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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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Online Counseling on Global Issues: Systematic Literature Review Ifdil, Ifdil; Zatrahadi, Muhammad Fahli; Darmawati, Darmawati; Istiqomah, Istiqomah; Bakar, Abu Yazid Abu
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.9.3.2448

Abstract

The integration of expertise in counseling with a deep comprehension of contemporary technology is essential. Developing a sustained method is crucial for creating a practical framework to address the psychological ramifications associated with the escalating complexities of global challenges. Therefore, this study was conducted to explore the use and challenges of online counseling (e-counseling) for global issues using the systematic literature review (SLR) method. The search was carried out in the Scopus database to obtain 637 documents after limitations in the year of publication, starting in 2020–2023. Another limitation was the use of the English language, and after quality assessment, a 25-article document analysis was conducted. The results showed that e-counseling was critical in addressing challenges and impacted many individuals in different regions. According to NVivo analysis, the practical implementation of online counseling (e-counseling) encountered several challenges, such as using potentially vulnerable technology, constraints within interpersonal relationships, and incorporating different methods.
Entity Extraction in Indonesian Online News Using Named Entity Recognition (NER) with Hybrid Method Transformer, Word2Vec, Attention and Bi-LSTM Zainuddin, Zahir; Mudassir, -; Tahir, Zulkifli
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.9.3.2902

Abstract

Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) that identifies entities such as person names, locations, and organizations within the text. While many NER studies have concentrated on the English language, there is a significant need for further research on Indonesian NER. Indonesia presents unique challenges due to its structural complexities, polysemy, and ambiguities. Conventional machine learning and deep learning techniques have been widely applied in NER; however, more detailed exploration into integrating these methods for performance improvement is needed. This study introduces a novel hybrid model, TWBiL, which combines Transformer mechanisms, Word2Vec embeddings, Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention mechanisms to enhance NER performance on Indonesian text. TWBiL harnesses the strengths of each component to generate superior word vector representations, extract intricate sentence features, and disambiguate entities contextually. Our experimental results demonstrate the effectiveness of the proposed hybrid model, revealing a significant improvement in NER performance. Specifically, TWBiL achieves an F1-Score of 85.11 on an Indonesian online news dataset, outperforming the traditional Bi-LSTM model, which achieved a score of 75.18. The results indicate that TWBiL effectively reduces ambiguity and captures context more accurately, enhancing entity recognition. Future research should priorities reducing computational time when handling larger datasets without compromising overall NER performance. This study underscores the potential of integrating advanced deep learning techniques to tackle the unique challenges of Indonesian NER, thus providing a solid foundation for further advancements in the field.
A Framework of Forensic Analysis and Visualization: Using WhatsApp Chat Data as a Case Study Pirzada, Shahnaz; Ab Rahman, Nurul Hidayah; Cahyani, Niken Dwi Wahyu; Othman, Muhammad Fakri
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.8.3-2.2868

Abstract

Digital forensic analysis involves studying and analyzing acquired evidence artifacts using methodical approaches. However, unstructured data could be time-consuming and difficult in the forensic examination phase. Automation in digital forensic processes has recently been seen as a potential solution to improve analysis processes. Therefore, we propose a forensic analysis and visualization framework via exploratory data analysis (EDA) using WhatsApp chat datasets as a case study. Univariate and multivariate EDA visualization models were applied to the datasets. The framework's utility was demonstrated through forensic analysis simulation scenarios: linkage (interaction) and attribution (who was responsible). origination (evaluation of source), and sequencing (timeline). It was conducted in a controlled experiment environment using Python scripting. The aim is to test the extent to which EDA visualization models can visualize complete and accurate artifacts based on the scenarios. Our evidence-based findings demonstrated the suitability of specific univariate and multivariate in visualizing complete and accurate data. The framework was able to visualize key metadata such as incoming and outgoing chats, sender identification, communication timeline, and shared media. The findings suggested that the EDA approach aligns with forensic analysis, as it helps describe investigative clues by analyzing data patterns. Additionally, an expert review was conducted, in which the experts confirmed the adequacy of the simulation scenarios and the usefulness of the forensic visualization. Furthermore, the results of this study could aid in presenting evidence in a court of law.
Applying Data Mining on Personal Computer for Document Classification Chai, Ian; Salleh, Ahmad Zarif
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.9.3.3473

Abstract

The typical user creates documents over many years of computer usage. As people move from computer to computer, they tend to copy the files to the new computer, because "you never know when we might need to refer to something from the past." Hence, the collection grows larger and larger, expanding to hundreds and thousands. This collection soon exceeds the ability of most people to remember what each document was, even if they have been keeping them in some order in folders – and many people fail to anticipate how the folders and subfolders should be arranged as time passes – and by the time they realize it, most find it too daunting a task to reclassify them all manually. Therefore, we sought to solve this problem using a data mining-based solution, specifically multinomial naive Bayes. We developed a document classification program to automatically categorize all documents stored on a person's personal computer hard drive, eliminating the need for manual classification. The proposed algorithm achieved a score of 0.853 for accuracy, 9,833 for precision, 0.661 for recall, and 0.767 for the F1 metric. It should be possible, with further refinement and improvement, for example by balancing the dataset and increasing its size, for this technique to be applied in practical applications that enable automatic document classifications on the computers of most computer users.
Enhancing Security in Cross-Border Payments: A Cyber Threat Modeling Approach Amiruddin, Amiruddin; Briliyant, Obrina Candra; Windarta, Susila; Setiadji, Muhammad Yusuf Bambang; Priambodo, Dimas Febriyan
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.9.6.3205

