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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 52 Documents
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Search results for , issue "Vol 9, No 1 (2025)" : 52 Documents clear
Classification of Arabic Documents with Five Classifier Models Using Machine Learning Najjar, Esraa; A. Alkhaykanee, Nibras.; Breesam, Aqeel Majeed
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2539

Abstract

Automated document classification is becoming important and highly required for many applications, particularly in light of the exponential increase of Arabic-language internet documents. Text classification is a big data issue and an essential aspect of our lives; classifying content in a typical Arabic text is a significant and arduous challenge. The process of classifying a document involves placing it in the appropriate class or category. The major goal of this work is to use pre-processing techniques to evaluate the effectiveness of machine learning (ML) algorithms. The inclusion of preprocessing in this research methodology is vital. This study uses machine learning methods to classify different Arabic documents and uses five well-known classification systems' performance in categorizing the documents. This work used a model developed using various algorithms, namely Support Vector Machines, Naive Bayes, Logistic Regression, K-Nearest Neighbors, and Random Forest, for the classification procedures. The findings indicate that SVM achieved the highest performance evaluation, boasting an accuracy of 98%, surpassing all other algorithms employed in this study.
Canva-based Animation Comic Video Media in Informatics Learning at SMP Negeri 14 Padang Huda, Asrul; Sari, Liza Mustika; Effendi, Hansi; Giatman, Muhammad; Firdaus, -; Sukmawati, Murni
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3177

Abstract

This research addresses challenges in conventional learning media, limited facilities, and suboptimal use of technology, which hinder student material mastery, motivation, and independence. The study aims to develop and validate Canva-based animated comic video learning media for Informatics subjects in class VIII at SMP N 14 Padang. Using the Research and Development (R&D) method with a 4D development model—define, design, develop, disseminate—primary data were collected from validators, teachers, and students. Descriptive and inferential analyses were employed to evaluate the validity and practicality of the media. The learning media achieved high validity scores: 0.963 for media design and 0.975 for material content. Expert evaluations highlighted the media’s effective visual design, systematic content presentation, and alignment with curriculum objectives. Practicality was confirmed with average scores of 97.04% from teachers and 93.14% from students, who appreciated its ease of use, accessibility across devices, and engaging, interactive features that support both independent and collaborative learning. This study underscores the importance of integrating technology into learning media to enhance education quality. Canva-based animated comic videos are not only applicable to Informatics but also have potential for adaptation to other subjects. The combination of visual, audio, and interactive elements fosters engaging, flexible, and impactful learning experiences for students. Future research could explore AI integration for personalized learning and broader testing across diverse student groups and subjects. This research provides a foundation for developing innovative, accessible, and inclusive technology-based learning tools to improve education quality in the digital era.
Disease Classification by SVM and GBC Algorithms AL Kafaf, Dhrgam; Thamir, Noor N
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2557

Abstract

Artificial intelligence (AI) application in disease classification is a rapidly growing area of interest for medical practitioners in diagnosing illnesses. This work provides a comprehensive study on the application of AI, particularly Machine Learning (ML) algorithms, for predicting diseases based on symptoms in healthcare. The research wants to improve the diagnosis of illnesses using symptom data by utilizing two popular ML algorithms, the Support Vector Machine (SVM) and the Gradient Boosting Classifier (GBC). The research utilizes a dataset containing 4,921 items, split into 80% for training and 20% for testing. The methodology section includes information on the procedures for collecting and preparing data, such as importing data, handling missing values, categorizing symptom severity, and dividing the data. Subsequently, a range of measurement performances such as F1 score, accuracy, precision, and recall are utilized to evaluate the model technology's effectiveness. The default hyperparameters of the GBC model are used for evaluation, while the SVM model is optimized through parameter adjustments using GridSearchCV.  The effectiveness of the GBC model is evaluated utilizing similar metrics, while the SVM model demonstrates high accuracy across different hyperparameter configurations. The research suggests that ML algorithms have the potential to enhance the precision of predicting illnesses, and it also considers the significance of these discoveries within the broader scope of AI in healthcare. The research sets the stage for potential explorations in this field, emphasizing the importance of continual research and enhancement of AI techniques to enhance healthcare outcomes.
Simulation of Land Use and Land Cover of Peatland Bengkalis Using QGIS Fauziah, Fauziah; Hayati, Nur; Prasetyo, Lilik Budi
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2432

