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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Fajria Ulumin Nafiah
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Engineering Public Health Social Sciences

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Enhancing MSME Capacity Through the Utilization of QRIS as a Digital Payment System in Dukuh Setro Subdistrict, Surabaya: Peningkatan Kapasitas UMKM Melalui Pemanfaatan QRIS sebagai Sistem Pembayaran Digital di Kelurahan Dukuh Setro Surabaya Fajria Ulumin Nafiah; Aulya Dista Yasah; Nasywa Athaya Muthmainnah Rahman; Agnes Tresia Silalahi
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 9 No. 4 (2025): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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Abstract

The digital transformation in payment systems presents both opportunities and challenges for Micro, Small, and Medium Enterprises (MSMEs). Although QRIS has been introduced as a practical and efficient cashless payment solution, some MSMEs still lack optimal understanding of its utilization. This community service activity aimed to improve the digital literacy of MSMEs in Dukuh Setro Subdistrict, Surabaya, through socialization and technical assistance in using QRIS. The implementation method included planning, information dissemination, socialization with Bank Mandiri, and technical assistance for QRIS registration and activation. A participatory approach was adopted to ensure the activities aligned with the actual needs of business actors. The results showed an increase in participants’ understanding of QRIS benefits and its operational mechanism, with most participants successfully activating their accounts and expressing readiness to implement QRIS in their daily business activities. This activity not only supports MSME digitalization but also strengthens financial literacy and prepares business actors to adapt to technological developments in the digital era.
Indonesian Cyberbullying Detection Using IndoBERTweet-BiGRU Model on Class-Imbalanced X (Twitter) Data Fajria Ulumin Nafiah; Aviolla Terza Damaliana; Kartika Maulida Hindrayani
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13686

Abstract

Cyberbullying on social media platforms, particularly X (formerly Twitter), has become a serious issue that negatively affects users' mental health and well-being. Automatic cyberbullying detection in Indonesian remains challenging due to the widespread use of informal language, slang, abbreviations, and highly imbalanced class distributions. This study proposes a hybrid deep learning model that integrates IndoBERTweet with a Bidirectional Gated Recurrent Unit (BiGRU) to improve cyberbullying detection performance on Indonesian tweets. A dataset of Indonesian tweets was collected from X and annotated using a multi-stage dual large language model (LLM) labeling strategy to reduce the time and effort required for manual annotation while maintaining label consistency. To address class imbalance, this study investigates the effectiveness of Focal Loss and label distribution modification through multiple experimental scenarios. The proposed approach was evaluated using accuracy, precision, recall, and F1-score. The best performance was achieved by combining Focal Loss with a modified four-class label configuration consisting of Rude and Vulgar Words, Sexual Harassment, Body Shaming and Hate Speech, and Non-Cyberbullying. This configuration obtained an accuracy of 0.93, precision of 0.90, recall of 0.90, and F1-score of 0.90. These findings demonstrate that integrating contextual language representations with sequential modeling, supported by an efficient LLM-assisted labeling strategy and class imbalance handling, provides an effective approach for Indonesian cyberbullying detection and offers a practical solution for large-scale social media content moderation.
Co-Authors Agnes Tresia Silalahi Aulya Dista Yasah Aviolla Terza Damaliana Kartika Maulida Hindrayani Nasywa Athaya Muthmainnah Rahman