Abstract

Cross-border payment (CBP) systems are critical to the global economy but are increasingly susceptible to cyber threats due to their complex structures and diverse transaction models. This paper analyzes cyber vulnerabilities across four CBP models: correspondent banking (SWIFT), infrastructure (ApplePay), closed-loop (PayPal), and peer-to-peer (Ripple). It employs the STRIDE methodology and adapts the cyber threat modeling framework proposed by Khalil et al. Key objectives include identifying vulnerabilities, assessing the impact of threats, and proposing mitigation strategies. The corresponding banking model shows the highest threat impact due to extensive transaction elements crossing trust boundaries. In contrast, the closed-loop model demonstrates lower vulnerability because of fewer components outside its trust boundary. Peer-to-peer and infrastructure models present moderate risk levels influenced by blockchain transparency and infrastructure dependencies. Critical threats identified include abuse of authority, malware, and script injection, which can result in significant losses, such as financial theft, service outages, and data breaches. Results indicate that interactions between processes across trust boundaries exacerbate cyber risks. Strategic recommendations include reducing system complexity, reinforcing security protocols at trust boundaries, and integrating advanced threat detection mechanisms. The study highlights these vulnerabilities and risks and underscores the need for robust cybersecurity measures to protect CBP systems. This research contributes to the existing knowledge by providing a detailed threat assessment and practical insights for improving CBP security. Future studies should explore alternative modeling methods, update security contexts to reflect real-world scenarios, and analyze the impact of open banking technologies.
Computational Visualization and Informatics Interaction Analysis of Daidzein Compound from Soybean (Glycine max L.) on Maltase-Glucoamylase Protein for Predictive Study of Intestinal Disaccharidase Deficiency Zainul, Rahadian; Elkhool, Tarek A.; Ahmed, Shafique; Goh, Khang Wen; Muhardi, -
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3487

Abstract

This study explores the potential of daidzein, a bioactive compound derived from soybean (Glycine max L.), as a maltase-glucoamylase protein inhibitor to address intestinal disaccharidase deficiency, utilizing in silico methodologies. The research supports Sustainable Development Goal 3: Good Health and Well-being by evaluating the binding interactions, physicochemical properties, and therapeutic potential of daidzein. Structural data of daidzein and maltase-glucoamylase were analyzed using PyMOL, PyRx, Protein Plus, and Lipinski’s Rule of Five to predict interaction mechanisms and drug-likeness. The methodological framework consisted of molecular docking and physicochemical analysis, including binding affinity and Root Mean Square Deviation (RMSD) evaluations. The docking results demonstrated strong and stable interactions between daidzein and the target protein, with binding affinities of -2.5 and -2.4 kcal/mol. Additionally, key physicochemical parameters—such as molecular weight (254) and log P (2.713)—indicated favorable drug-likeness and oral bioavailability. RMSD values supported the stability of daidzein within the enzyme’s active site. These findings suggest that daidzein may serve as a promising natural therapeutic agent for digestive disorders associated with enzyme deficiencies. The study also illustrates the efficiency of computational tools in the early stages of drug discovery, reducing reliance on laboratory testing. It is recommended that future research includes in vitro validations and preclinical studies to further assess the safety, efficacy, and pharmacokinetics of daidzein. Structural optimization to enhance target binding is also encouraged. Ultimately, this research contributes to the sustainable development of plant-based therapies for managing non-communicable diseases and improving digestive health.
Factors Influencing Information Quality of Information Systems: A Systematic Literature Review Aziz, Azwan Abd; Haizan Nor, Rozi Nor; Jusoh, Yusmadi Yah; Wan Ab. Rahman, Wan Nurhayati; Mohd. Ali, Norhayati
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.8.3-2.3483

Abstract

In today's digital era, information quality in information systems is essential for organizational effectiveness and decision-making. This systematic literature review aims to assess and synthesize factors influencing information quality across various systems, focusing on key dimensions such as reliability, accessibility, usability, accuracy, completeness, and timeliness. The existing literature is fragmented, lacking an integrated theory that comprehensively addresses the significance of information quality. Thus, a systematic review was conducted following the PRISMA framework to address this gap and provide evidence-based recommendations for research and practice. Studies were identified, screened, and selected from Scopus and Web of Science. After an initial search using specific keywords, a total of 1,548 articles were found that contained specified terms or strings in various combinations. Of these, 31 studies were chosen for full review based on predefined inclusion and exclusion criteria. The analysis was organized into three primary themes: i) Core Information Quality Factors, ii) Synergizing Information Quality with System and Service Quality, and iii) Impact of Information Quality on User Satisfaction and Organizational Outcomes. The results emphasize the significant role of high information quality in enhancing user satisfaction and operational efficiency. Different industries prioritize various quality dimensions according to their specific needs. Therefore, this review elucidates the imperative function of good quality information in reinforcing information systems' proper functioning, calling for empirical studies to develop holistic frameworks that incorporate multiple dimensions and impact analysis across different domains.
Development of Conventional Lathe Machine Manual User by Using Augmented Reality Frameworks Hamid, Abdul; Puan, Loretta Anak; Tamin, Norfauzi; Maslan, Andi; A.S, Darmawan
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.9.3.2712

