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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.
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Articles 40 Documents
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Search results for , issue "Vol 9, No 3 (2025)" : 40 Documents clear
Comparative Analysis of Homomorphic and Morphological Filters Using Inception V3 for Thermal Facial Image Classification of Autistic Children Catur Andryani, Nur Afny; Melinda, Melinda; Tariliani, Cut Dara; Oktiana, Maulisa; Junidar, Junidar
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.9.3.2885

Abstract

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder characterized by varying degrees of difficulty in social interaction and communication and repetitive behaviors. Early confirmation of the diagnosis of ASD leads to early appropriate treatment. However, confirming ASD diagnosis is challenging due to its wide spectrum and challenging behavior assessment. This research proposes a technology-based ASD diagnosis on children utilizing thermal facial analysis. This is conducted subject to the uniqueness of facial expression that is typically applied to children with ASD. A modified Inception V3 architecture did the intended thermal facial analysis for ASD diagnosis. Homomorphic filters and morphological filters are applied to the data pre-processing to improve the classification ability. The proposed identification method shows better sensitivity to the false-positive aspect. It is indicated by better performance in terms of precision, with a rate of 90% to 91%. This research is expected to support medical experts in confirming early diagnosis in children with ASD.
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.
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.
Recommender System Based on Social Network Analysis of Student Workshop and Event Activities Compared to GPA and Department Setiawan, Esther; Santoso, Joan; Cahyadi, Billy Kelvianto; Afandi, Acxel Derian; Saputra, Daniel Gamaliel; Ferdinandus, FX; Fujisawa, Kimiya; Purnomo, Mauridhi Hery
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.9.3.2943

Abstract

This research uses social network connections and academic data to create a recommender system that helps students choose seminars and events that suit their interests. The aim is to address the issue of students' hesitation in selecting activities. This project investigates the use of social network analysis (SNA) to provide individualized suggestions by analyzing student involvement in workshops and events, as well as their grade point average (GPA). The materials contain student data gathered between 2018 and 2023 from Institut Sains dan Teknologi Terpadu Surabaya (ISTTS), emphasizing the student's social media interactions and event participation. Metrics like centrality are employed to identify prominent nodes inside the network, and the approach combines graph-based SNA and cosine similarity for event recommendation. The network of student involvement in events was represented by a dataset comprising 2,293 edges and 602 nodes. The results show that the relevance of recommendations is improved when social network data is integrated with GPA, rather than GPA-based systems alone. The system identified key nodes, such as specific lectures, that significantly impacted student involvement and were rated highly in terms of centrality. Future research implications recommend expanding the dataset to encompass a broader range of events and refining the algorithm by including content-based filtering. The system's application is not limited to educational environments; it may also be tailored for career counselling or professional development.
Evaluation of Extreme Rainfall Occurrences Using Short-term and Long-term Standard Precipitation Index (SPI) Razuki, Nurul Dayana; Abdul Rauf, Ummul Fahri; Zainol, Zuraini; Mohd Isa, Mohd Rizal; binti Jamaludin, Nor Azliana Akmal
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.9.3.2213

Abstract

The main objective of this study is to investigate the Standard Precipitation Index (SPI), a method commonly used to determine extreme rainfall occurrences. It is also used to gauge the severity and duration of drought in meteorological studies. To highlight exceptional extreme rainfall events in selected areas, a methodology for calculating the SPI was provided in this paper using a range period and thresholds. The Standard Precipitation Index (SPI) is used to analyze monthly precipitation data from several selected rain gauge stations between 1970 and 2014. The goal of this study is to monitor the extremely moist conditions that may eventually lead to flooding. Precipitation index data from several rain gauge sites in the selected region are used to calculate the SPI time series. Additionally, SPI readings for 3 months or less may usually be used for basic drought monitoring, values for 6 months or less may be useful for monitoring agricultural impacts, and values for 12 months or more may be useful for monitoring hydrological impacts. In this study, two states affected by the monsoon season were selected: Johor and Kelantan. Two rain gauge stations were selected from these two states to calculate the SPI results. From this study, statistics on the occurrence of dry and wet events in specified areas were determined based on the SPI readings for 3-month, 9-month, 12-month, and 24-month periods. To summarize, this research demonstrates the potential of SPI to enhance our understanding of extreme rainfall events in Peninsular Malaysia.
Optimizing Artificial Neural Network for Customer Churn: Advanced Data Balancing and Feature Selection Hermawan, Aditiya; Wijaya, Willy; Daniawan, Benny
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.9.3.3064

Abstract

Customers are valuable assets in the dynamic business world. However, service dissatisfaction often leads them to switch to competitors, a phenomenon known as customer churn. In the telecommunications industry, churn poses a significant challenge as it directly impacts revenue and influences other customers within their social networks to do the same. Consequently, predicting churn has become essential, with numerous researchers employing various methods to classify potential churners. This study builds upon prior research that utilized Artificial Neural Networks (ANN) or Deep Learning to predict churn, achieving an accuracy of 88.12%. To improve model performance, this research implements an Artificial Neural Network (ANN) as the primary algorithm, along with Random Over-Sampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE) for data balancing, and three feature selection methods: Minimum Redundancy Maximum Relevance (mRMR), Lasso Regression, and XGBoost. The results demonstrate a 0.38% increase in accuracy compared to previous studies. The finding suggests opportunities for further exploration. Future studies can consider alternative feature selection techniques, such as Wrapper Methods or Heuristic/Metaheuristic approaches, which may produce more optimal feature combinations. Other data balancing methods, such as Undersampling techniques (e.g., Random Undersampling, Tomek Links) or Hybrid Methods (e.g., SMOTE combined with Tomek Links), could be explored to address imbalanced datasets effectively. These approaches are expected to provide better combinations and to improve overall prediction performance, enabling researchers to develop more robust and accurate models for customer churn prediction in subsequent studies.
Bibliometric Analysis of AI-Based Prototype Proposal for User Security Awareness in Healthcare Pratama, I Putu Agus Eka; Widyantara, I Made Oka; Linawati, Linawati; Gunantara, Nyoman
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.9.3.3319

Abstract

In the realm of public healthcare, integrating information technology (IT) must be judiciously balanced with heightened security awareness among users, given the escalating frequency of cyberattacks targeting this sector. Despite the availability of various product and service solutions aimed at enhancing user security awareness, these efforts have yet to yield optimal outcomes. There is a pressing need for innovative approaches to bolster healthcare user security awareness through IT, particularly leveraging the rapidly advancing field of artificial intelligence (AI). This study conducts a comprehensive review of prior research on the application of AI, specifically Large Language Models (LLM), within the domain of healthcare cybersecurity from 2014 to 2024. The objective is to ascertain the volume of publications, trace the evolution of publication trends, and assess the potential and positioning of research in this area. Employing a bibliometric analysis methodology, this study analyzes a dataset comprising 1000 related publications indexed by Google Scholar. The findings reveal that publications concerning applying LLM AI in healthcare cybersecurity constituted 12.82% in 2023, with a significant increase to 87.18% in 2024, representing a 6.8-fold rise. The mapping of publication developments is categorized into 24 clusters, with large language models, healthcare, retrieval-augmented generation, LLM, artificial intelligence, and cybersecurity emerging as the six most frequently discussed keywords in the research landscape. Consequently, this study underscores the substantial potential for current and future research on the application of AI in healthcare cybersecurity, advocating for the development of AI-based solutions to enhance healthcare user security awareness.
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.

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