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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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Towards an Integrated Decision Support System for the Evaluation Data Mining Tools in Economic Intelligence System Ali Salah, Hussein; Shihab Ahmed, Ahmed; Bhar Layeb, Safa
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.9.6.3635

Abstract

Information is very important in the economic world because it affects how industries make choices in today's fast-growing economy. It's essential to learn how to find valuable knowledge for the company's operations quickly. The goal of this work is to create a database of socioeconomic intelligence, operationalize it as a system, and use the study's results to make better decisions. Building economic understanding processes requires research into economic analysis algorithms and the development of computational representations for financial systems. This information is used to construct knowledge item architecture for financial reasoning systems. This study employs data mining methods to assess and extract relationships among dataset elements. Association rules and forecasting techniques are used to quickly and accurately retrieve relevant data for the financial intelligence sector. The research examines the application of financial intelligence mechanisms via data mining methodologies. The article discusses the dataset and reveals that the suggested algorithm's classification accuracy surpasses that of the Logistic Regression (LR) technique by 2.76%. This illustrates the efficacy of the devised system in obtaining and analyzing economic intelligence data. Research on sophisticated algorithms and their use in financial intelligence platforms could improve the precision and effectiveness of data collection and analysis. The results of this study provide a basis for enhancing financial decision-making and underscore the potential for further innovation in this field.
Artificial Intelligence: Creating a Hyper-personalization Artifact Murugasu, Umapathy Sivan G; Subbarao, Anusuyah
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.9.6.3367

Abstract

This research aimed at synthesizing an artifact that could hyper-personalize customers’ needs using a design science framework. Artificial intelligence (AI) was applied to enable telecommunication (Telco) businesses to offer hyper-personalized products and services, based on a database of customers’ digital demography. A digital demographic database was created using data collected on attributes derived from a systematic literature review. The proof of concept (POC) was developed using the Waikato Environment for Knowledge Analysis (WEKA) software. A systematic literature review was conducted to identify documents used in creating a cross-tabulation of attributes through thematic analysis. This analysis resulted in 32 attributes. The Delphi method for consensus reaching by 10 industry experts was used to reduce to 12 attributes in 2 stages. These attributes were structured into a Google Form to collect customer usage data. Outlier data were removed using multivariable outlier detection by Mahalanobis distance available via SPSS version 21. Using the updated database, several procedures were conducted to determine the best artificial intelligence algorithm for subsequent analysis. Using the Logistic Model Tree algorithm and the customer digital demography, the Telco offering for the customers was predicted with 97.6% accuracy. The artifact created was named Hypersona. The theoretical contribution lies in the applicability of real-time identification of client requirements, targeted client classification, and the ability to offer hyper-personalized products. Implications for Further Research: This research highlights the potential of AI-driven hyper-personalization in the telecommunications sector. Future studies could explore scaling the artifact across diverse businesses.
NeuraWheel: A Synergistic Approach with Deep Learning Models and Curated Dataset Mahendrada, Vamsi sravanth; Parameswaran, Murali; Parameswaran, Seetha
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.9.6.2771

Abstract

This research addresses a critical aspect of automotive safety by developing an advanced tire defect classification model to enhance tire maintenance practices and reduce tire-related accidents. The primary objective is to leverage the power of deep learning to accurately distinguish between good and defective or worn-out tires, which is vital for ensuring road safety. The study utilizes a comprehensive dataset encompassing various tire conditions to train and evaluate six prominent deep learning models—EfficientNet, InceptionNet, VGG19, DenseNet121, DenseNet201, and ResNet101—as well as three lightweight models—SqueezeNet, MobileNet V2, and MobileNet V3. Customised Neurawheel models are also introduced and specifically designed for this task. Employing state-of-the-art deep learning and image processing techniques, the models were rigorously trained and tested to ensure high accuracy in classification tasks. Among the models tested, Neurawheel-4j emerges as the top performer, achieving an impressive accuracy rate of 98.44%, significantly outperforming ResNet101 and other models. The research highlights the effectiveness of sophisticated model architectures, rigorous dataset curation, and optimized training configurations, underscoring the potential for these models to be deployed in real-world applications. The implications of this study are profound, as the deployment of such a model in real-world scenarios could dramatically reduce tire-related accidents, contributing to the broader goal of enhancing road safety. Future research should focus on expanding the dataset to include a wider range of real-world scenarios, exploring additional metrics to assess tire wear severity, and integrating the model with IoT-based systems for real-time tire monitoring. This study lays the foundation for further advancements in tire defect classification and automotive safety.
Traffic Violation Detection Using Computer Vision Techniques Ong, Chin Sin; Connie, Tee; Ong Goh, Michael Kah
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.8.3-2.2941