Abstract

Machining is one of the familiar subjects in the field of Technical and Vocational Education and Training (TVET) and has been offered at several Vocational Colleges and Institutes of Higher Education (IPT) throughout Malaysia. However, the level of dominance is limited to a handful of students in understanding the learning content and achieving learning outcomes at the end of the course's teaching and learning process. Therefore, this research intends to design and develop a machine manual using an interactive multimedia concept characterized by Augmented Reality (AR). The method of creating forms and developing interactive multimedia routinely uses the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model as a reference model and guideline for implementing learning. The research instruments used were product development expert review forms and student investigation questionnaires. The research respondents consisted of 80 TVET students from Universiti Tun Hussein Onn Malaysia (UTHM) and Tanjung Piai Vocational School. The data obtained is collected and analyzed periodically using statistical-based software. An evaluation is conducted on the product's design, form, content, and functionality. The results of the analysis on the use of interactive multimedia concepts indicate that the average minimum standard for all variables exceeds 3.25, which is interpreted as Highly Acceptable for the Use of Multimedia-Based Learning. Three experts in the field of multimedia and engineering agree that the product developed has a shape that matches the design and can function effectively. Overall, the research found that the design form, content, and functionality of conventional interactive machines can enhance students' visualization abilities in the teaching and learning process, as well as improve their skills when practicing with the devices.
Assessing the ReCODE (Reading, Connecting, Observing, Discussing, and Evaluating) Instructional Model with ICT Assistance: Its Effects on Collaborative Skills and Academic Resilience of Students Saenab, Sitti; Yunus, Sitti Rahma; Saleh, Andi Rahmat; Wulandari, -; Muhiddin, Nurhayani H.
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3277

Abstract

Education in the 21st century requires students to have collaborative skills. Academic resilience is essential for facing various challenges in education. However, several studies have shown that students' collaborative skills and academic resilience are still low. This study aims to determine the effect of the ICT-assisted ReCODE instructional model on students' collaborative skills and academic resilience. This study is a quasi-experimental study with a posttest-only non-equivalent Control Group design. The population in this study consisted of all students in class VIII at SMPN 18 Makassar. The sample in this study was selected using a purposive sampling technique consisting of an experimental class and a control class. The data obtained were analyzed using descriptive and inferential statistics, including an independent t-test with a significance level of 0.05. The results of the inferential statistical analysis of collaborative skills obtained tcount = 1.75 > ttable = 1.67, which means H0 is rejected and H1 is accepted. The inferential statistical analysis of academic resilience yielded tcount = 2.04 > ttable = 1.67, indicating that H0 is rejected and H1 is accepted. Based on this analysis, it can be concluded that the ICT-assisted ReCODE instructional model affects the collaborative skills and academic resilience of class VIII students at SMPN 18 Makassar in the primary material on the human digestive system. The implications of this study suggest the need for further research on the broader application of the ICT-assisted ReCODE learning model to enhance students' collaborative skills and academic resilience.
Handwritten Hiragana Letter Detection Using CNN Fernandi, Arya; Sa'idah, Sofia; Magdalena, Rita
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.8.3-2.3035

Abstract

Hiragana is one of the primary alphabets used in Japanese. Hiragana is a phonetic symbol; each letter represents one syllable. Hiragana letters are formed from curved lines and strokes. However, detecting Hiragana letters causes many errors because people still rely on their vision to detect the letters, especially people familiar with them for the first time. It will be difficult and not very clear to read the letters. Therefore, a Convolutional Neural Network (CNN) method is used to detect handwritten Hiragana letters and help people who first get to know Hiragana letters when the letters are too complicated for human eyes to detect. This research uses the YOLOv8 model as a handwritten Hiragana letter detection algorithm. The Hiragana letters to be detected are basic letters with 46 characters. This research uses the YOLOv8 model run on Google Collaboratory with the Ultralytics library version 8.0.20 using the Python programming language. The dataset is collected from the internet and annotated using the Roboflow framework and dataset 4600 Hiragana letters. From the test results, the best model is YOLOv8l using SGD optimizer and learning rate 0.01 with a precision value of 98.5%, recall value of 95.7%, f1-score value of 97.1%, and mAP value of 95.5%. In the future, we aim to expand the number of datasets and employ a broader range of hyperparameter values to optimize the classification precision and accuracy of the Hiragana Letter Detection system.

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