Sentiment Analysis for Zoning System Admission Policy Using Support Vector Machine and Naive Bayes Methods

Reynaldy Aries Ariyanto, N. Chamidah

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

Indonesia have low quality of education. According Trend International Mathematics and Science Study, Indonesian student's mathematical literacy is ranked 36 from 49 countries. For Science literacy, Indonesia is ranked 35 from 49 countries. To increase quality of education in Indonesia, Indonesia's government make a new policy for new student admission called zoning system. Zoning system is a new student admission according distance from house to school. Zoning system is new policy in Indonesia and there are still many pros and cons of the zoning system. Sentiment analysis is used to know whether Indonesia's people agree or disagree about zoning system policy. In this study, we use supervised statistical learning methods that are Support Vector Machine (SVM) and Naïve Bayes for sentiment analysis of zoning system admission policy. The results show that Indonesia's people tend to disagree with the zoning system admission policy because negative opinion is greater than positive opinion. Furthermore, accuracy rates of SVM and Naïve Bayes are 92.93% and 79.86% respectively. So, SVM is better than Naïve Bayes for sentiment analysis of zoning system admission policy.

Original languageEnglish
Article number012058
JournalJournal of Physics: Conference Series
Volume1776
Issue number1
DOIs
Publication statusPublished - 3 Feb 2021
Event5th National Conference on Mathematics Research and Its Learning, KNPMP 2020 - Surakarta, Indonesia
Duration: 5 Aug 2020 → …

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