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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 51 Documents
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Search results for , issue "Vol 9, No 4 (2025)" : 51 Documents clear
Web-Based Deep Learning Approach to Identifying AI-Generated Anime Illustration Johan, Monika Evelin; Wong, Richard Faustine; Godata, Gempar Bambang; Wijaya, Westley; Haezer, Eben
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2899

Abstract

As technology advances rapidly in artificial intelligence, the dominance of generative artificial intelligence (AI) images becomes increasingly evident in art, design, and the creative industry. However, the generative AI has processed numerous images from the Internet, including copyrighted content, trademarks, and artists' illustrations, which pose legal risks. Consequently, the manual tasks involved in managing and classifying these images have become more complex and time-consuming. Therefore, this research proposes the application of deep learning techniques, specifically Convolutional Neural Network (CNN), to automate the process of classifying AI-generated illustrations. The research was conducted by the Cross-Industry Standard Process for Data Mining (CRISP-DM) method. Initially, the study began with a literature review to describe the state-of-the-art in image detection. Then, a dataset of illustrations was collected from the Pixiv website using web scraping techniques. After data cleaning, separation, and augmentation, three pre-trained models were created and compared on 1200 training data and evaluated against 400 testing and 400 validation data. From the evaluation, the model using MobileNet V3 Large architecture achieved an impressive 94% accuracy, outperforming MobileNet V2 and Inception V3 architectures, respectively by 3% and 5%. Thus, the implementation of CNN holds the promise of providing an efficient solution for identifying and classifying various types of AI anime illustrations, benefiting consumers and artists practically. Future research could consider incorporating additional data categories and variations to further enhance the model's ability to distinguish between AI-generated and human-made illustrations.
Comparative Analysis of Weighted-KNN, Random Forest, and Support Vector Machine Models for Beef and Pork Image Classification Using Machine Learning Satria, Budy; Afrianto, Nurdi; Ningsih, Lidya; Sakinah, Putri; Sidauruk, Acihmah; Mayola, Liga
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3736

Abstract

The actual problem that occurs in the sale of meat by some conventional market traders is mixing beef with pork because of the high selling price. The difference between pork and beef lies in the color and texture of the meat. However, many people do not understand this difference. This study aims to provide a solution to distinguish the two types of beef through a classification process by obtaining the best accuracy using the W-KNN, RF, and SVM models based on machine learning. This study compares the model's performance based on the number of datasets, comprising 400 original images (200 beef and 200 pork images), using a 80:20 ratio for training and test data. The extraction process uses two algorithms: HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue). The model evaluation uses a confusion matrix that includes accuracy, Precision, Recall, and F1-score. Based on the results of the model testing, it was found that the random forest algorithm gave the best overall results, with the highest accuracy of 98.75%, Precision of 97%, F1-score of 98%, and recall of 99% on the number of decision trees of 400. This shows the stability and generalization of the superior model. The random forest algorithm is the most effective for classifying beef and pork data with minimal errors. Implications for further research include using a deep learning approach, especially for image processing, to detect differences in each meat characteristic and increase accuracy.
Analysis of Stakeholder Collaboration in Local Rice Seed Governance in West Sumatra: Fuzzy Delphi Approach to Improve Food Security and Sustainable Agriculture Masruri, -; Hadiguna, Rika Ampuh; Suliansyah, Irfan; Henmaidi, -
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3499

Abstract

Local rice seeds are crucial to Indonesia's food security and agricultural sustainability, especially in West Sumatra. Seed management has yet to achieve seed independence, hindering the development of breeders and seed users. Overcoming this requires collaboration between stakeholders because locals can take collective responsibility and build trust in the Government through collaborative seed governance. This study introduces the concept of assessing the level of collaboration between stakeholders to analyze the stages of cooperation currently occurring in the governance of local rice seeds in West Sumatra. This study employs a mixed-methods approach, combining quantitative and qualitative methods. The study involved interviews with experts and a literature review, identifying 14 indicators influencing collaboration in seed governance. After validation by 5 (five) Experts, 13 (thirteen) indicators were obtained for further analysis using the Fuzzy Delphi, which was applied through a questionnaire distributed to 30 (thirty) informants representing stakeholders in five Regencies/Cities selected purposively in West Sumatra Province. The results were analyzed using a Likert scale and converted to fuzzy logic. These indicators are categorized into 5 (five) characteristics of collaboration. This study found that the level of collaboration between stakeholders is at the "cooperation" stage, which shows a significant lack of effectiveness in the interaction between stakeholders. This study offers valuable insights for stakeholders to enhance the collaborative process of local rice seed management, thereby achieving food security and sustainable agriculture.
The Effect of Feature Selection on Machine Learning Classification Pardede, Jasman; Dwianto, Rio
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2926

