KLASIFIKASI EMOSI PUBLIK TERHADAP LARANGAN PENGGUNAAN OBAT SIRUP MENGGUNAKAN ALGORITMA NAIVE BAYES

Abstract

In October 2022 BPOM withdrew the circulation of syrup drugs and banned the public from using syrup drugs due to the increasing cases of kidney failure in children with a mortality rate of up to 59%. The phenomenon of mass death has caused psychological trauma that threatens. The purpose of this study was to find out how public comments on Twitter and Instagram social media regarding the prohibition of using syrup drugs by classifying emotions using the Naive Bayes algorithm with Particle Swarm Optimization (PSO) feature selection. The methodology used is the AI Project Cycle which consists of problem scoping, data acquisition, data exploration, modeling and evaluation. The amount of data in this study was 1213 comments with 381 surprised comments, 318 angry comments, 277 sad comments, 137 scared comments, and 100 happy comments. Happy and afraid comments have fewer comments than other emotions, so the imbalance dataset will be handled using SMOTE. The results of this study are to compare the classification results of the application of PSO and SMOTE to the Naive Bayes algorithm to determine the best model. Based on the classification results, the Naive Bayes, PSO, and SMOTE models produced the highest accuracy values of 76.48%, with a recall value of 76.47%, a precision of 76.20%, and a fmeasure of 75.62%.