Sentiment Analysis of Handling "Klitih" in Yogyakarta Using Naïve Bayes - Windha Mega P Dhuhita; Ilham Ferry Pratama; Bayu Setiaji (Indonesian Journal of Data Science, IoT, Machine Learning and Informatics, 2024)
Activities that lead to crimes called "klitih" often occur and disturb the community. The community's response to the handling carried out by the regional government also varied. The public expressed this response using various types of social media, one of which was Twitter. This research analyzes sentiment or responses given by the public by utilizing the social media Twitter to collect data. Data in the form of tweets that have been taken will go through text processing. After that, the text will be weighted using two methods as a comparison, namely tf-idf and count vector. Then the data will be divided into training data and test data to proceed to the classification stage. Classification is carried out using the Naïve Bayes algorithm. To evaluate the results of Naïve Bayes classification, researchers used the Confusion Matrix, by comparing weighting methods and dividing the training data and test data into several different ratios, to find out the scenario that produces the best level of accuracy. The sentiment obtained was dominated by negative sentiment at 75.8%, while positive sentiment was 24.2%. By using existing data, it was found that weighting with count vector had an accuracy rate of 82%. Meanwhile, weighting using TF-IDF obtained an accuracy of 80%.