Comparison of Support Vector Machine (SVM) and Random Forest Algorithms in the Analysis of SOcial Media X User Sentiment Towards the TNI Bill
The rapid advancement of information technology has enabled the public to openly express their views through social media, including on strategic national issues such as the Draft Law on the Indonesian National Armed Forces (RUU TNI). This study aims to map public sentiment toward the RUU TNI and to...
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| Vydané v: | Journal of Applied Informatics and Computing Ročník 9; číslo 5; s. 2854 - 2860 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English Indonesian |
| Vydavateľské údaje: |
Politeknik Negeri Batam
19.10.2025
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| Predmet: | |
| ISSN: | 2548-6861, 2548-6861 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | The rapid advancement of information technology has enabled the public to openly express their views through social media, including on strategic national issues such as the Draft Law on the Indonesian National Armed Forces (RUU TNI). This study aims to map public sentiment toward the RUU TNI and to compare the effectiveness of two popular sentiment analysis algorithms, Support Vector Machine (SVM) and Random Forest (RF). A total of 525 relevant tweets collected between February and May 2025 were analyzed and classified into three sentiment categories: positive, negative, and neutral. The results reveal that neutral opinions dominate at 81.4%, followed by negative sentiments at 11.1% and positive sentiments at 7.4%. The performance comparison shows that SVM achieved an accuracy of 92%, outperforming RF which obtained 91%. These findings highlight that strategic defense issues tend to generate predominantly informative public opinions, while critical voices show an increasing trend as the discourse evolves. The novelty of this study lies in the application of three-class sentiment classification and the comparative evaluation of SVM and RF within the domain of defense policy. This research contributes to the academic discourse by extending sentiment analysis beyond electoral and marketing topics, while also providing practical insights for policymakers in understanding and responding to public aspirations more effectively.
The rapid advancement of information technology has enabled the public to openly express their views through social media, including on strategic national issues such as the Draft Law on the Indonesian National Armed Forces (RUU TNI). This study aims to map public sentiment toward the RUU TNI and to compare the effectiveness of two popular sentiment analysis algorithms, Support Vector Machine (SVM) and Random Forest (RF). A total of 525 relevant tweets collected between February and May 2025 were analyzed and classified into three sentiment categories: positive, negative, and neutral. The results reveal that neutral opinions dominate at 81.4%, followed by negative sentiments at 11.1% and positive sentiments at 7.4%. The performance comparison shows that SVM achieved an accuracy of 92%, outperforming RF which obtained 91%. These findings highlight that strategic defense issues tend to generate predominantly informative public opinions, while critical voices show an increasing trend as the discourse evolves. The novelty of this study lies in the application of three-class sentiment classification and the comparative evaluation of SVM and RF within the domain of defense policy. This research contributes to the academic discourse by extending sentiment analysis beyond electoral and marketing topics, while also providing practical insights for policymakers in understanding and responding to public aspirations more effectively. |
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| ISSN: | 2548-6861 2548-6861 |
| DOI: | 10.30871/jaic.v9i5.10883 |