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Eko Risdianto
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Journal on Informatics Visualization and Social Computing
Published by CV Media Inti Teknologi
ISSN : -     EISSN : 31237002     DOI : https://doi.org/10.58723/jivsc.v1i1.44
Core Subject :
The journal welcomes high-quality submissions covering, but not limited to, the following areas: Informatics Visualization Big data visualization and visual analytics Interactive and immersive data visualization techniques Visualization of graphs, networks, and social relationships Multimedia and digital image visualization Visualization algorithms and techniques Human-computer interaction (HCI) in visualization Virtual reality (VR) and augmented reality (AR) applications for data visualization Social Computing Social network analysis and modeling Collaborative and social computing systems Social media analytics and mining Social influence and digital interaction modeling Participatory computing and crowdsourcing Security, privacy, and ethical issues in social computing Machine learning and artificial intelligence for social applications Interdisciplinary and Applied Research Applications of visualization and social computing in education, business, healthcare, government, and social sciences Experimental methodologies, case studies, and quantitative/qualitative approaches in informatics and social computing Development of tools, platforms, and systems for visualization and social computing
Arjuna Subject : -
Articles 10 Documents
 1 
Design and Development of An Android-Based Server Monitoring Application Using The aaPanel API with The Waterfall Method Teguh Rijanandi; Eko Risdianto; Mohammad Qais Rezvani
Journal on Informatics Visualization and Social Computing Vol. 1 No. 1 (2025): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v1i1.44

Abstract

Background of study: The evolution of cloud computing has transformed server management, yet many management interfaces remain web-based and are not optimized for mobile devices. aaPanel, a popular server control panel, also faces this challenge of providing a mobile-friendly monitoring experience. Aims and scope of paper: This research aims to design and build a mobile application named "aaPanel Mobile" to efficiently monitor vital server statistics on Android devices, addressing the gap in mobile accessibility for aaPanel users. Methods: The software development follows the Waterfall model, covering requirements, design, development, testing, deployment, and maintenance. The application was built using Expo React Native, integrates with the aaPanel API for data retrieval, and its functionality was verified through black-box testing. Result: Testing results confirmed that all primary functionalities were successfully implemented. The application correctly displayed resource usage statistics, provided real-time data updates, and listed websites as expected. Conclusion: The "aaPanel Mobile" application has met its design and functional objectives, proving to be a viable tool ready for use in a production environment to help administrators monitor servers from anywhere.
Bibliometric Analysis of Big Data Visualization and Visual Analytics in Social Media Analysis: Techniques, Tools, and Trends Eko Risdianto; Sultan Hammad Alshammari; Md Zahidul Islam
Journal on Informatics Visualization and Social Computing Vol. 1 No. 1 (2025): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v1i1.63

Abstract

Background of Study: Big data visualization and visual analytics are essential in social media analysis because they help process large and complex data into more understandable information. With this technique, we can identify patterns, trends, and relationships in social media data, such as user interactions and social influences, that are difficult to analyze without visualization tools. This allows for faster, more accurate, and in-depth analysis, and helps with data-driven decision-making in a variety of areas. Aims and Scope of Paper:  The purpose of this paper is to review the literature related to big data visualization and visual analytics in the context of social media, using bibliometric analysis methods to identify current trends, techniques used, and important tools. Methods: This study used a bibliometric design to analyze the literature in the Scopus database with three main keyword combinations: "Big Data Visualization" AND "Social Media Analysis", "Visual Analytics" AND "Bibliometric Analysis", as well as "Data Visualization" AND "Social Media Analytics". The literature analyzed consisted of journals published between 2015 and 2025.  After data collection, filtering is carried out using OpenRefine to eliminate bias and duplication, ensure the accuracy and validity of the data, resulting in objective insights into trends in data visualization and social media analytics. Results: A summary of key findings on the most widely used techniques and tools in big data visualization on social media. Conclusion: Closing on the contribution of bibliometric analysis in understanding the development of big data visualization in social media research.
Next-Generation Digital Twin UX: IoT-Driven Smart and Interactive Design ANWAR ALI SATHIO; MUHAMMAD MALOOK RIND; SAMEER ALI
Journal on Informatics Visualization and Social Computing Vol. 1 No. 1 (2025): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v1i1.106

