PUTRA, VARADA DWI CAHYA (2022) NEW INDONESIA CAPITAL SENTIMENT ANALYSIS ON TWITTER USING SVM AND APPROACH WITH LEXICON METHOD. Other thesis, Universitas Katholik Soegijapranata Semarang.
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Abstract
Social media is growing fast on the internet. One of the most popular social media is Twitter. Many topics are discussed on Twitter such as economic, politic, social, culture, and law. One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However, there is controversy from supporters and opponents. They have different views. This issue leads to a phenomenon of debate on Twitter that actually shows a collective concern about the public discourse. Sentiment analysis is a process of extracting, understanding and processing unstructured data to get sentiment information which is found in an opinion sentence. Application of sentiment analysis using machine learning methods shows that there are several method that are often used in In this study, the Support Vector Machine (SVM) method is proposed to be applied to classified sentiment tweets on the topic of Indonesia new capital on social media twitter. The classification technique is carried out into 3 classes, namely positive and negative, and neutral. Before the classification process the data is labeled by lexicon method approach to help increase accuracy. This research also use K-Fold Cross Validation for Evaluation and Validation the classification model. Based on testing on the sentiment of Indonesia new capital city from social media twitter from 3000 tweets (1674 positive, 1005, and 321 neutral) using SVM with lexicon method approach obtained accuracy 86%. After evaluation dan validation using K-Fold Cross Validation the accuracy increase from 87% to 88,23%.
| Item Type: | Thesis (Other) |
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| Subjects: | 000 Computer Science, Information and General Works > 005 Computer programming, programs & data |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | mr AM. Pudja Adjie Sudoso |
| Date Deposited: | 26 Oct 2022 09:16 |
| Last Modified: | 26 Oct 2022 09:16 |
| URI: | http://repository.unika.ac.id/id/eprint/30014 |
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