Analisis Sentimen Komentar Media Sosial Twitter Terhadap Tes CPNS dengan Algoritma Naive Bayes - Nurhidayat, Rifki; Hendrastuty, Nirwana (Forum Kerjasama Pendidikan Tinggi, 2024)
The Calon Pegawai Negeri Sipil (CPNS) is one of the most sought-after careers in Indonesia, with the number of applicants increasing every year. The CPNS selection process attracts public attention and triggers various opinions, both positive and negative, which are widely conveyed through social media such as Twitter. This research aims to analyze public sentiment towards the CPNS selection process using the Naive Bayes algorithm. The data used in this study consists of 5,599 comments on Twitter, with a composition of 5,269 negative sentiment data and 323 positive sentiment data. Tests were conducted using several data sharing ratios, namely 80:20, 70:30, 90:10, and 50:50. The results show that the 70:30 ratio provides the best accuracy, which is 95%. However, data imbalance causes the model to focus more on negative sentiment. To address this issue, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, which successfully improved the model's performance in classifying positive data, with precision and recall reaching 85-98%. After the application of SMOTE, the overall accuracy decreased slightly to 91% at 80:20, 70:30, and 90:10 ratios, but the model became more effective in detecting both sentiments. The results of this study provide insight into the public's views on CPNS selection and can be used by the government to improve the selection process in the future. With this approach, it is expected that government agencies can better understand public perceptions and optimize a more transparent and fair recruitment system.