Putra, Muhammad Meiditya (2024) Analisis Sentimen Masyarakat Indonesia Pengguna Twitter/x Terhadap Pemindahan Ikn 2024 Menggunakan Algoritma Naive Bayes Classifier. Other thesis, Universitas Islam Riau.
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Abstract
The relocation of the Indonesian capital city (IKN) from Jakarta to Nusantara has become a widely discussed national issue among the public, including on social media platform Twitter. This research aims to analyze the sentiment of Indonesian Twitter/X users towards the 2024 IKN relocation using the Naive bayes Classifier algorithm. By doing so, it is expected to understand the patterns of sentiment, attitudes, and public views more systematically and objectively.The research is conducted through methodological stages such as research data collection obtained from Twitter by using the keyword "IKN lang:id". Data Preprocessing: Cleaning and processing the data to remove noise , such as links, punctuation, and unimportant words. Dataset Formation: Creating a dataset that includes positive, negative, and neutral sentiments based on tweet content analysis. Implementation of Naive bayes Classifier Algorithm: Applying the Naive bayes Classifier algorithm to classify sentiments from the formed dataset. Model Evaluation: Evaluating the performance of the sentiment classification model using relevant metrics, such as accuracy, precision, recall , and F1-Score. Result Analysis: Analyzing the sentiment classification results to gain a deeper understanding of public attitudes and views towards the 2024 IKN relocation. By conducting this research, it is expected to contribute to understanding the dynamics of Indonesian public opinion on the Twitter/X platform regarding the 2024 IKN relocation issue, as well as enhance the understanding of the use of the Naive bayes Classifier algorithm in effectively analyzing sentiment.
| Item Type: | Thesis (Other) |
|---|---|
| Contributors: | Contribution Contributors NIDN/NIDK Sponsor Efendi, Akmar 1031126801 |
| Uncontrolled Keywords: | Sentiment analysis, IKN, Naive bayes Classifier, Twitter/X. |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | > Teknik Informatika |
| Depositing User: | Yolla Afrina Afrina |
| Date Deposited: | 18 Nov 2025 07:40 |
| Last Modified: | 18 Nov 2025 07:40 |
| URI: | https://repository.uir.ac.id/id/eprint/30608 |
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