Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)
Open Access
- 11 February 2022
- journal article
- Published by Universitas Nusantara PGRI Kediri in INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
- Vol. 6 (1), 11-28
- https://doi.org/10.29407/intensif.v6i1.15870
Abstract
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%.Keywords
This publication has 49 references indexed in Scilit:
- SENTIMENT ANALYSIS OF BPJS KESEHATAN SERVICES TO SMK EKLESIA AND BINA INSANI JAILOLO TEACHERSJurnal Terapan Teknologi Informasi, 2018
- Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviewsJournal of Computational Science, 2018
- Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector MachineJournal of Information Systems Engineering and Business Intelligence, 2018
- The Implementation of Speech Recognition using Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machine (SVM) method based on Python to Control Robot ArmIOP Conference Series: Materials Science and Engineering, 2018
- Random Forest and Support Vector Machine based Hybrid Approach to Sentiment AnalysisProcedia Computer Science, 2018
- Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme GenetikaJurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 2017
- PENERAPAN SENTIMENT ANALYSIS PADA HASIL EVALUASI DOSEN DENGAN METODE SUPPORT VECTOR MACHINEJurnal Transformatika, 2017
- Ensemble model for Twitter sentiment analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Clustering Hierarchy Protocol in Wireless Sensor Networks Using an Improved PSO AlgorithmIEEE Access, 2016
- SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification PerformanceTELKOMNIKA (Telecommunication Computing Electronics and Control), 2016