cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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 51 Documents
 2   3   4   5   6 
Search results for , issue "Vol 9, No 4 (2025)" : 51 Documents clear
Enhancing Coffee Marketing Strategies through Multi-Criteria Decision-Making Andryana, Septi; Wahyuddin, Mohammad Iwan; Rahman, Ben; Putra, Abdul Rahman Wijaya; Mantoro, Teddy
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.9.4.3282

Abstract

Coffee is a globally preferred beverage, and Indonesia, as a major supplier, provides a wide variety of high-quality coffee varieties with unique characteristics from each region. East Manggarai, East Nusa Tenggara, Indonesia, produces Colol coffee, a high-quality variety with unexplored market potential. The marketing of Colol coffee faces significant challenges, including limited accessibility, lack of market information, and inadequate logistics infrastructure. A comprehensive marketing strategy necessitates the consideration of numerous criteria, which generate a range of alternative decisions to identify the marketing area. This study proposes a framework to optimize the marketing strategy of Colol coffee using the MCDM (Multi-Criteria Decision-making) approach, which integrates AHP, SMARTER, and TOPSIS methods. This framework is applied to rank marketing areas in 38 provinces in Indonesia based on five criteria, namely, accessibility, market potential, logistics, environmental conditions, and safety. The results show that the MCDM method can increase the effectiveness of marketing strategies. The top three alternative coffee marketing regions are Papua, East Kalimantan, and South Papua, with eigenvalues of 0.0569, 0.0424, and 0.0421. With incomplete data, in some marketing areas, it is a challenge to integrate multiple MCDM methods to have a better ranking that represents the real world of marketing strategy. This study supports the enhancement of the digital economy in the agricultural sector. It provides a meaningful understanding of the application of MCDM in marketing agricultural products, with far-reaching implications for marketing strategies in similar sectors.
Enhancing Relational Database Efficiency through Algorithmic Query Tuning in Virtual Memory Systems Yulis, Nurlina; Ilham, Amil Ahmad; Achmad, Andani; Samman, Faizal Arya
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.9.4.2850

Abstract

The rapid evolution of virtual memory-based relational database systems has significantly advanced data processing capabilities. However, the efficiency of these systems largely depends on query execution optimization, which can be enhanced through algorithmic query tuning techniques. This study investigates the impact of these techniques on enhancing query performance in virtual memory-based relational databases. Various algorithmic methods were analyzed to optimize query execution plans, with a focus on key performance indicators such as execution time, CPU and memory usage, disk I/O, and cache hit ratio. The systematic application of these methods revealed effective strategies for performance enhancement. Results show substantial improvements in execution time, resource utilization, and scalability. This work offers valuable insights for database administrators and system architects, highlighting the role of algorithmic query tuning in managing the growing demands for data processing. Future research endeavors should explore the realm of AI-driven automation, with a particular focus on enhancing query optimization techniques. Additionally, there is a pressing need to investigate advanced security measures that safeguard data integrity within expansive, large-scale systems. By adopting innovative approaches, we can ensure robust protection and efficient performance in an increasingly data-driven world.
Developing a Pre-Implementation Model for the ERP Material Management Module in the PVC Supply Industry in Indonesia Luis, -; Sutomo, Rudi
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.9.4.2915

