Seleksi Fitur Alternative Accuracy2 pada Analisis Sentimen Mengenai Kebijakan Pembatasan Sosial Berskala Besar dengan K-Nearest Neighbor - Restu Amara; Yuita Arum Sari; Putra Pandu Adikara (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Pembatasan Sosial Berskala Besar or PSBB is one of Indonesian Goverment new policies to surpress the spread of COVID-19 pandemic. This policy generates lots of public opinion about pros and cons, and it became the most discussed topic in social media such as Twitter. From this public opinion, we can get the information about the act of PSBB wich can be classify as it either positive opinion or negative opinion. Sentiment analysis is used to extract information from data text, to get to know whats the point behind every opinion. Excessive data size has become the main problem about text classification, there's a step called feature selection, this step is used to eliminate the unnecessery words in data. In this research, we aim to know the effect of Alternative Accuracy2 feature selection that used with classification method like K-Nearest Neighbor (KNN) on classification result. We used data text with total about 300 public opinion and used K-Fold K-Fold Cross Validation as validation process. The average evaluation results of 5-fold for the use of the Allternative Accuracy2 feature selection, which is equal to 0,7367 for the accuracy value with 0,7667 for precision, 0,7277 for recall, and the f-measure is 0,7453 with a k value in KNN k = 47, while K-Nearest Neighbor without using feature selection resulted in 0,7167 for accuracy, 0,7467 for precision, 0,7049 for recall, and 0,7249 for f-measure. Based on these results, it can be concluded that the use of Alternative Accuracy2 feature selection can increase the evaluation value because the resulting features can clarify the characteristics of each document.