Analisis Sentimen Publik pada Media Sosial X terhadap Program Magang Pemerintah Menggunakan Algoritma Naïve Bayes - Cesia Trisani Saragih Garingging; Sasmita Lumban Gaol; Sardo Sipayung (Jurnal Teknik Informatika dan Teknologi Informasi, 2026)
Internship programs organized by the government are a strategic effort to enhance human resource competencies while providing employment opportunities for the younger generation. This study aims to examine public perceptions of these programs through sentiment classification on the social media platform X (formerly Twitter). The method employed is Naive Bayes, a probabilistic classification algorithm that is effective for text analysis. Data were collected from tweets containing keywords related to government internship programs within a specific time frame. After the data underwent cleaning and preprocessing, the model was trained and tested to classify sentiments into positive and negative categories. The classification results indicate that positive sentiment dominates with 52.3%, while negative sentiment accounts for 47.7%. This relatively narrow margin suggests that although the majority of users responded positively, a considerable portion expressed criticism or dissatisfaction with the programs. These findings serve as valuable input for the government to improve the quality and implementation of internship initiatives to better align with public expectations. Furthermore, this study demonstrates that the Naive Bayes method is capable of automatically identifying public sentiment using data from social media platform X. The conclusions drawn can be used as a reference for policymakers to enhance communication and execution of government internship programs in the future. It is also recommended that future research expand the data sources by including various other social media platforms to gain a more comprehensive view of public sentiment. This approach would enrich the analysis and contribute to more data-driven and responsive policy development.