Abstract

The increasing number of road accidents is still a global concern.  Traditional approaches to detecting traffic violators on the road, such as radar guns and sensors, are expensive and time-consuming to maintain and install. This often results in inefficient and ineffective detection of traffic violators. This paper proposes a more cost-effective and efficient approach to traffic violation detection utilizing visual data from CCTV footage. Specifically, the method targets two common violations: crossing red lights and overtaking on double lines. In this study, YOLO is integrated for road object detection, providing the detection of vehicles and traffic lights on the road for our system. Then, the Deep SORT tracker tracks detected vehicles, ensuring continuous monitoring over time. An automated lane detection technique is formulated to identify the stopping line/lane for red light violation detection, enabling precise detection of vehicles that cross the stop lane during red light. For overtaking detection, the system detects the double line to serve as the boundary that vehicles should not cross, identifying illegal overtaking. Furthermore, point-line distance calculation is utilized to detect traffic violators by analyzing their tracked trajectories and positions. The proposed solution is evaluated using real-world CCTV footage from online repositories to reflect the real-world scenarios as closely as possible. Experimental results show that the proposed techniques achieve promising detection of real-time traffic violators, which leads to a safer environment for road users.
Development of a Catch-Throwing Skill Analyzer Based on Sensors Komaini, Anton; Kiram, Yanuar; Razi, Pakhrur; Zakaria, Jaffri Bin; Handayani, Sri Gusti; Andika, Heru
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.8.3-2.2939

Abstract

This research aims to develop a tool for measuring catching-throwing skills using modern, effective, and efficient sensor technology. This tool is designed to increase accuracy in measuring catch-throwing skills. This type of research uses (R&D). This research involved three experts in their field: an electrical expert, an evaluation and measurement expert, and a physicist. A total of 30 participants participated in testing in a small group of 12 people and a large group of 18 people with an age range of 15-20 years. Testing is carried out with questionnaires and direct tests. The statistical analysis used is r-correlation with the help of the SPSS application. The test results show a high alpha coefficient, confirming that this tool is consistent and reliable in collecting data. Three experts carried out practicality testing: Electrical Experts, Evaluation and Measurement Experts, and Physicists. The results show that this tool is efficient, with practicality levels of 98%, 92%, and 89%, respectively. In addition, an excellent level of effectiveness is demonstrated by expert assessment results, which reach 99%, 89%, and 85%. With this tool, researchers and teachers can evaluate object control skills more efficiently and accurately and assist in collecting more comprehensive data. Overall, this tool for measuring throwing and catching skills provides significant innovation and is expected to support research and education in measuring and developing children's motor skills.
Multi Criteria Decision Making Method For Developing Smart Indonesia Program Scholarship Recipient Candidate System Supriyanta, Supriyanta; Sutanto, Yusuf; Susilo, Dahlan; Setyadi, Heribertus Ary; Syukron, Akhmad
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.9.6.3706

Abstract

The Government of Indonesia is continuously striving to improve its education quality with the provision of scholarship programs, one of which is the Smart Indonesia Program (SIP). Students' interest in obtaining SIPs is increasing, but the selection process still relies on conventional methods. Without adequate IT support, the selection process for SIP scholarship candidates will be complex, less objective, and somewhat unfair. State Vocational High School (SVHS) 5 Surakarta was selected as a case study for this research to establish the selection process and the data collection methods used in previous years. The research aims to develop a Decision Support System (DSS) to assist in nominating students deemed eligible for SIP scholarship recommendations. The applied methods include Analytical Hierarchy Process (AHP) and Multi-Objective Optimization by Ratio Analysis (MOORA). Four criteria have been set in this DSS: card ownership status, total parental income, household income, and number of siblings. Each of which is further broken down into several sub-criteria and assigned a value for use in the AHP process. Upon comparing data from 2021 to 2023, it was found that the accuracy in 2021 was 92.9%, in 2022 it reached 94.7%, and in 2023 it recorded 92.3%. Based on the results of this system accuracy test, it can be concluded that the AHP and MOORA methods can be used to objectively produce recommendations for students eligible for SIP scholarships, based on the input criteria.
Grey Level Differences Matrix for Alcoholic EEG Signal Classification Sri Aprillia, Bandiyah; Rizal, Achmad; Geraldy Fauzi, Muhammad Arik
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.8.1.2602

Abstract

Electroencephalogram (EEG) signals can provide information on abnormalities in a person's brain and characterize brain activity. Brain injury or diseases can manifest as brain disorders. Trauma or the use of specific chemicals or medications, such as alcohol, can result in brain damage. Previous research has demonstrated variations in the patterns of EEG signals between alcohol-using and non-drinking people. Various techniques, including wavelet and entropy, have been developed to detect alcoholic EEG using event-related potential (ERP) testing. This work proposes a feature extraction technique based on texture analysis for the classification of alcohol EEG signals because ERP-measured EEG often involves many channels.  An NxM image is thought to be equivalent to an EEG signal with N channels and a recording duration of M samples. The NxM matrix is formed by channelizing the N-channel EEG signal in this investigation. Normalization is then used to get a matrix value of 0-255 or an 8-bit image in the following step. Five features are measured in four directions, and the Grey Level Difference Matrix (GLDM) approach is utilized for feature extraction. Using five grey-level difference matrix (GLDM) features and linear discriminant analysis as a classifier, the maximum accuracy was achieved at 73.3%. Image processing can still be used to increase accuracy even though the final product is less accurate than the earlier technique. The suggested approach can still be adjusted to work with biomedical signals or image processing techniques like the Grey Level Co-occurrence Matrix (GLCM).
Multimedia-Assisted Elementary School Learning Materials Innovation Using STEAM Learning Approach Desyandri, -; Agustina, Yana; Yeni, Indra; Parmadi, Bambang
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.8.3-2.2238

