Analisis Sentimen Jogja Darurat Sampah di Twitter menggunakan Ekstraksi Fitur Model Word2Vec dan Convolutional Neural Network - Yusanto, Yoga; Akbar, Mutaqin (TIN: Terapan Informatika Nusantara, 2024)
Due to a waste emergency, the Special Region of Yogyakarta has garnered public attention and sparked discussions. Numerous community groups express their opinions through various social media platforms, especially Twitter. It's undeniable that Twitter is currently one of the places for freely expressing opinions. Therefore, sentiment analysis plays a crucial role in efforts to categorize public opinions on something trending or viral into three categories: positive, negative, and neutral. In this study, the dataset was obtained using scraping techniques and the tweetscraper tool from the APIFY actor web.harvester/easy-twitter-search-scraper. The method employed in this analysis is the Convolutional Neural Network (CNN) classification method using Word2Vec model extraction. The study involves 505 tweets in Bahasa Indonesia with the hashtags #JogjaDaruratSampah (#JogjaDaruratSampah) and #TPSTPiyungan as data. Out of these, 381 tweets are utilized as training data, and the remaining 124 tweets are used as test data. The highest accuracy in testing the training data was achieved in the 19th epoch with a 90% accuracy rate. It can be concluded from the testing process that this study can identify positive, negative, and neutral sentiments with an accuracy of 53%. The sentiment analysis results indicate a significant amount of negative tweets, accounting for 49.7% of the total 505 tweets.