Abstract

Background of study: The rapid development of IoT-enabled systems has transformed user interaction by enabling intelligent, responsive, and interconnected digital environments. However, existing studies often emphasize traditional usability factors while overlooking emerging interaction attributes essential for next-generation Digital Twin and IoT-based interfaces. Aims and scope of paper: This study aims to investigate next-generation Digital Twin user experience (UX) by exploring interactive IoT design attributes, including gesture-based interaction, gaze tracking, multimodal interfaces, and AR-assisted usability. The research also develops an enhanced usability framework that integrates efficiency, cognitive load, and user satisfaction metrics. Methods: Using a mixed-method approach, the study integrates quantitative evaluations (task completion time, error rates) and qualitative assessments (NASA-TLX, SUS). Data were collected from open-source IoT usability datasets and supported by prototype testing, including touch-based, voice-assisted, gesture-controlled, and AR-enhanced interfaces. Result: Findings show that AR-enhanced and touch-based interfaces significantly improve task efficiency, reduce cognitive load, and increase user satisfaction. Gesture-based systems, while offering immersive interaction, exhibit higher error rates and cognitive strain. Users also expressed concerns regarding data security and interface complexity in IoT-enabled environments. Conclusion: IoT-enabled Digital Twin interaction offers substantial improvements in usability and engagement, particularly through AR and touch-based designs. However, challenges persist in gesture accuracy, voice recognition consistency, and privacy risks. This research establishes a structured framework for future IoT-UX development, emphasizing adaptive, intuitive, and user-centered design principles.
The IoT-enabled Interactive Design Investigation in User-Centered Design MUHAMMAD MALOOK RIND; ANWAR ALI SATHIO; SHAFIQUE AHMED AWAN; GHULAM AHMED
Journal on Informatics Visualization and Social Computing Vol. 1 No. 1 (2025): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v1i1.107

Abstract

Background of study: IoT enabled systems such as mobile phones, smart homes, and medical applications play a significant role in improving convenience and usability in everyday life. The combination of IoT networks with graphical user interface designs, including websites and online applications, continues to grow rapidly. Despite this progress, interactive usability challenges remain, especially in aligning IoT technologies with human centered interaction principles. Aims and scope of paper: This study explores the usability of interactive design and identifies new attributes that contribute to improving user experience in IoT enabled systems. It aims to classify logical and physical attributes that influence usability and to propose new interactive dimensions for IoT based design. Methods: The study uses a descriptive and analytical approach based on a review of relevant literature and conceptual analysis. Logical and physical usability attributes were examined to understand their roles in interactive design and user experience across IoT environments. Result: The results reveal that two major factors, logical and physical attributes, are fundamental to interactive design. These attributes can guide new rules for IoT based usability, expanding human computer interaction beyond traditional interfaces such as keyboard, mouse, and screen, and including motion, gaze, and posture as part of new interaction mechanisms. Conclusion: This study focuses on interface usability to help developers create more attractive and user friendly designs. The findings open new research areas in interactive IoT systems and contribute to the development of adaptive, efficient, and human oriented interface design principles for future IoT applications.
Sentiment Analysis on the Indonesian Military Draft Bill in YouTube Comments Using a LSTM Model Adib Raihan Ashidiq; Fradika Anggara Putra; Agil Febri Pradana; Nuriwan Saputra
Journal on Informatics Visualization and Social Computing Vol. 2 No. 1 (2026): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v2i1.123

