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Febi Yanto
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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A web-based decision support system for e-wallet selection using AHP-TOPSIS with integrated economic and technical values Rahmat Zuhri Hafidz; Suwanto Sanjaya; Yelfi Vitriani; Fitra Kurnia; Febi Yanto
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.394

Abstract

The rapid growth of financial technology in Indonesia has introduced a diverse range of digital wallet (e-wallet) options including OVO, GoPay, DANA, and ShopeePay. While this abundance of choice benefits consumers, it also creates decision-making challenges, particularly since most prior studies have neglected the balanced integration of economic and technical criteria in e-wallet evaluation. This study addresses that gap by developing a web-based decision support system for e-wallet selection using the AHP-TOPSIS method with integrated economic and technical criteria. Nine criteria were applied, comprising three economic criteria (cost structure, financial incentives, additional fees) and six technical criteria (data security, ease of use, merchant coverage, transaction speed, customer service, additional features). Primary data were collected from 85 active e-wallet users through a Likert scale 1 – 5 questionnaire. Results indicate that OVO ranked first with a TOPSIS preference score of 0.5835, followed by DANA (0.5802), GoPay (0.4654), and ShopeePay (0.3994). The developed system demonstrated the ability to produce objective, adaptive, and user-friendly recommendations to empower users with an interactive, data-driven decision support tool.
Application of ensemble methods on transformer sequence classification of BERT base uncased and RoBERTa-base models for hate speech detection Reza Mahendra Sardi; Surya Agustian; Rahmad Abdillah; Febi Yanto
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.397

Abstract

The rapid growth of social media platforms has brought a significant impact on the volume of digital interactions, which unfortunately is accompanied by a dramatic increase in the spread of hate speech and offensive language. Manual identification of negative content is highly inefficient and unscalable, thereby necessitating the development of state-of-the-art natural language processing (NLP) based automated detection systems. This study proposes the application of ensemble methods on Transformer architectures by combining two leading pre-trained language models, namely BERT (bidirectional encoder representations from transformers) base uncased and RoBERTa (robustly optimized BERT approach) base. The main focus of this research is to evaluate the performance of combining both models through a weighted average ensemble approach based on raw prediction probabilities (logits) with an even weighting ratio (50:50). Experiments were conducted using the public hate speech and offensive content identification (HASOC) 2021 dataset, covering two main scenarios: binary classification to distinguish NOT (normal) and HOF (hate/offensive) classes, and multi-class classification to categorize samples into HATE, OFFN (offensive), PRFN (profane), and NONE classes. To address the inherent challenge of significant class imbalance in the training data, this study implemented a custom class weighting function in the trainer module during the fine-tuning process. Empirical evaluation results demonstrate that the integration of the ensemble method effectively optimizes linguistic representation, suppresses prediction bias in minority classes, and improves performance stability. The ensemble model successfully achieved a macro F1-score of 0.8186 with 83.37% accuracy in the binary scenario, and a macro F1 score of 0.6570 with 68.93% accuracy in multi-class classification. This superior performance surpasses the capabilities of each baseline model individually, making it a robust hybrid architecture in tackling the variation of foul language in contemporary social media ecosystems.
Co-Authors Fitra Kurnia Rahmad Abdillah Rahmat Zuhri Hafidz Reza Mahendra Sardi Surya Agustian Suwanto Sanjaya Yelfi Vitriani