Abstract

High-dimensional datasets can lead to overfitting and computationally expensive model building on machine learning. This study uses a dimensionality reduction technique, namely feature selection techniques, to overcome these problems. Five feature selection methods were used, i.e., Chi-Square (CS), Information Gain (IG), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Least Absolute Shrinkage and Selection Operator (LASSO), and three classifier methods viz. Naïve Bayes, Extreme Gradient Boosting (XGB), and RF Classifier. The dataset used is the Heart Attack Analysis & Prediction Dataset. In this study, three scenarios of the best feature selection were carried out, namely: 1. selection of the best feature using a specific feature selection, 2. the intersection of selection of the best feature from the same category, 3. the intersection of selection of the best feature from the five proposed feature selection methods. The performance model is measured using accuracy, precision, recall, f1-score, AUC, and training time. This study reveals that feature selection is very effective in improving the performance of prediction models. Based on the experiment results, the best feature selection is CS and IG in the Filter Category with the XGB model. The best feature selected improved the performance of accuracy, precision, recall, f1-score, and AUC, i.e., 1.7%, 1%, 2.3%, 1.6%, and 0.2%, respectively. Meanwhile, training time requirements decreased by 23.5%. Feature selection with specific techniques performs better than feature selection by selecting the best features from the same category feature selection technique or various other feature selection methods.  
Smart Early Detection of Rheumatoid Arthritis Tool on Nails with a Certainty Factor Technology Approach Based on Image Processing Octavio, Abi Mufid; Syafaah, Lailis; Vhirdausia, Nuri; Wijaya, Frenischa Yincenia; Hery Soegiharto, Achmad Fauzan; Faruq, Amrul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3252

Abstract

This study developed the Smart Early Detection Rheumatoid Arthritis (SEDRA) tool, designed to diagnose RA at an early stage by analyzing nail conditions. Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects joints, commonly in older individuals. Left untreated, RA can lead to severe complications such as pain, fatigue, paralysis, and even death. Early detection is essential to mitigate these effects. The research utilized advanced image processing techniques, MATLAB, Python, and a certainty factor approach. The experimental method involved capturing nail images, which were then processed in MATLAB to identify abnormalities associated with RA. Key nail indicators, including yellowing, brittleness, bloody splinters, textured surfaces, and jagged or perforated patterns, were validated using certainty factor technology to ensure diagnostic accuracy. The findings indicate that SEDRA effectively identifies RA through these nail features, providing accurate and timely diagnostic results. The results showed that this tool can detect Rheumatoid Arthritis disease through yellowing, brittle nails, bloody splinters, textured nails, and jagged or perforated nails. SEDRA was created to meet the needs of innovation in the health sector. SEDRA represents a breakthrough in health technology, providing a practical tool for early RA detection that can be integrated into primary healthcare systems. Its implications include improving patient outcomes by enabling early intervention and monitoring. Future research should focus on enhancing the diagnostic accuracy of SEDRA, expanding its applicability to diverse populations, and integrating it with mobile or wearable technologies to increase accessibility and usability in remote or underserved areas.
Affective-Avatar: Remote Sharing Non-Verbal Affective Data through 2D Animated Avatar in a Remote Meeting Ming, Teo Rhun; Norowi, Noris Mohd; Rahmat, Rahmita Wirza O. K.; Kamaruddin, Azrina
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2857

Abstract

This paper describes the use of an affective avatar that detects and shares non-verbal affective data in a 2D-based remote meeting application. It represents the foundation of the design and compares the effects of the affective-avatar with other modes in 2D-based remote meeting applications (audio-only, video, and affective-avatar) in the context of emotion sharing, social presence, and user preferences. A prototype application was developed to detect facial expressions and hand gestures, transmit affective data, and animate these expressions on an affective avatar using emoji cues. A user study involving 18 participants was conducted, in which participants were asked to perform a collaborative drawing task using the prototype application. The study found that emotion sharing remained consistent across all conditions, with no notable differences. The study also revealed that both video and affective-avatar modes fostered a stronger sense of social presence compared to the audio-only mode. Although there was no significant quantitative difference between the video and affective-avatar, participants generally preferred using either mode over audio-only. In the future, researchers aim to explore methods for enhancing detection accuracy in challenging lighting conditions. The study also wishes to improve the performance of the prototype application. As the research progresses, there is the possibility of strengthening the current prototype with additional methods for sharing nonverbal and affective information, such as physiological signals and body movements.
Maximizing Prospective Students through Instagram-Driven Influence Maximization for University Branding Ma'ady, Mochamad Nizar Palefi; Hidayat, Alifiansyah Arrizqy; Nasution, Anita Hakim; Kusumawati, Aris; Insani, Rokhmatul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3385