Abstract

Background: The legislative proposal for the revision of the Indonesian Military Draft Bill (RUU TNI) has incited significant public controversy, largely articulated in digital arenas like YouTube. This revision fuels widespread apprehension about the potential revival of the military's dual-function role (Dwifungsi ABRI) and its potential detriment to civilian supremacy.Aims: This study aims to quantify and analyze public sentiment towards the RUU TNI, as expressed in Indonesian YouTube comments, using a Deep Learning approach combining FastText embedding and Long Short-Term Memory (LSTM) classification.Methods: A dataset of 520 Indonesian comments was collected and manually labeled into three classes: positive, neutral, and negative. The methodology included comprehensive text preprocessing, feature extraction via FastText word embeddings (300 dimensions), and sentiment classification using the LSTM architecture. The model was rigorously evaluated using a confusion matrix across standard metrics, including accuracy, precision, recall, and F1-score.Result: The dataset exhibited significant class imbalance, dominated by negative sentiment (42.31%). The optimal LSTM configuration, tested with Stratified K-Fold Cross Validation, yielded an accuracy of 42.39%, a precision of 43%, a recall of 47%, and an F1-score of 60%. Topic modeling via LDA revealed dominant themes criticizing the bill's quality, state corruption, and legislative integrity. The low accuracy, barely surpassing the baseline, suggests that the model struggled with the limited and imbalanced data.Conclusion: Public sentiment regarding the RUU TNI is strongly negative and critical, reflecting deep concerns about democracy and state institutions. While the FastText-LSTM pipeline was established, its classification performance was severely constrained by the small, highly imbalanced dataset and the complex nature of political discourse. Future research must utilize advanced Transformer models (e.g., IndoBERT) and larger, balanced datasets to achieve meaningful classification performance on this critical socio-political domain.
Analysis of Factors that Influence User Acceptance of PERSIS Solo Application with Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) Model Faiz Muhammad; Dyah Ayu Irawati; Muhammad Hafiz Kurniawan
Journal on Informatics Visualization and Social Computing Vol. 1 No. 1 (2025): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v1i1.124

Abstract

Background: In the digital era, football clubs increasingly adopt mobile applications to enhance fan engagement and service delivery. The PERSIS Solo application is a new digital innovation that provides official club information, ticket sales, and merchandise services. However, there has been no prior evaluation of user acceptance, which is crucial for ensuring the successful adoption and continued use of such technology.Aims: This study examines the determinants of user adoption of the PERSIS Solo mobile application through the application of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) framework. There are several influences of key factors examined in this study, they are performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habitual behavior on user acceptance, while taking into account the moderating role of demographic variables such as age, gender, and user experience.Methods: A quantitative approach was employed with 150 active users of the PERSIS Solo application as respondents. Data were examined using Structural Equation Modeling with Partial Least Squares (SEM-PLS) in SmartPLS to assess the validity and reliability of constructs as well as the relationships between variables.Results: Out of 25 tested hypotheses, six were supported. Social influence, hedonic motivation, and habit significantly influenced Behavioral Intention, while facilitating conditions, habit, and Behavioral Intention significantly affected Use Behavior.Conclusion: The study concludes that hedonic motivation and habit are dominant predictors of user acceptance and actual usage. The findings provide empirical insights for improving mobile applications in sports organizations and contribute to understanding digital fan-engagement systems in Indonesia.
A Web-Based Forward-Chaining Expert System for Smartphone Selection A Decision Support Tool for Novice Daffa Ihsan Al Hakim; Lucky Edward; Muhammad Fitrizhika; Deosa Putra Caniago
Journal on Informatics Visualization and Social Computing Vol. 2 No. 1 (2026): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v2i1.125

Abstract

Background: The ratio of ophthalmologists to the population in Indonesia is extremely low, leading to unequal access to eye care and making it difficult for the public to obtain early information regarding eye symptoms.Aims: This study aims to develop and validate an efficient, domain-specific eye care chatbot using the Llama 3.2 1B Instruct model, focusing on an optimal fine-tuning pipeline for limited hardware constraints.Methods: Utilizing an Experimental Research (Model Development) design, a QLoRA 4-bit quantization technique was applied on a Google Colab T4 GPU (15.64GB VRAM). The training dataset comprised 16,742 samples from Kaggle Eye Care and MedQuad Indonesian Translation, with a subset of 5,000 samples used for experiments.Result: After training for 3 epochs (750 steps), the Training Loss decreased from 1.3394 to 0.7188 (46.3% improvement), while Perplexity reached 2.3620, categorized as excellent. The model maintained stability with a final gap of 0.1407 between training and validation loss.Conclusion: The Meta-Llama 3.2 1B Instruct model, fine-tuned with 4-bit QLoRA, is highly effective for building a domain-specific healthcare chatbot, successfully operating within an 8GB VRAM limit while maintaining high generalization capabilities.
Spatial Analysis and Classification of Traffic Congestion Levels Using a Hybrid Machine Learning Approach in Badung Regency, Bali Rizal Wahyu Pratama; Mikhael Setia Budi; Eka Sahputra; Khulika Malkan; Wisnu Aji Sanjaya; Yoka Romadani
Journal on Informatics Visualization and Social Computing Vol. 2 No. 1 (2026): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v2i1.192

