WIJAYA, ALDI TRI (2024) SENTIMENT ANALYSIS OF INFLATION IN INDONESIA USING THE NAÏVE BAYES CLASSIFIER METHOD. ["eprint_fieldopt_thesis_type_tugasakhir" not defined] thesis, Informatics.
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Abstract
ABSTRACT The aim of this research is to analyze sentiment towards inflation in Indonesia using the naïve Bayes method. Inflation that occurs throughout the world, including Indonesia, causes problems for society and the country, there is instability in the country's economy and increasing unemployment due to the large number of employee reductions, the prices of goods and clothing have increased significantly. So, from this problem, public opinions arise on social networks, especially Twitter, regarding the inflation problem. Therefore, a system was created to carry out sentiment analysis of the inflation problem in Indonesia. The method used in this research is the naïve Bayes classifier with the multinomialnb model as the method for carrying out classification. The system process requires tweet data with a total of 1725 tweets resulting from the Twitter data crawling process using the help of the Python library, namely Tweepy, using the keyword Indonesian inflation. The classification results from the Indonesian inflation search key contain several classes, namely positive, negative and neutral. The research process was tested using several data sharing scenarios, namely 90:10, 80:20, 70:30, for the best level of accuracy, it was produced using a scenario of 90% train data and 10% test data, getting results of 75.5% accuracy, 75% precision, f1 -score 75% and recall 74%. Keywords: Inflation, Classification, Naïve Bayes Classifier, MultinomialNb, Sentiment.
| Item Type: | Thesis (["eprint_fieldopt_thesis_type_tugasakhir" not defined]) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 29 May 2024 03:27 |
| Last Modified: | 29 May 2024 03:27 |
| URI: | http://eprints.uty.ac.id/id/eprint/15677 |
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