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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Abdi Insani
Sari , Julia Purnama
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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PENERAPAN EDUTAIMENT POLA ASUH ANAK SEBAGAI PENDIDIKAN KARAKTER PADA KELUARGA MISKIN Daryati, Melia Eka; Sari , Julia Purnama
Jurnal Abdi Insani Vol 11 No 4 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i4.1886

Abstract

This community service activity aims to apply the concept of edutainment in childcare as an effort to educate character in poor families. Edutainment is a combination of education and entertainment, expected to be an effective method in educating children's character in underprivileged family environments. The implementation of community service is carried out for parents in Rawa Makmur Village who are included in the data of 28 hopeful family recipients. The methods used include developing materials and forming caregiver groups. The stages of community service are carried out through preparation of activities carried out because of the program family capacity improvement meeting, implementing technical assistance activities in the form of education in the form of counseling and mentoring, and reporting activities. The results of community service are expected to show an increase in parents' understanding of the importance of character education, positive changes in childcare patterns, and better development of children's character in poor family environments. The sustainability of community service activities through the formation of a community of parents who care about children's character education, providing edutainment materials that can be accessed sustainably, and collaborating with educational institutions and local governments to support this program in the long term.
Application of Two-Stream Late Fusion on EfficientNetV2 based on Transfer Learning to classify AI-generated paintings Rinaldi, Muhammad Kevin; Ernawati , Ernawati; Andreswari , Desi; Sari , Julia Purnama
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15814

Abstract

The rapid advancement of generative artificial intelligence (AI) has made synthetic digital paintings increasingly difficult to distinguish from human-made artworks, raising concerns regarding authenticity, copyright protection, and digital forensics. The main objective of this research is to develop a reliable and interpretable framework for distinguishing AI-generated paintings from human-created artworks by integrating visual and noise-based features. To address the limitations of conventional single-stream CNN models, this study proposes a Two-Stream Network with a Late Fusion strategy, combining a visual stream based on EfficientNetV2-S and a noise stream based on Xception with Spatial Rich Models (SRM).The proposed architecture processes semantic visual features and residual noise characteristics independently, followed by weighted decision-level fusion with a ratio of 0.7:0.3. Experiments were conducted using the AI-Artwork public dataset from Kaggle, consisting of 15,000 images with a data split of 64% training, 16% validation, and 20% testing. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC, ensuring a comprehensive assessment beyond accuracy alone. The results demonstrate that the proposed method achieves 98% accuracy, 98% precision, a 99% F1-score, and high discriminative capability compared to single-stream baselines. Model interpretability was analyzed using Grad-CAM to examine the contribution of each stream. Despite promising results, this study is limited by evaluation on a single dataset and static fusion weights, which may affect generalization to unseen generative models. Future work includes cross-dataset evaluation, adaptive fusion strategies, and exploration of lightweight architectures. Practically, this approach has potential applications in digital art authentication, forensic analysis, and content moderation systems, as well as supporting emerging policies for AI-generated content regulation and copyright protection.
Co-Authors Andreswari , Desi Ernawati , Ernawati Melia Eka Daryati Rinaldi, Muhammad Kevin