Abstract

The phenomenon of forest and peatland fires in Bengkalis Regency is inseparable from the change in land use and cover (LULC). The dynamic LULC in Bengkalis Regency is caused by economic factors sourced from land-based resource management. As a result, negative impacts such as environmental damage can trigger fires. Therefore, this study attempts to observe the LULC patterns on peatlands in the Bengkalis Regency using overlay techniques using QGIS. QGIS functions unlock the software's full potential, empowering you to manipulate data, automate workflows, create custom expressions, and perform advanced spatial analysis—all within a single platform. There are 12 LULC that can be identified on peatlands in Bengkalis Regency, including plantations (42.98%), primary forests (42.68%), shrubs (12.29%), residential and activity areas (0.71%), fields/farmlands (0.64%), lakes/ponds (0.43%), empty/bare land (0.18%), rivers (0.05%), and ponds, ponds, mangrove forests, and rice fields ranging from 0.004% to 0.008%. In addition, in the Bengkalis Regency, concession areas of at least 175,081.19 Ha are in the Peatland Ecosystem Protection Function (FLEG). LULC simulation provides a powerful tool for assessing the potential impact of various development plans and policies on society, the economy, and the environment, enabling more sustainable and responsible choices. A comprehensive understanding of land use and land-cover patterns is essential for further research on sustainable resource management and climate change mitigation. While LULC research has advanced significantly, several critical questions require further investigation
Exploring the Capabilities of GPT Models in Drafting Course Assessments Based on Bloom’s Taxonomy Muhamad, Gilang Aulia; Alsulami, Bassma Saleh; Thabit, Khalid Omar
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2811

Abstract

The application of Generative Pre-trained Transformer (GPT) models is significantly essential in automating drafting course assessment based on Bloom’s Taxonomy, specifically GPT-3.5-turbo, GPT-4, and GPT-4o. Therefore, this study aimed to explore the interaction between Artificial Intelligence (AI) models and educational content using refined prompt engineering methods to enhance the accuracy and relevance of the generated questions. For the investigation, the processing 146 Course Learning Outcomes (CLOs) method was applied through each model using OpenAI Application Programming Interface (API). Metrics such as 'Accuracy', 'Precision', 'Recall', and 'F1 Score' were used to assess the performance of each model. The results showed that GPT-4 was suitable for complex course assessments, showing superior performance in delivering detailed and precise responses. A cost-effective solution was obtained using GPT-3.5-turbo for generating simpler course assessment, while GPT-4o provided a middle ground, balancing cost, and performance. The results showed the potential of AI to reduce the administrative burden on instructors by streamlining the creation and refinement of course assessments. The enhancement of course assessments was also facilitated by automation, thereby supporting more adaptive questions. The potential for broader AI integration into educational practices promised a transformative impact on traditional course assessment drafting methods, enabling more dynamic and educational experiences. Moreover, further studies were recommended to explore the ethical dimensions of AI in education, the ability to handle diverse tasks, as well as assess the long-term impacts on learning outcomes and educational equity.
An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification Elvitaria, Luluk; Ahmad, Ezak Fadzrin; Samsudin, Noor Azah; Ahmad Khalid, Shamsul Kamal; Salamun, -; Indra, Zul
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2591

Abstract

Batik is one of Indonesia's most important cultural arts and has received recognition from UNESCO. Batik has high artistic and historical value with a variety of patterns. Currently, Indonesia has 5,849 batik motifs which are generally classified based on shape, color, motif and symbolic meaning. The diversity of batik motifs makes it difficult for ordinary people to fully recognize them. This paper intends to develop an automatic framework for classifying batik motifs as a solution to overcome this issue. To develop this classification automation framework, the paper proposes a new architecture based on deep learning, which is named Okta-net. The architecture consists of 8 convolutional layers with separate convolution operations (SeparableConv2D). The output of the last convolution block will be fed to the fully connected layer using global average pooling. Meanwhile, in developing a deep learning model to classify batik image patterns, a dataset of 5 batik classes (motifs) was organized, consisting of 4,284 batik images. Through a series of experiments carried out, the proposed Okta-Net architecture succeeded in achieving satisfactory results with a validation accuracy of 93.17%, Precision of 91.60%, Recall of 92.28%, F-1 Score of 91.54%, and a loss of just 0.12%. Thus, it can be concluded that Okta-Net architecture can help preserve Indonesia's batik cultural heritage by accurate batik motif’s classification. Apart from that, based on a comparison of research outcomes, Okta-Net outperformed most of earlier studies, the majority of which had an accuracy of below 90%.
Determining the Grade of Robusta Coffee Beans of Lampung, Bengkulu, and South Sumatra Provinces by Using the Analytical Hierarchy Process (AHP) Yuniarthe, Yodhi; Syarif, Admi; Gitosaputro, Sumaryo; Warsito, Warsito
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2667

Abstract

Coffee is an important commodity for the world business community. One of the world's leading coffee producers is Indonesia. In Indonesia, several provinces produce coffee beans, especially in Sumatra island. They generally cultivate robusta-type coffee. The determination of coffee quality here is still done manually. Recently, along with the increasing recognition of computers, several decision-support system approaches have been introduced, including the Analytical Hierarchy Process (AHP). This research aims to implement the AHP to assess Indonesian robusta coffee beans (Lampung, Bengkulu, and South Sumatra). The researchers use a systematic process, including the preparation stage, data collection using datasets, determination of criteria and alternatives, hierarchical structure, creation of matrices to compare pairs, calculation of priority vectors and eigenvector values, and accuracy testing. This research uses six criteria with 19 sub-criteria and seven alternatives. From the rankings calculated using the AHP method for coffee production areas, the best quality coffee bean is in West Lampung, with the highest value of  0.28. The results of this study are compared with those given by an expert. The results show the MAPE error of 4.42%, a very accurate category.  Thus, it is shown that this method provides excellent results. Future research can be conducted to develop a more sophisticated and efficient AHP method for multi-criteria decision-making in various fields such as business management, engineering, environment, and health.
RC5 Performance Enhancement Based on Parallel Computing Abead, Suaad Ali; Ali, Nada Hussein M.
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2675

