## 2 Sentiment Analysis Algorithms
### Machine Learning Approach
The text was preprocessed using techniques such as lemmatization and stop-word removal. The results showed that the AdaBoost model achieved the highest accuracy of 84.5%, demonstrating that boosting models perform better in terms of sentiment classification accuracy. [PERSON] et al. (2019) [11] compared the performance of two machine learning models, multinomial naive Bayes and support vector classifier (SVC) with linear kernels, for sentiment analysis. The dataset consisted of approximately 10 K positive, negative, and neutral. Tweets, which were collected from Twitter. The text was preprocessed using techniques such as stemming, URL removal, and stop-word removal. The results showed that SVC achieved an accuracy of 82.48% and outperformed the multinomial naive Bayes model, which obtained an accuracy of 76.56%. In the study conducted by [PERSON] et al. (2019) [12], the authors proposed the use of a support vector machine (SVM) method for the sentiment analysis of Tweets related to homosexuality in Indonesia. The dataset was preprocessed to remove stop words and undergo lemmatization and stemming and was represented by Term Frequency-Inverse Document Frequency (TF-IDF) features. The results showed that the SVM method achieved an impressive accuracy of 99.5% on the dataset. [PERSON] and [PERSON] (2019) [13] conducted a comparison of three machine learning methods, support vector machine (SVM), naive Bayes, and k-nearest neighbors (KNN), for sentiment analysis. The dataset was a collection of Tweets related to the presidential candidates in Indonesia in 2019 and was divided 80:20 for training and testing. The preprocessing steps included text parsing, tokenization, and text mining. The results showed that naive Bayes achieved the highest accuracy of 75.58%, followed by KNN with 73.34% and SVM with 63.99%. In the study by [PERSON] (2019) [14], the author compared the performance of four machine learning methods, C4.5, naive Bayes, support vector machine (SVM), and random forest, on a dataset collected from Twitter related to Indian Railways. The dataset was divided into positive, negative, and neutral sentiments. The results showed that the highest accuracy was achieved by SVM with 91.5%, followed by C4.5 with 89.5%, random forest with 90.5%, and naive Bayes with 89%. In [PERSON] et al. (2019) [15], a comprehensive study was conducted to evaluate the performance of four machine learning algorithms for sentiment analysis, including decision tree, logistic regression, support vector machine, and neural network algorithms. The sentiment140 dataset was utilized as the sample data for the study, which was preprocessed and transformed into TF-IDF features. The results indicated that the neural network model outperformed the other algorithms, achieving the highest accuracy of 80. [PERSON] et al. (2019) [16] introduced a novel approach to sentiment analysis by combining boosting and bagging methods in an AdaBoost model. The study focused on sentiment analysis of US airline Twitter data and the dataset was preprocessed to remove irrelevant information and then analyzed using data-mining techniques. The authors used a 75-25 split for the data, with 75% of the data utilized for training and 25% for testing. The AdaBoost model showed promising performance, with an F-score of 68%. [PERSON] et al. (2019) [17] applied the ridge classifier method for sentiment analysis on two datasets: IMDb and Sentiment140. The preprocessing stage involved various techniques such as tokenization, case folding, and the removal of URLs, HTML tags, and irrelevant words. The resulting data were then represented using the TF-IDF method. The experiments showed that the ridge classifier achieved an accuracy of 90.54% on the IMDb dataset and 76.84% on the Sentiment140 dataset, demonstrating its effectiveness for sentiment analysis. The study by [PERSON] (2020) [18] aimed at enhancing the accuracy of sentiment analysis for Arabic Tweets.