Public Sentiment Analysis on the Issuance of Panda Bonds as an Effort for Rupiah Stability using SVM Algorithm on Youtube Social Media

Junjung Rahmat Santosa; Rangga Apriwijaya; Ilham Ardiasyah; Rangga Apriansyah; Destiarini

doi:10.59934/jaiea.v5i3.2350

Authors

Junjung Rahmat Santosa Universitas Baturaja
Rangga Apriwijaya
Ilham Ardiasyah Universitas Baturaja
Rangga Apriansyah Universitas Baturaja
Destiarini Universitas Baturaja

DOI:

https://doi.org/10.59934/jaiea.v5i3.2350

Keywords:

Lexicon-based, Panda Bonds, Rupiah Stability, , Sentiment Analysis, Support Vector Machine, YouTube

Abstract

The stability of the Rupiah exchange rate is a crucial indicator of Indonesia's economic health, one of which is pursued through the issuance of Panda Bonds. However, this policy has triggered dynamic discourse on social media, particularly YouTube. This study aims to map public perception and test the performance of the Support Vector Machine (SVM) algorithm in classifying sentiments related to this issue. The research methodology includes scraping YouTube comment data, text preprocessing, automated labeling using the Lexicon-based method, and classification using SVM with a Linear kernel. From a total of 659 collected data, the results show that public sentiment is dominated by positive responses at 51.9%, followed by neutral sentiment at 29.0%, and negative sentiment at 19.1%. While public concerns focus on the debt burden and foreign currency dependence, there is overall support for economic stability efforts. The model evaluation demonstrates excellent performance, achieving an accuracy rate of 87.86%, precision of 88.79%, and an F1-score of 87.96%. This proves that a hybrid approach between Lexicon-based and SVM is effective in analyzing complex public opinions within the economic domain on social media.

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