Analisis Sentimen Publik Pada Media Sosial X Terhadap Pembentukan Danantara Menggunakan Metode Support Vector Machine 69 35
DOI:
https://doi.org/10.26623/transformatika.v24i1.14327Keywords:
Analisis Sentimen, Danantara, TF-IDF, Lexicon InSet, Support Vector MachineAbstract
The establishment of Danantara in Indonesia has elicited various public responses, making sentiment analysis necessary to understand the public perception of this policy. This study aimed to analyze public sentiment regarding the formation of Danantara on the X platform into positive, neutral, and negative sentiment classes. The novelty of this research lies in the comparison of the performance of four Support Vector Machine kernels, namely Linear, Polynomial, Radial Basis Function, and Sigmoid, as well as the evaluation of sentiment before and after the official launch of Danantara. Data processing involved text preprocessing, sentiment labeling based on the InSet Lexicon, TF-IDF weighting, and classification using the SVM method. Testing was conducted using data splits of 70:30, 80:20, and 90:10 ratios. The results show that the 90:10 ratio provided the best performance, with polynomial achieving the highest accuracy of 91.66%. Additionally, public sentiment was dominated by the negative class at 65.9% and 60.0% before and after the official launch of Danantara, respectively. These results indicate that SVM has proven effective for sentiment classification and can be used as a tool for policy evaluation and public communication strategy.
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