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All Journal IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi JOIV : International Journal on Informatics Visualization Syntax Literate: Jurnal Ilmiah Indonesia JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Electrical Engineering and Computer Science
Rahadianti, Laksmita
Faculty Of Computer Science, Universitas Indonesia, Depok, Indonesia
Humanities Automotive Engineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Automated Assignment of Community Reports With Early Fusion Multimodal Transformer Hanif, Ikhlasul Akmal; Tjitrahardja, Eduardus; Naufal, Rahmat Bryan; Rahadianti, Laksmita
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1426

Abstract

In the current digital era, city governments require effective and responsive platforms to handle public reports and feedback. One such example is Cepat Respon Masyarakat (CRM) in Jakarta, Indonesia, which allows residents to report various issues to the city government, such as infrastructure damage, traffic accidents, and environmental problems. However, after a report is created, it must be assigned to the appropriate agency. Currently, this assignment process is a challenge, taking an average of nearly two hours. To improve the efficiency and responsiveness of handling public reports through the CRM platform, this research proposes an innovative, multimodal solution for classifying public report data, using both text and images to automatically assign community reports. The proposed method was trained and evaluated using a dataset built from real CRM data. Experiments showed that the multimodal model, using a fusion of the DINOv2 transformer and Multilingual E5 with the Early Fusion method, achieved 80.73% accuracy, an increase from the 68.9% achieved by BERT and ResNet. The results of this research are expected to expedite the issue reporting process and enhance the effectiveness of public services, ultimately contributing to the prosperity of all Indonesian citizens in this era of technological advancement.
Co-Authors Aniati Murni Arymurthy Caryn, Femilia Hardina EVI YULIANTI Hamidiyati, Nazifa Hanif, Ikhlasul Akmal Hartanto, Cahyo Adhi Kardawi, Muhammad Yusuf Naufal, Rahmat Bryan Nugraha, Made Prastha Persada, Bayu Satria Raihan Taufiq, Muhammad Feriansyah Saragih, Frederic Morado Steven Christ Pinantyo Arwidarasto Susanto, Muhammad Rifqi Priyo Tjitrahardja, Eduardus