Abstract

Rapid technological advancements have transformed the business landscape by introducing systems that enable more efficient and integrated operational management. One increasingly popular solution is Enterprise Resource Planning (ERP), which helps companies comprehensively manage business activities. However, many companies still face challenges in adopting ERP technology, particularly in managing inventory effectively and efficiently. One such company is PVC Supply Industry, which uses manual inventory recording. This research addresses the issues faced by the PVC Supply Industry as a case study to analyze pre-implementation ERP before transitioning to ERP implementation. The study begins with a literature review to identify 20 relevant ERP and inventory management indicators using the Semantic Scholar database. These 20 indicators were then included in a questionnaire distributed to individuals or groups with experience or understanding of ERP/IT. The responses were analyzed using the SmartPLS application, utilizing the IS success model, and categorizing the 20 indicators into four areas: people, process, technology, and ERP implementation readiness. The research results will provide four dominant indicators from these areas based on the SmartPLS analysis. The dominant indicators are P3 (human resource management), PC4 (material requirement planning), TC3 (prototype), and RE (ERP system implementation). These indicators will be applied to a Figma prototype using the prototyping method. The Figma prototype is designed to offer a rough user interface of the ERP material management module to address the issues encountered by the PVC Supply Industry as a case study.
A Study on the Development of Data Literacy Content Framework for Elementary School Students Moon, Hyunwoo; Go, HakNeung; Kim, Seong-Won; Lee, Youngjun
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.9.4.4385

Abstract

This study aimed to develop a data literacy content framework for elementary school students to establish a foundation for systematic data literacy education. The research was conducted through a literature review and analysis of the 2022 Revised National Curriculum of Korea. Based on Ridsdale et al. (2015)'s framework, components of data literacy suitable for elementary students were derived, and curriculum achievement standards related to data literacy were analyzed to develop the content framework. The research identified eight key components of data literacy: understanding data, data collection, data evaluation, data organization, data analysis, data visualization, data-driven decision-making, and data ethics. Curriculum analysis revealed that science (36.3%) and social studies (32.7%) subjects contained the highest proportion of data literacy elements, with grades 5-6 (63.2%) including more achievement standards than grades 3-4 (36.8%). The developed framework is categorized into three domains: knowledge and understanding, processes and skills, and values and attitudes. It considers grade-level hierarchy by focusing on basic concepts and simple functions for grades 3-4, while emphasizing complex concepts and higher-order functions for grades 5-6. This study contributes to supporting systematic data literacy education in elementary schools by providing a content framework that considers students' cognitive developmental stages and is expected to foster future core competencies through practice-centered education. Further research is needed to verify practical applicability, develop teaching and learning methods, and strengthen connections between school levels.
A Review of Technology Acceptance Model Application in User Acceptance of Autonomous Vehicles Ho, Jen Sim; Tan, Booi Chen; Lau, Teck Chai; Pang, Suk Min
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.9.4.2835

Abstract

Touting the merits of reducing traffic fatalities by eliminating human errors and promoting social benefits, autonomous vehicle technology has been rapidly developed over the last few decades. Using the systematic review approach, this study provides an overview of current studies that apply the Technology Acceptance Model (TAM) in shaping user acceptance of autonomous vehicles. Based on a set of inclusion and exclusion criteria, a total of 16 articles out of 792,364 articles were retained for further analysis. The factors that have garnered the most attention from researchers are the technical and psychological factors, with the most frequent constructs integrated in these studies being perceived ease of use, followed by perceived usefulness, trust, attitude, perceived enjoyment, and perceived innovativeness. This study presents three key findings. The first shows that 36 potential antecedents influencing AV adoption were incorporated into TAM. Excluding the baseline model antecedents, the most studied factors were trust, personal innovativeness, and perceived enjoyment. Trust is widely recognized as a crucial factor in AV adoption and requires longitudinal research, as it is a dynamic element that evolves. The second finding concerns the various causal relationships between the constructs. The results showed mixed outcomes, which may be due to differences in levels of automation examined, geographical contexts, and socio-demographic factors. Third, the gaps identified in Section 4 can guide policymakers, researchers, and automakers in developing effective future strategies and research directions. Ultimately, a deeper understanding of public acceptance can facilitate the safe deployment of AVs on roads, leading to benefits such as improved traffic safety and increased sustainability for society and the economy.  
Comparison of Classification Algorithms in Bamboo Distribution Mapping for Identification of Industrial Supporting Raw Materials Veritawati, Ionia; Maspiyanti, Febri; Mastra, Riadika; Fernando, Erick; Murtako, Amir
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.9.4.3072