Abstract

This study aims to enhance the RC5 algorithm to improve encryption and decryption speeds in devices with limited power and memory resources. These resource-constrained applications, which range in size from wearables and smart cards to microscopic sensors, frequently function in settings where traditional cryptographic techniques because of their high computational overhead and memory requirements are impracticable. The Enhanced RC5 (ERC5) algorithm integrates the PKCS#7 padding method to effectively adapt to various data sizes. Empirical investigation reveals significant improvements in encryption speed with ERC5, ranging from 50.90% to 64.18% for audio files and 46.97% to 56.84% for image files, depending on file size. A substantial improvement of 59.90% is observed for data files sized at 1500000kb. Partitioning larger files notably reduces encryption time, while smaller files experience marginal benefits. Certain file types benefit from both strategies. Evaluation metrics include encryption execution time and throughput, consistently demonstrating ERC5's superiority over the original RC5. Moreover, ERC5 exhibits reduced power consumption and heightened throughput, highlighting its multifaceted benefits in resource-constrained environments. ERC5 is developed and tested on various file types and sizes to evaluate encryption speed, power consumption, and throughput. ERC5 significantly improves encryption speed across different file types and sizes, with notable gains for audio, image, and large data files. While partitioning smaller files only slightly improves encryption time, larger files partitioning yields faster results. Future research could explore ERC5 optimizations for different computing environments, its integration into real-time encryption scenarios, and its impact on other cryptographic operations and security protocols.
Examining the Impact Factors Influencing Higher Education Institution (HEI) Students’ Security Behaviours in Cyberspace Environment Syed Zulkiplee, Syed Muzammer; Mohd Shukran, Mohd Afizi; Isa, Mohd Rizal Mohd; Adib Khairuddin, Mohammad; Wahab, Norshahriah; Hidayat, Hendra
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2296

Abstract

The Internet’s increasing connectivity through devices and systems, particularly with the Internet of Things (IoT), has expanded the threat landscape, making cybersecurity a constantly evolving field. Phishing is a common and emerging cyber-attack that attempts to deceive individuals and persuade them to disclose sensitive information, such as passwords, financial information, or personal data. Researchers have studied phishing extensively in recent years to understand its mechanisms, strategies, and potential solutions. This research examines essential factors that affect how online users behave regarding security in cyberspace, focusing on phishing attacks through the Health Belief Model (HBM). Understanding what influences users' security behaviors is crucial for building strong defenses. A survey was sent to students via WhatsApp and email, with 252 participants. The results were analyzed using quantitative methods. Principal Component Analysis (PCA) revealed that perceived barriers, self-efficacy, and privacy concerns were the main determinants of students' security behaviors. Students were particularly concerned about the misuse of their personal information. Despite varying levels of formal cybersecurity education, most students demonstrated confidence in configuring web browser security settings. The findings underscore the importance of tailored educational interventions and user-friendly security tools. Future research could explore additional security issues such as spyware, adware, and spam attacks. Additionally, leveraging machine learning and deep learning algorithms offers promising avenues for enhancing phishing detection and mitigation strategies. Furthermore, this study contributes to understanding cybersecurity behaviors, providing valuable insights for policymakers, educators, and developers to foster a safer online environment.
Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection Megawaty, Dyah Ayu; Damayanti, Damayanti; Sumanto, Sumanto; Permata, Permata; Setiawan, Dandi; Setiawansyah, Setiawansyah
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2744

Abstract

The purpose of this research is to propose a new approach in the criteria weighting method using the RECA method, the RECA method can help provide a systematic and structured framework for determining criteria weights in multi-criteria decision making. The determination of weights using the RECA method is to increase objectivity and accuracy in the candidate assessment and selection process by determining the appropriate weight for each criterion based on responses and assessments from experts or stakeholders. Testing the RECA Method with Multi Attribute Decision Making (MADM) techniques is an important step in measuring the effectiveness of the RECA Method in the context of multi-criteria decision making. Ranking tests using Spearman correlation between the RECA method and other methods such as SAW with a correlation value of 1, MOORA with a correlation value of 0.9636, MAUT with a correlation value of 0.9515, WP with a correlation value of 0.891, SMART with a correlation value of 0.9636, and TOPSIS with a correlation value of 0.8788 show a high level of rank consistency between the RECA method and these methods. This indicates that the RECA Method has a strong ability to generate similar candidate rankings with other methods, validating its reliability and consistency in the context of multi-criteria decision making. Implications for further research include exploring the application of the RECA method in different decision-making contexts other than recruitment, such as performance evaluation, project selection, or supplier selection. Further research could investigate the integration of the RECA method with other decision-making methods or algorithms to improve its performance and applicability in complex decision environments. Comparative studies with larger sample sizes and diverse datasets can provide deeper insights into the effectiveness and reliability of the RECA method compared to other methods.

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