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All Journal JOIV : International Journal on Informatics Visualization Jurnal ULTIMA InfoSys Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Information Systems and Informatics PROFICIO: Jurnal Pengabdian Masyarakat Jurnal Informatika Ekonomi Bisnis
Johan, Monika Evelin
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Humanities Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Education Mechanical Engineering Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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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.
Implementation of Customer Segmentation Model using K-Means and DBSCAN for Fashion Industry Product Transaction William, William; Johan, Monika Evelin
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.9.6.2978

Abstract

The use of online marketplaces is rapidly expanding in Indonesia, particularly within the fashion industry. To develop effective marketing strategies, it is essential to understand consumer behaviour through customer segmentation. With a deeper understanding of consumer behaviour, XYZ company, which is engaged in the fashion industry, can improve the effectiveness of marketing strategies and respond to consumer needs more accurately to achieve a significant increase in sales. This study aims to implement a customer segmentation model using clustering methods with machine learning algorithms, specifically K-Means and DBSCAN, following the CRISP-DM Data Mining Framework for data processing. The research utilizes purchasing transaction data from XYZ fashion industry, applying pre-processing techniques such as Standard Scaler and PCA before clustering. The K-Means and DBSCAN algorithms are implemented and evaluated using Silhouette Score and Davies-Bouldin Index matrices. Results show that the K-Means algorithm outperformed DBSCAN, achieving an optimal cluster number of k=7 with a Silhouette Score of 0.549 and a Davies-Bouldin Index of 0.593, compared to DBSCAN's Silhouette Score of 0.29 and Davies-Bouldin Index of 0.92. The final implementation involves creating a dashboard that automatically processes data and generates clusters to support customer segmentation decisions. The model was deployed through a simple website using FastAPI for backend Python execution and React with TypeScript for the front end. Future studies could address limitations by incorporating recent datasets to improve model accuracy, exploring alternative algorithms like Gaussian Mixture Models (GMM) for additional insights, and focusing on robust deployment strategies for real-world applications within the fashion industry.
Co-Authors Aulia, Azka Erick Fernando B311087192 Faza, Ahmad Fianty, Melissa Indah Godata, Gempar Bambang Haezer, Eben Ririn Ikana Desanti Sobiyanto, Sobiyanto Soegono, Joaquin Noah Sugara, Victor Ilyas Sulaiman, Agus Suryasari Susanto, Meitio Tjahjana, David Varrel, Primus Kartika Veronica, Mella Margareta Wella Widjaya, Ageng Cahyo Wijaya, Kimi Axel Wijaya, Matthew Chandra Wijaya, Westley William William WINANTI, WINANTI Wiratama, Jansen Wong, Richard Faustine Wongso, Bernard Pratama Yulius Denny Prabowo, Yulius Denny