedagogical innovations used by lecturers, and the other their reactions to the classroom and educational system. Before any data was collected, the validity and reliability of the instruments were confirmed. The findings showed that students had preferences for different types of technology. Students in scientific programs liked interactive platforms like Edmodo and Google Classroom, while students in other programs liked webinars and video conferences. Additionally, the study found a relationship between the educational trends that lecturers apply, pedagogical innovation, and the learning environment. The results of this study are anticipated to improve instructional practices in digital learning settings and provide a basis for policymaking in continuing education. 
The Rewardable Persuasive Model: A Mobile Exergame Conceptual Design Model that Facilitates Youths to Exercise through Mobile Gaming Hashim, Hasdina Lynn; Kamaruddin, Azrina; Jantan, Azrul Hazri; Sulaiman, Puteri Suhaiza
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.8.3-2.3461

Abstract

This study seeks to assess current design models to propose a conceptual design model for a mobile exergame that promotes physical activity among youths. Encouraging youths to engage in physical activity rather than remaining sedentary is essential due to the numerous health benefits associated with regular exercise. Exercise is a subset of physical activity structured for fitness. Exergames have the potential to inspire youths to be active by combining exercise and gaming engagingly and enjoyably. The proliferation of mobile gaming has further broadened the availability and accessibility of exergames. In this study, we conducted four stages of information gathering to assess and propose a mobile exergame conceptual design model: a literature review, a user survey, expert reviews, and in-depth user interviews. Based on the study's findings, we identified appropriate components and their rationale by adapting an existing design model to our conceptual design model for a mobile exergame. This conceptual design model is called the Rewardable Persuasive Model (RPM). This model aims to help youths achieve their weekly physical activity targets using an engaging and functional mobile application (app). The app incentivizes exercise by integrating it as a key element for unlocking gameplay. With the introduction of these components, an exergame can be designed to engage youths and facilitate physical activity effectively. In a future study, youths will use an app to assess the RPM's effectiveness over time. This assessment will ascertain its appropriateness for facilitating exercise during inactivity.
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation Vu, Van Vien; Le, Phuoc Tai; Do, Thi Mai Thom; Nguyen, Thi Thuy Hieu; Tran, Nguyen Bao Minh; Paramasivam, Prabhu; Le, Thi Thai; Le, Huu Cuong; Chau, Thanh Hieu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2641

Abstract

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.
A Novel Information Hiding Approach using Selective Quantization Technique in Video Coding Hau, Joan; Tew, Yiqi; Tan, Li Peng
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.8.3-2.3029

Abstract

This research examines the different areas of information hiding in current and emerging video compression standards. In the subsequent sections, we provide a detailed comparison of these techniques based on partition modes, prediction units, transform coding, and syntax elements. It shows the engineer and the reader that none of the methods are perfect but are the best for selected applications. We also consider the new video coding standards that have recently appeared, H.266/Versatile Video Coding (VVC) and H.265/High-Efficiency Video Coding (HEVC) and stress the fact that information hiding is critical in attaining such high compression efficacy. To facilitate the reader's understanding of all the relative information, the table that provides the analysis of each technique is presented in the form of a simple listing containing information about each technique's advantages, disadvantages, impacts, and practical applications. The current resource is intended to assist researchers and practitioners in optimizing information hiding for improved video compression. The study's outcome can contribute to enhancing knowledge of information hiding and the new developments of information hiding in video compression beyond what current research offers now, as well as provide a foundation for fresh advances in the field. Further, it is introduced to selective quantization techniques as the approach to information hiding. This method also minimizes this distortion while putting the information into the compressed stream. Finally, we evaluate the performance of this introduced approach towards information hiding capacity and maintaining video quality, with the potential to inspire further research and development in the field.
Development of a Predictive Model for Citrus Shipments and Prices, and Analysis of Influencing Factors Kim, Seongyul; Seo, Yun Am
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.9.3.4245

Abstract

Given the significance of the citrus industry, which accounts for more than half of Jeju Island's agricultural revenue (KRW 950.8 billion, 55.92% of farming income), this study aims to develop prediction models for open-field and greenhouse-grown citrus shipment volumes and prices. While previous research has explored crop production forecasting, there is a notable absence of comprehensive studies integrating deep learning approaches with environmental factors for Jeju citrus prediction, particularly in addressing the complex interplay between weather patterns and market dynamics. To bridge this gap, this study analyzed various domestic and international factors, including weather information, public holidays, and imported fruit data, which were utilized as independent variables in the model design. Deep learning-based models, specifically LSTM for capturing long-term dependencies, Seq2Seq for handling variable-length sequences, and Attention mechanisms for focusing on relevant temporal patterns, were employed to perform the predictions. Their accuracy and stability were thoroughly evaluated against traditional machine learning benchmarks. The findings revealed that citrus shipment volumes and prices are significantly influenced by temporal factors (average temperature, shipment timing) and market dynamics (transaction volume, competing fruit prices), with the Seq2Seq model achieving the highest prediction accuracy. Furthermore, by adjusting the window sizes in various time series models, we were able to simulate different scenarios, providing stakeholders with a robust tool for market planning and decision-making. The findings of this research are expected to contribute to the efficient operation of the citrus market and the maximization of benefits for related stakeholders.
Artificial Intelligence Adoption on Investment Platform for Robo Advisory Users in Indonesia Fahruri, Arief; Rusmanto, Toto; Warganegara, Dezie Leonarda; Tjhin, Viany Utami
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.9.3.2842

