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Fitra Kurnia
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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Implementation of fuzzy analytical hierarchy process (FAHP) and TOPSIS for scholarship decision support system for the underprivable Fitra Kurnia; M Khairul Amri
Science, Technology, and Communication Journal Vol. 6 No. 2 (2026): SINTECHCOM Journal (February 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i2.361

Abstract

Scholarship is part of assistance given by organization, company, institution or person. One of the existing scholarships at SMKN 2 Bengkalis is underprivileged scholarship given to students with middle to lower economic levels. This research does for helping underprivileged scholarship selectors in SMKN 2 Bengkalis to choose who are recommended eligible students as recipients of underprivileged scholarships with set established criteria before. Fuzzy analytical hierarchy process method implemented in this research with 7. With several criteria having each 4 – 5 values. And this research uses 239 active students data in SMKN 2 Bengkalis in 2021 as a decision alternative. Testing is done with 2 methods that are black box which system has worked in accordance with expected and UAT by 79% of both respondents. After obtaining the preference value, the final results of the ranking are obtained, the order of the smallest value of the student who is most. Entitled to receive scholarship assistance, namely students 1, 5, and 10.
An IndoBERT-based framework for emotion classification in Indonesian song lyrics Agustar Alfonso; Fitri Insani; Okfalisa Okfalisa; Muhammad Fikry; Fitra Kurnia; Sri Wahyuni
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.v6i3.372

Abstract

Emotion classification in song lyrics represented a significant research area within natural language processing, yet studies targeting Indonesian-language lyrics remained scarce due to the limited availability of labeled datasets and the absence of domain-specific models. This study developed and evaluated an emotion classification model for Indonesian song lyrics using fine-tuned IndoBERT-base-p2, a transformer-based language model pre-trained on a large Indonesian corpus. A dataset of 1,025 labeled lyric entries was compiled from Kaggle, Genius, and KapanLagi, covering four emotion categories: joy, sadness, fear, and anger. Preprocessing encompassed duplicate removal, case folding, structural marker removal, and non-alphabetic character cleaning. Nine fine-tuning experiments were conducted by systematically varying learning rate and dropout rate, with early stopping applied based on validation loss. The optimal configuration employed a learning rate of 3 × 10-5 and a dropout rate of 0.1, achieving 75.73% accuracy and 75.85% macro-averaged F1-score on the held-out test set. Joy and anger were classified most reliably, attaining F1-scores of 82.76% and 76.47% respectively, while sadness presented the greatest challenge, exhibiting the lowest precision of 64.10% alongside a recall of 80.65%, indicating a systematic tendency of the model to over-predict this class. These findings demonstrated that IndoBERT-base-p2, when fine-tuned with appropriate hyperparameter configuration, served as an effective approach for domain-specific emotion classification in Indonesian song lyrics.
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.
Co-Authors Agustar Alfonso Febi Yanto Fitri Insani M Khairul Amri Muhammad Fikry Okfalisa Okfalisa Rahmat Zuhri Hafidz Sri Wahyuni Suwanto Sanjaya Yelfi Vitriani