Abstract

Printed materials dominate the teaching materials used in elementary schools in Indonesia. These conditions are backward with the development of the Industrial Revolution 4.0, so it is urgent to research the effectiveness of multimedia-based teaching material using Macromedia Flash Eight application with the learning approach of Science, Technology, Engineering, Art, and Math (STEAM) in Elementary Schools. The research method used is research and development (R&D) with the ADDIE model consisting of Analysis, Design, Development, Implementation, and Evaluation. The data collection instrument uses pre-test and post-test sheets, and an evaluation lift is open to the use of teaching materials for teachers and students. The respondents were eight teachers and 84 elementary school students. The effectiveness of the teaching material is analyzed in two ways: (1) the analysis of learning outcomes (pre-test and post-test) using the N-Gain Test, and (2) the evaluation of the product is based on the results of open-haul analysis, advice, and recommendations. This research and development results show that the pre-test average of 47,14 increased to 84.58 (post-test), while the test score of N-Gain was 71.75%, with the category quite effective. Thus, it can be concluded that Macromedia Flash 8-based teaching materials with STEAM approach to integrated thematic learning in elementary schools are practical and capable of improving student learning outcomes.
Enhancing User Experience through UI Redesign Using the UEQ+ Method Setiawan, Wahyu Fajar; Amirullah, Afif; Rochimah, Siti
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.9.6.3596

Abstract

This research redesigned the User Interface (UI) of the XYZ e-wallet application, applying the User Experience Questionnaire Plus (UEQ+) testing method within the Design Thinking framework. This research contributes to the field by addressing the absence of comprehensive UI/UX evaluation in financial technology applications through an iterative design methodology. Initial UEQ+ assessment utilizing nine user experience questionnaire scales revealed significant usability issues, with intuitive use scoring 2.32 and clarity scoring 2.60, indicating substantial potential for improvement. The five stages of Design Thinking (Empathize, Define, Ideate, Prototype, Test) were systematically applied to solve the identified problems. Interactive prototyping in Figma facilitated real user testing of critical features, including the homepage, QRIS Payment, and History & Transfer notify. Post-redesign, there were significant increases in intuitive use (from 2.34 to 3.91; 67.1%), clarity (from 2.90 to 4.33; 49.3%), efficiency (from 3.25 to 4.44; 36.6%), trust metrics (from 3.41 to 4.51; 32.3%), and content quality (from 3.07 to 4.34; 41.4%). The statistical validation yielded a Cronbach’s Alpha of 0.965, indicating excellent reliability of the measurement. The high relationship among the factors (0.313-0.960) reflects a broad improvement. This study introduces the first empirically validated model that combines UEQ+ evaluation with Design Thinking for e-wallet applications, offering evidence-based UI/UX design guidelines for fintech, particularly valuable for Indonesian and similar developing markets where trust critically affects adoption.
Advancements in Detection Top Influencer Marketing in the Airline Industry: A Combination of the Leiden Algorithm and Graph Coloring Handrizal, Handrizal; Sihombing, Poltak; Budhiarti Nababan, Erna; Andri Budiman, Mohammad
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.9.6.3440

Abstract

In recent years, the airline industry has increasingly utilized social media and online platforms to engage customers and enhance brand loyalty. Identifying key influencers within these networks is crucial for optimizing marketing strategies and improving customer engagement. Influencers play a pivotal role in shaping opinions, driving behaviors, and amplifying brand messages within social networks. Consequently, efficient methods for detecting influencers are essential for understanding network dynamics and maintaining a competitive edge. This study introduces a novel contribution to the field of social network analysis by proposing the Leiden Coloring Algorithm, an enhancement of the traditional Leiden algorithm that integrates graph coloring techniques. The scientific contribution of this research lies in improving the precision of community detection and computational performance in large-scale networks. Experimental results on five airline-related datasets demonstrate that the proposed method achieves higher modularity (average 0.9375), faster processing time (average 204.88 seconds), and identifies fewer, more cohesive communities compared to the Louvain Coloring Algorithm. These findings highlight the algorithm's effectiveness in influencer detection and its potential application in community detection, marketing optimization, and strategic decision-making within the airline industry.

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