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All Journal Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika
Sabrina, Siti Sarah
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Computer Science & IT

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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.
VISUALISASI DATA PENYEBAB KEMATIAN DI INDONESIA RENTANG TAHUN 2000-2022 DENGAN POWER BI Sabrina, Siti Sarah
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 2 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i2.4071

Abstract

Kematian, sebagai peristiwa tak terhindarkan bagi semua makhluk hidup, termasuk manusia, terjadi setiap tahun dan menyebabkan jutaan kematian di seluruh dunia, dengan tren peningkatan jumlah kematian yang semakin mencolok di Indonesia dari tahun ke tahun. Penelitian ini memiliki tujuan menganalisis penyebab kematian, mencakup bencana alam, non bencana alam, dan bencana sosial, dari tahun 2000 hingga 2022, menggunakan Power BI sebagai alat utama analisis. Dengan dataset dari www.kaggle.com, penelitian ini menjelajahi tren, pola, dan hubungan antara jenis penyebab kematian dan jumlah kematian, serta memberikan visualisasi dinamis melalui Power BI. Hasilnya adalah penyajian data dalam bentuk dashboard, mencakup total kematian berdasarkan kategori, jenis kematian, dan total kematian per tahun. Kesimpulan dari penelitian menunjukkan adanya 777.076 korban meninggal karena bencana non alam atau penyakit, 185.290 korban meninggal akibat bencana alam, dan 261 korban meninggal akibat bencana sosial. Sehingga total kematian di Indonesia dalam periode 2000-2022 mencapai 962.627. Penelitian ini diharapkan memberikan pemahaman mendalam mengenai dinamika kematian selama dua dekade terakhir, menjadi dasar untuk kebijakan kesehatan, serta pemahaman lebih lanjut mengenai hubungan antara penyebab kematian dan total kematian di Indonesia.
Co-Authors Fikri Fahru Roji Shiddieq , Diqy Fakhrun