Abstract

In many developing countries, private universities face considerable financial challenges due to their limited sources of income. Unlike public universities, which receive substantial government subsidies, private institutions must rely primarily on tuition fees and student enrollment to sustain their operations. This financial dependency pushes private universities, particularly in Indonesia, to invest heavily in branding and marketing to attract prospective students. Social media platforms, especially Instagram, have emerged as crucial tools in these efforts. Instagram enables universities to engage with potential students directly, providing a space for showcasing academic programs, campus life, and student achievements. This paper’s goal is to explore the potential of Instagram-driven influence maximization (IM) techniques for enhancing university branding. Traditionally, these techniques are employed in product marketing to increase consumer engagement and sales, but their application in higher education remains relatively unexplored. We aim to address this gap by examining three promising IM methods that private universities can adopt: first, leveraging Instagram’s broadcast channel feature to communicate effectively with large audiences in real time; second, selecting key opinion leaders (KOLs) from influencer marketplace platforms to enhance credibility and broaden outreach; and third, optimizing the use of targeted hashtags to increase discoverability and engagement. As a result, we provide a review of recent evidence supporting the effectiveness of these strategies in higher education marketing. By adopting these techniques, private universities can improve their digital presence, enhance brand awareness, and increase student enrollment. IM with uncertainty is a challenging educational landscape for future research.
Levenshtein Distance Algorithm in Javanese Character Translation Machine Based on Optical Character Recognition Pradana, Musthofa Galih; Seta, Henki Bayu; Irzavika, Nindy; Saputro, Pujo Hari; Rusiyono, Ruwet
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3151

Abstract

Indonesia has diverse art, cultures, and languages. Linguistically, Indonesia has many local languages, which makes it a diverse country, with Javanese being the regional language with the highest number of entries in the Kamus Besar Bahasa Indonesia. The Javanese script, one of the cultural symbols of Java, differs significantly from the Latin script commonly used in daily communication. In the context of cultural preservation, which is also one of the ministry's strategic steps, a translation or transfer process is needed from the Javanese script to the Latin script to the Indonesian language as an active participation in culture, with technology helping promote and introduce Indonesian culture. This study develops an algorithm-based approach to capture data images and improve translation accuracy. Transliteration is further enhanced by incorporating optical character recognition to convert character images. The study also applies a convolutional neural network (CNN) algorithm for character image recognition and a Levenshtein distance algorithm to translate Latin characters into Indonesian. The convolutional neural network (CNN) algorithm achieved an optimal % image detection accuracy of 95% at the 21st epoch. The translation process yielded a 90% word-level translation accuracy and 70% sentence-level accuracy. These results indicate that sentence translation remains suboptimal due to a lack of sufficient training data and similarities between scripts, highlighting the need for further improvements through transformer models or data augmentation.
Multi-label Aspect Dangerous Speech Classification Using Keyword-Driven Ensemble Classifier on Imbalanced Data Findawati, Yulian; Budi Raharjo, Agus; Adni Navastara, Dini; Yonathan, Vincent; Yatestha, Anak Agung; Purwitasari, Diana
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3129

Abstract

This study aims to detect various aspects of dangerous speech on social media, particularly Twitter, which has the potential to incite violence and increase prejudice against specific communities. The research dataset includes tweets containing dangerous speech related to the Indonesian government from 2019 to 2022. Researchers manually labeled the data based on seven aspects of hazardous speech, including social and historical context, dehumanization, accusations in the mirror, threats against women/children, questioning in-group loyalty, and threats against groups. The study employs a multi-label classification method to handle these aspects, which appear simultaneously in a single text. The main challenges include data imbalance, ambiguity, and the informal language frequently appearing in tweets. This study introduces a Keyword-Driven Ensemble Classifier (KDEC), a new ensemble model that leverages the strengths of SVC, Logistic Regression, IndoBERTweet, and specific keyword lists for each label. Researchers designed KDEC based on the best results from machine learning and deep learning methods tested in this study. The research team tested the model on small and large datasets, conducting trials involving seven and four-label classifications. The results show that KDEC, with label reduction and keyword support, effectively addresses data imbalance, resolves label overlap, and achieves 92% accuracy for seven-label classification and 88% for four-label classification. The findings of this research are highly relevant for hate speech analysis across various platforms and languages, particularly in understanding context and conveyed messages. Additionally, this study provides valuable insights into managing harmful content in online government-related discussions. This method identifies dangerous speech on a larger scale and supports data-driven social media content regulation decision-making.
Systematic Literature Review of Gender Bias within Video Games Character Design Ibrahim, Najwa Sabirah; Senan, Norhalina; Othman, Muhammad Fakri; Azmi, Shahdatunnaim; Erianda, Aldo; Gusman, Taufik
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3297

Abstract

Gender bias in video games refers to the unequal treatment and discrimination that players experience based on gender, which is often normalized within the gaming community. Gender bias is widespread in Multiplayer Online Battle Arena (MOBA) games, where it can take many different forms. Common examples include assumptions made about players' abilities, character design in games, and the roles given to characters according to gender. This situation has created an unwelcoming environment, especially for female players, leading to feelings of exclusion. This study conducts a systematic literature review to examine gender bias in MOBA games, explicitly focusing on character representation, hypersexualized character models, and gameplay mechanics. By analyzing data from peer-reviewed articles, theses, and research papers, the study highlights the recurring patterns of bias and identifies gaps in current approaches. Although prior studies have explored the elements that contribute to gender bias, few studies have offered practical solutions to mitigate this bias. However, there is still a lack of research proposing a practical game design framework that integrates strategies to reduce this bias. In conclusion, efforts to address gender bias are not only significant in terms of design ethics, but also a good strategy in expanding the game's audience. This study identifies possible solutions that might help future research and be developed into a conceptual framework model that developers can understand to create a more inclusive, fair, and profitable gaming environment in the long term.

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