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All Journal SMATIKA Fountain of Informatics Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Jurnal Nasional Komputasi dan Teknologi Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Jumat Informatika: Jurnal Pengabdian Masyarakat Jurnal Ilmiah Multimedia & Komunikasi INSTALL: Information System and Technology Journal Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Smatika Jurnal : STIKI Informatika Jurnal
Sabrila, Trifebi Shina
Universitas Sari Mulia
Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Neuroscience Social Sciences Transportation Other

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Journal : journal of applied informatics and computing

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Sasirangan Motif Classification Using MobileNetV2 Transfer Learning for Cultural Heritage Preservation Nadia Azaria; Mahdi Mahdi; Muhammad Hanafi; Mambang Mambang; Trifebi Shina Sabrila; Finki Dona Marleny
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12648

Abstract

Sasirangan is a traditional textile from South Kalimantan renowned for its unique motifs and deep cultural significance. However, the preservation of Sasirangan motifs is increasingly challenged by the declining number of skilled craftsmen and inadequate digital documentation. This study presents the development of an automated motif classification system to support the digital preservation of Sasirangan cultural heritage. The system was developed using the MobileNetV2 architecture with transfer learning from ImageNet pre-trained weights, implemented through the TensorFlow framework. A dataset comprising 70 images from 9 different Sasirangan motifs was utilized. To address the limited dataset size, various data augmentation techniques were applied. In the proof-of-concept phase, a binary classification task (Gigi Haruan vs. Unknown) was conducted using an 80:10:10 training-validation-test split. Experimental results demonstrated strong model performance, achieving 96.06% test accuracy for Gigi Haruan motif detection, 96.5% average F1-score, and 98.31% rejection accuracy for non-Sasirangan images. Additionally, a user-friendly web interface based on Gradio was developed, featuring real-time prediction through webcam integration. This study highlights the effectiveness of transfer learning in classifying traditional textile motifs and provides a solid foundation for future advancements, including multi-class classification and cloud-based database integration. The proposed system is expected to contribute significantly to the documentation, education, and preservation of Sasirangan cultural heritage in the digital era.
Agentic AI Adoption: Balancing Enthusiasm and Ethical Concerns An Exploratory Study Nadia Azaria; Mambang Mambang; Finki Dona Marleny; Trifebi Shina Sabrila
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13070

Abstract

Agentic AI represents a significant paradigm shift in artificial intelligence, transitioning from passive command execution to autonomous goal pursuit. This exploratory study investigates user perceptions toward Agentic AI adoption in Indonesia, focusing on the balance between functional enthusiasm and ethical concerns. The 20 Likert-scale statements were developed based on the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and AI trust frameworks. These items cover key dimensions including perceived usefulness, ease of use, trust in autonomy, productivity enhancement, data privacy, loss of human control, algorithmic bias, and adoption intention. Utilizing Exploratory Data Analysis (EDA) supplemented with non-parametric tests on a convenience sample of 22 respondents predominantly tech-savvy young adults this study reveals high familiarity with AI tools (54.5% daily users). Respondents showed strong optimism regarding productivity and efficiency (Positive Aspects mean = 3.52), while maintaining notable ethical concerns (Concern Aspects mean = 3.64). The instrument demonstrated excellent reliability (Cronbach’s Alpha = 0.921 overall). Mann-Whitney U tests indicated significant gender differences on certain items, particularly bias concerns. Due to the small sample size and self-selection bias, findings should be interpreted cautiously. This study provides preliminary insights and highlights the need for human-centric design, transparent governance, and culturally appropriate regulations to support responsible Agentic AI adoption in Indonesia.
Co-Authors Abdul Kadir Abdul latif Abdul Latif Abdul Latif Abdul Latif Abdul Latif Adriani, Adella Agus Eko Minarno Bie, Kalistowen Christian Sri Kusuma Aditya Daud, Excel Delakamin, Putri Ika Finki Dona Marleny Finki Dona Marleny Ghifari, Nabil Najwa Al Hapsari, Erwenda Tri Ilmiyanor, Akhmad Hafi Ivan Cavanneva Royd M. Ulil Albab Mahdi Mahdi Mambang Mambang Mambang Maretmy, Angella Maulina, Riska Muhamad Kurniawan Muhamad Kurniawan, Muhamad Muhammad Azmi Muhammad Azmi, Muhammad Muhammad Hanafi Muhammad Nursandi Muhammad Zulfadhilah Mujaddid Izzul Fikri Mujaddid Izzul Fikri Nabila Khalilah Sabrina Nadia Azaria Nadia Azaria Nisa, Zapiratun Pepriyani, Nor Lintang Rahman, Sultan Nur Ramadhani, Amanda Putri Ramadhani Ramdhani, Amanda Putri Ratna Lindawati Reski, Chatherina Royd, Ivan Cavanneva Sabrina, Nabila Khalilah Sari, Veronica Retno Septian, Daniel Marvelino Septyan Eka Prastya Septyan Eka Prastya Septyan Eka Prastya Subhan Panji Cipta Subhan Panji Cipta Subhan Panji Cipta Suliana, I Made Sultan Nur Rahman Veronica Retno Sari Yahu, Mikhael Neta Yanti, Hardi Yufis Azhar