
Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)
Author(s) -
M. Khairul Anam,
Muhammad Ihza Mahendra,
Wirta Agustin,
Rahmaddeni Rahmaddeni,
Nurjayadi Nurjayadi
Publication year - 2022
Publication title -
intensif
Language(s) - English
Resource type - Journals
ISSN - 2549-6824
DOI - 10.29407/intensif.v6i1.15870
Subject(s) - support vector machine , sentiment analysis , computer science , feature selection , social media , particle swarm optimization , selection (genetic algorithm) , artificial intelligence , public opinion , adaboost , feature (linguistics) , machine learning , social network (sociolinguistics) , genetic algorithm , data mining , political science , law , world wide web , politics , philosophy , linguistics
The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.