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Sentiment analysis of comments from visitors to tourist attractions and the public on tourist attractions in Sukabumi Regency which is one of the areas with various categories of tourist objects and is a sector of economic income for the surrounding community or for related parties such as the government and managers, in sentiment analysis research This includes using the Nave Bayes classification algorithm to examine the sentiment of tourist visitors and the performance of the classification model used. The data used in this research was taken from the website from Tripadvisor and Google Maps using a crawling technique, which then processed the data by a pre-processing process and then applied a classification to the data and got a sentiment visualization by processing word frequency on tourist visitor sentiment data. The results of the accuracy of the model used were re-tested with the k-fold cross validation method and the results of sentiment visualization got the frequency of words that most often appear on negative sentiment labels are garbage, beaches, lacking, places, roads, parking, dirty, entering, caring, clean , expensive, pay, manage, good and water.

The Free Nutritious Meals (MBG) program launched by the Indonesian government aims to address the problem of malnutrition in children and students. However, the acceptance of this program in the community still requires in-depth evaluation because there are many negative sentiments that dominate on social media. This study aims to analyze the sentiment of the Indonesian community regarding the Free Nutritious Meals program on social media X (Twitter) using the Bidirectional Gated Recurrent Unit (BiGRU) model. Of the 1,405 tweet data obtained, 57% were negative opinions and 43% were positive opinions. The evaluation results show that the BiGRU model with FastText support to handle potential overfitting, is able to classify sentiment effectively, with an accuracy of 80%. Sentiment analysis shows that the majority of public responses to the Free Nutritious Meals (MBG) program tend to be negative, with 798 negative tweets and 607 positive. This reflects public dissatisfaction with the implementation of the program and highlights the need for evaluation and improvements so that the benefits can be more widely felt by the community.

Organisasi Kesehatan Dunia (WHO) menyatakan COVID-19 pandemi di awal 2020, dan itu tiba di Indonesia pada Maret 2020. Tidak semua orang di dunia, termasuk Indonesia, memandang pandemi dalam cara yang sama. Menganalisis postingan Facebook Covid-19 adalah cara yang baik untuk mengukur opini publik tentang pandemi. Tujuan dari penelitian ini adalah untuk mengkaji sentimen publik di Indonesia terkait wabah penyakit Covid19 dengan menganalisis reaksi terhadap Postingan Facebook, terutama yang berasal dari yang terverifikasi rekening pemerintah, yang akan dibandingkan dengan akun dari portal berita. Dengan bantuan CrowdTangle, 1211 postingan Facebook yang berisi kata "wabah covid-19" dari 10 pemerintah pejabat dan 10 portal berita dikumpulkan antara 21 Januari 2020, dan 21 Januari 2021, untuk ini belajar. Boxplot dan visualisasi cloud kata, sebagai serta uji statistik, digunakan untuk mengkonfirmasi sentimen yang berbeda dalam posting oleh berbagai jenis akun, serta reaksi publik yang berbeda. Postingan dari pejabat pemerintah, di sisi lain, cenderung menjadi lebih positif, sedangkan posting dari portal berita cenderung lebih negatif. Selanjutnya, posting oleh pejabat pemerintah cenderung menerima lebih positif reaksi, terlepas dari sentimen mereka, dibandingkan dengan posting oleh portal berita, yang menerima berbagai reaksi publik tergantung pada sentimen.