Abstract

Robo-advisors provide an alternative financial solution tailored for regular clients. Beyond the acceptability of technology, financial factors significantly influence the adoption of robo-advisors. While existing studies extensively discuss the stages involved in the intention to utilize robo-advisors, only a few offer insights into financial capabilities. The purpose of this study is to investigate the extent to which Indonesian investors embrace robo-advisors by incorporating financial variables such as financial goals and financial literacy into the technology adoption in Robo Advisor. Additionally, the study explores the relationship between application costs and data privacy on the adoption of robo-advisor technology. This research employs a quantitative approach using purposive sampling techniques. Data were collected through a survey of 431 robo-advisor users and analyzed using SmartPLS. The findings reveal a significant and positive correlation between financial goals, perceived technology usefulness, and application costs in the adoption of robo-advisors in Indonesia. These results contribute to the development of investment decision theory using technology-based approaches, specifically robo-advisors. Furthermore, companies in the financial sector, particularly in wealth management or investment management, can benefit from incorporating financial goal features, enhancing technological performance, and setting competitive fees to increase adoption rates. Future research should further explore robo-advisor adoption, focusing on additional financial variables and financial behaviors that drive technology adoption as an investment decision. These findings highlight the importance of considering both financial and technological factors in promoting the use of robo-advisors among investors especially in Indonesia.
An Automated Fingerprint Image Detection and Localization Approach-based Unsupervised Learning Algorithms using Low-quality Biometrics Plam Data Jadaan Abed, Abdulrasool; Abdulhadi Abdullah, Dhahir
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.8.3-2.1665

Abstract

In this study, fingerprint identification and classification of low-quality fingerprints have been analyzed accordingly.  As technology advances and methodologies evolve, staying at the forefront of research and innovation is imperative. The challenges addressed in this paper provide a foundation for future investigations and underscore the importance of developing resilient and adaptable biometric systems for real-world applications. The quest for accurate, efficient, and robust fingerprint identification in adverse conditions is a testament to the continuous evolution and refinement of machine learning and deep learning approaches in biometrics. While deep learning models exhibited improved performance, it is essential to acknowledge the need for further research and development in this domain. Additionally, integrating multimodal biometric systems and combining fingerprint data with other biometric modalities might present a viable avenue for mitigating the limitations associated with degraded fingerprints. In this paper, we develop a fingerprint identification approach for low-quality fingerprint images. The success rate accuracy of the propped algorithm for the low-quality fingerprint images should be significantly better than that of the standard local minutia approach. The main design of our deep learning approach is based on detecting and extracting the primary correlation during the training and using the correlation feature map to calculate the distance between the low-quality fingerprint images during the predicting phase. The experimental results show a very promising repulsing and high prediction accuracy.
Enhanced BatikGAN SL Model for High-Quality Batik Pattern Generation Minarno, Agus Eko; Akbi, Denar Regata; Munarko, Yuda
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.9.3.3096

Abstract

Batik represents one of the most prominent traditional cultural forms in Indonesia, serving not only as an art form but also as a symbol of cultural identity and heritage. The creation of intricate and unique Batik patterns is a highly skilled craft that has been passed down through generations. Still, modern efforts to innovate and enhance Batik designs face significant challenges. Specifically, there is a growing demand for high-quality Batik patterns that maintain the aesthetic and cultural value of traditional motifs while incorporating modern design elements. This study aims to address these challenges by introducing an enhanced BatikGAN SL model that leverages local features. The model's performance was rigorously evaluated using the Batik Nitik dataset, which consists of 126 Batik motifs collected from artisans in Yogyakarta, a region renowned for its rich Batik traditions. This dataset allowed for a robust testing ground, representing a diverse array of motifs and styles. In comparative evaluations, the enhanced BatikGAN SL model outperformed not only its predecessor but also models utilizing histogram-equalized datasets, which are often employed to improve image contrast. Key metrics, including the Fréchet Inception Distance (FID) score of 20.087, Peak Signal-to-Noise Ratio (PSNR) of 25.665, and Structural Similarity Index Measure (SSIM) of 0.918, demonstrated significant improvements in both the visual and technical quality of the generated Batik patterns. These metrics indicate that the proposed model excels in producing patterns with more precise details, better contrast, and higher overall image fidelity when compared to previous approaches.
Performance Evaluation of a Simple Feed-forward Deep Neural Network Model Applied to Annual Rainfall Anomaly Index (RAI) Over Indramayu, Indonesia Herho, Sandy Hardian Susanto; Irawan, Dasapta Erwin; Fajary, Faiz Rohman; Suwarman, Rusmawan; Kaban, Siti Nurzannah
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.9.3.1984

Abstract

Indramayu is a district in West Java that is known for being the leading producer of rice and brackish salt. The production of these two commodities is strongly influenced by hydroclimatological conditions, making accurate and reliable long-term estimates crucial. In this study, we evaluated a simple feed-forward deep neural network (DNN) model that could potentially be used as a candidate for statistical guidance to improve the accuracy of a mesoscale numerical climate model. We used the spatial average of the accumulated annual rainfall of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data as an input time series with a time range from 1981 to 2022. This data was then processed into annual rainfall anomaly index (RAI) data. The Annual RAI was divided into training and test sets, and the feed-forward DNN model was fitted to the annual RAI in the training set. The accuracy of the model was then tested in the test set using the root-mean-square error (RMSE) metric. Our study shows that the feed-forward DNN model is unsuitable for estimating the annual RAI over Indramayu. The RMSE values are significantly high in the training and test sets.

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