Sentiment Analysis of School Zoning System On Youtube Social Media Using The K-Nearest Neighbor With Levenshtein Distance Algorithm

Abstract

The role of the education effect on the progress of each nation and State. The Ministry of education and culture (Kemendikbud) published a regulation of the Minister of education and culture (Permendikbud) No. 14 Year 2018 on the acceptance of New Learners (PPDB). Replace the previous rules, one of which is using a system of zoning for equalization learners. New Learner Acceptance Policy (PPDB) reaps the pros and cons of the zoning system. This research was conducted on the analysis of the sentiments of the public comments against the policy. The data were analyzed taken from youtube as much as 160 comments. This research uses the K-Nearest Neighbor algorithm to the value k = 3, k = 5, k = 7, k = 9 in classifying the test data and the Levenshtein Distance to fix incorrect type. This research used a combination of K-N Nearest Neighbor algorithm and Levenshtein Distance that aims to find out the level of accuracy from a combination of the algorithm. Testing was done using the confusion matrix on test data. Conclusions from testing the combination algorithm for K-Nearest Neighbor and Levenshtein Distance can increase the accuracy of classification. Accuracy results from K-Nearest Neighbor algorithm, namely in the amount of 50% to the value k = 3 and k = 7 whereas the results accuracy the combination of K-Nearest Neighbor and Levenshtein Distance of 65.625% with a value of k = 3.