2 research outputs found
Perancangan Dasbor Pra-Bencana Tsunami di Gugus Mitigasi Lebak Selatan: Visualisasi dan Mitigasi di Panggarangan
No full textTsunami telah meninggalkan tragedi di Indonesia, salah satunya tsunami Selat Sunda pada Lampung dan Banten. Diperlukan mitigasi bencana, tetapi kurangnya sinergi Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) dengan pelaporan erupsi Pusat Volkanologi dan Mitigasi Bencana Geologi (PVMBG) menghambat upaya mitigasi dampak tsunami Selat Sunda. Untuk meningkatkan sinergi, dirancang dasbor pra-bencana tsunami dengan tujuan memaksimalkan pengurangan risiko bencana (PRB) di Panggarangan, Lebak. Perancangan dilakukan dengan metodologi Prototype, yang dilakukan secara sistematis mulai dari analisis kebutuhan GMLS, perancangan dengan menggunakan business intelligence tool bernama Tableau, pengujian dengan kuesioner System Usability Scale, kemudian revisi hingga dasbor sesuai kebutuhan. Harapannya, rancangan dasbor dapat meningkatkan sinergi antar-stakeholders, terutama di Panggarangan, serta mendukung pengambilan keputusan baik pihak yang berkepentingan dalam perencanaan tata kota maupun mitigasi bencana
Web-Based Deep Learning Approach to Identifying AI-Generated Anime Illustration
No full textAs 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
