Analisis Sentimen Masyarakat Terhadap Kebijakan IKN Pada Periode Jokowi dan Prabowo Menggunakan Algoritma NBC, SVM, dan K-NN
-
Nur Shabrina Nasution
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Inggih Permana Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Febi Nur Salisah Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- M Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Megawati Megawati Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
The relocation of the National Capital City (IKN) from Jakarta to East Kalimantan has generated a variety of responses from the Indonesian people recorded through social media, especially platform X. This study aims to analyze and compare public sentiment towards the IKN policy in two periods of government, namely President Joko Widodo and President Prabowo Subianto. This study aims to analyze and compare public sentiment towards the policy of the National Capital City during two periods of government, namely President Joko Widodo and President Prabowo Subianto, using a machine learning approach. The three algorithms used in sentiment classification are Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The research process includes data crawling (600 data each per period), text preprocessing (cleaning, tokenizing, filtering, stemming), data labeling using Lexicon-Based approach with InSet dictionary, and weighting using TF-IDF method. The results of the analysis show that in the Jokowi period, public sentiment tends to be more balanced, with the dominance of negative sentiment (35.9%), followed by positive sentiment (33.4%) and neutral (30.7%). Whereas in the Prabowo period, negative sentiment increased to 40.3%, while positive decreased to 26.3%. Based on the model accuracy evaluation, in the Jokowi period, the NBC algorithm showed the best performance with an accuracy of 73%, while in the Prabowo period, the SVM algorithm excelled with the highest accuracy reaching 81%. These findings provide a dynamic picture of public perception of IKN policies under two different governments.
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