Abstract

This study aims to address the challenges in the widespread supply of bamboo raw materials and the lack of coordination between bamboo-producing regions, as well as to conduct a comprehensive inventory and mapping of bamboo resources. In addition, this study also explores the factors that influence the distribution and growth characteristics of bamboo, such as soil type, altitude, and rainfall. The main problems faced in the bamboo industry are the uneven distribution of raw materials and the lack of coordination between regions, which hinder the development of a strong and sustainable bamboo industry value chain. The lack of in-depth information on the ecological factors that influence bamboo growth also exacerbates this situation. The method used in this study involves mapping bamboo potential through aerial photography data collection, which is then analyzed using machine learning technology. The three algorithms used in the classification process are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. The study was conducted in an area rich in bamboo vegetation, especially Bojongmangu District in Bekasi, West Java, Indonesia. From the analysis results, the SVM algorithm showed the best performance with a classification accuracy ranging from 80% to 90%. These results indicate that this method is very effective in mapping bamboo vegetation areas with high precision. This study also identified other variables, such as soil type and altitude, that play a role in bamboo distribution. With this more holistic approach, the study is expected to provide deeper insights into bamboo ecology and improve sustainable bamboo resource management.
Comparative Performance Analysis of YOLO and Faster R-CNN in Detecting Species and Estimating the Weight of Grouper and Snapper Fish Using Digital Images Sidehabi, Sitti Wetenriajeng; Indrabayu, Indrabayu; Warni, Elly; Bake, Sabda Ansari
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.9.4.3121

Abstract

Grouper and snapper fish are widely consumed species with high economic value in the global market. To determine their economic value, identifying the species and estimating the weight are essential in the pricing and quality determination of the traded fish. The commonly used manual methods are often time-consuming and labor-intensive. Based on this, a more effective computer-based method is needed for these repetitive tasks. This research aims to analyze the performance of two commonly used deep learning models, YOLO and Faster R-CNN, in detecting species and estimating the weight of specific grouper and snapper fish. The data used consisted of 2991 samples divided into 18 classes. This data was then augmented using rotate and flip features to create 6843 image samples. A threshold of 0.8 was used in the detection process, meaning objects detected with confidence below 0.8 would be ignored. Once trained, the performance of both models was tested using precision, recall, and accuracy parameters to assess how accurately the models predicted fish species from the input data and Mean Absolute Percentage Error (MAPE) to evaluate the estimation results of the models. There were differences in the quantitative evaluation results between the YOLO and Faster R-CNN models. The YOLO model achieved precision, recall, and accuracy rates of 0.98, 0.98, and 0.96, respectively, while the Faster R-CNN model had precision, recall, and accuracy rates of 0.97, 0.98, and 0.95, respectively. Additionally, the MAPE for weight estimation was 2.42% for image data and 3.66% for video data for the YOLO model. In contrast, for the Faster R-CNN model, the results were 14.62% for image data and 13.59% for video data. Thus, it can be concluded that the YOLO model provides better quantitative evaluation results compared to the Faster R-CNN model.
Advanced Instance Segmentation of Aeroponics Tissue Culture-Based Seeds Potatoes Based on Improved YOLOv8l-small Avisyah, Gisnaya Faridatul; Kurnianingsih, Kurnianingsih; Hidayat, Sidiq Syamsul
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.9.4.3085