Abstract

Background: Traffic congestion in Badung Regency, Bali, presents complex challenges for transportation management in a strategic tourism area, necessitating intelligent monitoring methods that surpass conventional surveys.Aim: This study develops a Hybrid Machine Learning framework that integrates unsupervised and supervised learning techniques to map spatial patterns and precisely predict congestion anomalies.Methods: Initially, the K-Means Clustering algorithm validated the segmentation of traffic data into five optimal clusters (k=5) based on Elbow and Silhouette Score tests, which subsequently served as the ground truth for data labeling. Furthermore, a comparative evaluation of Random Forest, XGBoost, and Gradient Boosting models was conducted to determine the best predictive performance.Result: Experimental results identified Random Forest with strict pruning parameters (max_depth=3) as the optimal model, balancing accuracy and generalization stability while effectively avoiding overfitting through learning curve analysis. This model achieved a testing accuracy of 92.00% and a cross-validation score of 89.69%, with the Travel Time Index (TTI) and average speed identified as the primary determinants.Conclusion: Spatial visualization demonstrated high consistency between the model's prediction maps and actual field conditions, confirming that this approach is effective as a foundation for an Early Warning System to support data-driven traffic management policies in Bali.
Development of an Eye Care Chatbot Based on Llama 3.2 1B Using 4-bit QLoRA Technique Teguh Rijanandi; Eko Risdianto
Journal on Informatics Visualization and Social Computing Vol. 2 No. 1 (2026): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v2i1.220

Abstract

Background: The ratio of ophthalmologists to the population in Indonesia is extremely low, leading to unequal access to eye care and making it difficult for the public to obtain early information regarding eye symptoms.Aims: This study aims to develop and validate an efficient, domain-specific eye care chatbot using the Llama 3.2 1B Instruct model, focusing on an optimal fine-tuning pipeline for limited hardware constraints.Methods: Utilizing an Experimental Research (Model Development) design, a QLoRA 4-bit quantization technique was applied on a Google Colab T4 GPU (15.64GB VRAM). The training dataset comprised 16,742 samples from Kaggle Eye Care and MedQuad Indonesian Translation, with a subset of 5,000 samples used for experiments.Result: After training for 3 epochs (750 steps), the Training Loss decreased from 1.3394 to 0.7188 (46.3% improvement), while Perplexity reached 2.3620, categorized as excellent. The model maintained stability with a final gap of 0.1407 between training and validation loss.Conclusion: The Meta-Llama 3.2 1B Instruct model, fine-tuned with 4-bit QLoRA, is highly effective for building a domain-specific healthcare chatbot, successfully operating within an 8GB VRAM limit while maintaining high generalization capabilities.
Design of a Backward Chaining Expert System for Food Nutrition Recommendations Based on User Data Angga Febrilian Adhiatma; Noval Arif Rahma; Corry Triana Hadi; Nawa Al Syaputra; Ignatius Agus Supriyono; Dendy Jonas
Journal on Informatics Visualization and Social Computing Vol. 2 No. 1 (2026): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v2i1.231

Abstract

Background: Optimal health relies on balanced nutrition, but many struggle with food choices due to limited knowledge and restricted access to nutritionists. Existing solutions analyze food images or health texts separately, leaving a gap in integrating real-time image recognition with personalized medical inference.Aims: This study aims to develop a personalized food recommendation expert system combining Convolutional Neural Network (CNN)-based image classification with backward chaining reasoning, mapping food images and user health profiles (BMI, medical history, allergies) into structured IF–THEN rules.Methods: Using a software engineering R&D design, the system was built on a three-tier architecture guided by WHO and Indonesian Ministry of Health standards. Performance was evaluated via black-box testing, a 500-sample food image dataset, and synthetic user profile simulations (normal, obese, hypertensive, allergic).Result: The CNN model achieved 87% accuracy across 500 test images, exceeding the 85% target threshold. Simulations showed an 80% average alignment with official dietary standards, successfully automating caloric restrictions, low-sodium adjustments, and allergen eliminations.Conclusion: Combining computer vision with backward chaining inference effectively bridges the gap in automated personalized nutrition, serving as a self-service tool for the public and a decision support system for health professionals.

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