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The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.

PeduliLindungi is an application from the Government of Indonesia that was made in response to the COVID-19 pandemic. Since its initial release in 2020, this application has received many updates with the goal of improving its overall performance. One of the basics of updating applications is to process the reviews given by users at the Google Play Store using sentiment analysis. The methods used this time are Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). The sample data used were 300 reviews with positive feedback and 300 reviews with negative feedback, for a total of 600 user reviews. The results of the NBC algorithm calculations produce an accuracy of 76%, a precision of 76%, a recall of 82%, and an f1-score of 79%. As for the SVM algorithm, it produces an accuracy rate of 80%, a precision of 83%, a recall of 80%, and an f1-score of 81%.

The Halal Product Assurance Agency (BPJPH) is an agency under the auspices of the Ministry of Religion with the task of ensuring the halalness of products in Indonesia. BPJPH has become a public concern after establishing the new halal logo. On February 10, 2022 the new halal logo was ratified by the Head of BPJPH, Muhammad Aqil Irham. This has become a topic of public discussion either directly or through social media, one of which is social media twitter. The number of opinion tweets about the change of the halal logo can be used as a data source to obtain information about public opinion on the change of the halal logo through sentiment analysis. Sentiment analysis can be done by machine learning approach, one of these is the SVM algorithm . In this research, oversampling and undersampling are applied to handle data that has an unbalanced sentiment class. The results showed that the Support Vector Machine (SVM) model using oversampling training data got the highest accuracy, recall, precision, and f1-score, namely 71% accuracy, 67% precision, 61% recall, and 61% f1-score while training using undersampling training data has the lowest performance, namely getting 56% accuracy, 51% precision, 57% recall, and 52% f1-score.

The government that is running at this time is also not spared from public comments on Twitter, especially regarding the increase in subsidized fuel. There are at least 4 impacts felt by the community when subsidized fuel prices increase, namely a decrease in people's purchasing power, an increase in basic prices, an increase in the unemployment rate and an increase in the poverty rate. This study aims to implement the Naïve Bayes Classifier and KNN algorithms in classifying a tweet of an increase in subsidized fuel so that it can be identified as belonging to a class with positive or negative sentiments. The data used in this research are 560 tweets. The data is divided into 2, namely 500 training data from tweet data and 60 test data from tweet data stored in xlsx format. The results of the accuracy with the Naïve Bayes Classifier algorithm is 85% while the KN algorithm is 86.8% so it can be concluded that the KNN method is better than the Naïve Bayes Classifier method in classifying tweets of increases in subsidized fuel. Keywords: Subsidized BBM, Naive Bayes, KNN

The post-reform era became public and government awareness of ethnic Chinese. This is evidenced by President Abdurrahman Wahid's decree to relax ethnic Chinese activities. Because in the Old Order, there was a government policy that was anti-Chinese sentiment. There is even a ban on foreigners to trade in Indonesia, as well as ethnic Chinese who become looting and mob rage, victims of sexual violence and so on. Through this Soe Hok Gie film, it depicts the participation of ethnic Chinese in solving the problem of government authoritarianism and saving Indonesia from the economic crisis. In this studyThis type of research uses library research, with a communication approach that uses media text analysis. The data collection technique in this study uses the semiotic analysis research method of Charles Sanders Peirce. The method uses Sign, Object and Interpretant to explain the content of a film.From the film Soe Hok Gie, this research has found themeaning of Sign, Object and Interpretant of ethnic Chinese political participation in the film Soe Hok Gie based on Charles Sander Peirce's semiotic theory. Finding an analysis of Sign, Object and Interpretant of ethnic Chinese political participation in political developments in Indonesia in the film Soe Hok Gie

The rapid growth of e-commerce in Indonesia has generated a massive and continuous volume of product reviews. This user-generated content is vital for business intelligence, yet its sheer scale makes manual analysis inefficient, subjective, and practically impossible. Automated sentiment analysis is therefore crucial for businesses to efficiently understand customer feedback and market perception. This research addresses this gap by implementing the Naïve Bayes Classifier (NBC) algorithm to automatically classify the sentiment of Indonesian-language e-commerce product reviews. This study utilized a dataset of 2,000 reviews collected from a major e-commerce platform's "Electronics" category. The data underwent critical text preprocessing stages (case folding, tokenizing, stopword removal, and stemming using the Sastrawi library) to handle the complexities of informal Indonesian text. The dataset was split using an 80/20 ratio, resulting in 1,600 training reviews and 400 testing reviews. Model performance was then evaluated using a Confusion Matrix, focusing on the key metrics of Accuracy, Precision, and Recall. The test results showed excellent performance, achieving an Accuracy of 90.00%, Precision of 91.93%, and Recall of 95.00%. These results demonstrate that the Naïve Bayes algorithm, when supported by robust preprocessing, is a highly effective, reliable, and computationally efficient method for this task, providing a valuable tool for e-commerce stakeholders.