Abstract

To improve agricultural production, this study develops an advanced instance segmentation system for aeroponic tissue culture-based potato seedlings. We present an IoT system that integrates multiple sensors for humidity, temperature, pH, and turbidity to enable real-time monitoring. Additionally, we adapt the YOLOv8l-small computer vision model, an optimized version of YOLOv8, designed explicitly for efficient potato leaf disease detection and segmentation, even in resource-constrained IoT environments. YOLOv8 is a significant advancement in the YOLO series, for instance, segmentation, combining better accuracy, efficiency, and flexibility. YOLOv8 outperforms previous methods in generating precise segmentation masks while maintaining real-time performance. These innovations make YOLOv8 a robust choice for a variety of computer vision tasks, including instance segmentation, in both research and practical applications. When tested on a custom dataset of potato leaf pictures, the suggested model produced mask mAP50 of 0.842 and mAP50-95 of 0.566, with a model size of 36.1 MB and an inference duration of 9.3 ms. These outcomes are similar to those of the original YOLOv8l model, which had a slower inference time of 11.0 ms and a much larger model size of 92.3 MB, albeit at the expense of a somewhat higher mAP50 of 0.843. The study concludes that the proposed model provides similar accuracy with greater computational efficiency, making it ideal for IoT-based agricultural systems. Future research will explore additional aspects, while practical experiments aim to reduce labor costs.
The Effects of Design-Thinking-Based AI Education Programs Utilizing Generative AI on Korean Middle School Students’ Creative Problem-Solving Ability Kim, Seong-Won; Lim, Suhun; Hong, Seung-Ju; Lee, Youngjun
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.9.4.4378

Abstract

With the rise of digital transformation, fostering AI talent has become essential. Design thinking is gaining attention in AI education for enhancing creative problem-solving and facilitating a structured approach to problem-solving. A key stage in design thinking is Empathize, which involves a deep understanding of the problem. However, educational constraints often limit meaningful empathy activities. To address this, this study developed a design-thinking-based AI education program incorporating generative AI and evaluated its effectiveness. A total of 117 second-year middle school students in South Korea participated (57 in the experimental group, 60 in the control group). The experimental group used generative AI in the Empathize stage to create personas and storytelling activities, while the control group completed the stage without AI. The program’s impact was assessed by measuring changes in creative problem-solving skills and AI literacy. Results showed significant improvement in AI literacy for both groups, confirming the program’s effectiveness in developing AI literacy. However, creative problem-solving skills improved only in the experimental group, suggesting that generative AI enhances contextual understanding and emotional engagement, which in turn leads to better problem definition and idea generation. These findings highlight the potential of generative AI in AI education, particularly in facilitating deeper empathy and structured problem-solving. Future research should explore its application across different educational levels to validate its pedagogical impact further.
Performance Evaluation of RESTful Services and AMQP Protocol in Cashless Parking Payment Mobile Apps Farrabi, Dimas Rizky; Suharjono, Amin; Apriantoro, Roni
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.9.4.2798

Abstract

This study explores the challenges surrounding the inefficient use of e-parking applications in Semarang, Indonesia, where issues such as illegal parking and an underperforming digital infrastructure have resulted in lost regional revenue from parking fees. The lack of a robust, user-friendly system has hindered user adoption and limited the application’s effectiveness in managing urban parking. To address these concerns, we developed a modernized e-parking application focused on enhancing usability and system performance. The application design prioritizes intuitive user interaction and seamless integration with existing parking infrastructure. Usability testing using the System Usability Scale (SUS) yielded a score of 80, indicating a high level of user satisfaction and ease of use. In addition, we conducted a comparative analysis of two communication protocols—RESTful and Advanced Message Queuing Protocol (AMQP)—during key application processes, such as vehicle check-in and check-out transactions. Results revealed that AMQP significantly outperformed RESTful in terms of Quality of Service (QoS), particularly with lower response times and minimal packet loss. AMQP consistently met TIPHON standards, achieving a QoS index score of 4, further supporting its suitability for real-time transaction systems in urban environments. This study highlights the critical role of technology optimization in addressing urban mobility issues, reducing illegal parking, and improving public service efficiency. Looking ahead, future development should focus on refining secondary features and introducing new capabilities such as reservation systems, dynamic pricing, and real-time availability tracking to further enhance user engagement and operational effectiveness. The findings emphasize the potential of well-designed e-parking systems to transform urban parking management through smart, scalable technology.

Page 4 of 6 | Total Record : 51

 2   3   4   5   6 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue