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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Golden Ratio of Mapping Idea and Literature Format
Shiddieq , Diqy Fakhrun
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences

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Comparative Analysis of SVM and BERT for Sentiment and Sarcasm Detection in the Boycott of Israeli Products on Platform X Sabrina, Siti Sarah; Shiddieq , Diqy Fakhrun; Roji, Fikri Fahru
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14723

Abstract

The Israel-Palestine conflict has triggered a global consumer movement, including a widespread boycott of Israeli-affiliated products in Indonesia. As this campaign gains momentum on digital platforms like X (formerly Twitter), understanding public sentiment becomes crucial—not only for gauging public opinion but also for anticipating potential socio-economic impacts. This study evaluates the effectiveness of two sentiment analysis models—Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT)—in classifying sentiment and detecting sarcasm related to the boycott campaign. A total of 5,637 Indonesian-language tweets were manually labeled into positive, neutral, and negative categories, with sarcasm detection performed using a fine-tuned IndoBERT, model which classified tweets into two categories: sarcastic and non-sarcastic. The models were assessed using accuracy, precision, recall, F1-score, and computational efficiency. Results show that BERT outperforms SVM in both sentiment classification (accuracy: 69.26% vs. 64.58%; F1-score: 69.47% vs. 62.40%) and sarcasm detection (accuracy: 92.20% vs. 86.15%; F1-score: 92.38% vs. 85.27%). However, BERT requires significantly longer processing times 194.76 seconds for sentiment classification and 191.92 seconds for sarcasm detection, while SVM required only 18.81 seconds and 10.99 seconds. These findings highlight a trade-off between contextual comprehension and real-time efficiency. Future research may explore ensemble methods or threshold-tuning to optimize this balance. The practical implications of this research lie in its application for real-time public discourse monitoring and data-driven policy development. By improving the detection of nuanced expressions such as sarcasm, this study contributes to more accurate sentiment interpretation in polarized digital environments.
Consumer Preferences in Choosing Payment Methods on the TikTok Shop and Shopee Platforms Nurlaila, Fitria; Kusmiati , Eti; Shiddieq , Diqy Fakhrun
Golden Ratio of Mapping Idea and Literature Format Vol. 6 No. 1 (2026): July - January
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grmilf.v6i1.1769

Abstract

The development of digital technology has changed consumer behavior in choosing payment methods on e-commerce platforms. This study involved 100 respondents who have used the PayLater and Cash on Delivery (COD) payment methods at TikTok Shop and Shopee, to compare consumer preferences for the two payment methods using the Analytical Hierarchy Process (AHP) method. The four main criteria analyzed include convenience, security, ease, and trust, with sub-criteria such as customer protection, transaction security, payment flexibility, and service reliability. The results show that consumers prefer the COD payment method at Shopee compared to other methods on both platforms. This is due to the better security, trust, and ease of transaction factors at Shopee. The urgency of this research stems from the increasing competition among e-commerce platforms and the need to understand consumer preferences when choosing payment methods, thereby providing strategic insights for platform managers to optimize their services. As such, Shopee is still the more dominant platform in meeting consumer needs regarding cash-on-delivery payment methods.
Co-Authors Fikri Fahru Roji Kusmiati , Eti Nurlaila, Fitria Sabrina, Siti Sarah