Clustering Analysis from Universities in Indonesia based on Sentiment Analysis
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 10, Pages 1466-1481
AbstractThere are two kind of source to determine the quality for a good university in Indonesia. First from university cluster which is publish from Ministry of Research, Technology and Higher Education issued a clustering list of Indonesian universities, the second source of data from social media, such as Twitter. In this research we use Text Mining and Data Mining Methodology to build a sentiment analysis from 50100 Tweet to assess 501 university using Python and special library in Python for Natural Language Processing a sentiment analysis , which is join the university clustering from Ministry of Research, Technology and Higher Education, so it will produce the positive, neutral and negative sentiment for each 501 universities in 2020. The next process by using R STUDIO, the process classification is continued by using K-Means, the process can be devided into two step , step 1 it will process 501 dataset university and it will build 5 cluster and secondly the similarities between Netizen cluster and cluster from Ministry of Research, Technology and Higher Education is 37 %, and step 2 after cleansing the 0 value, the result is 169 universites the similarities between Netizen cluster and cluster from Ministry of Research, Technology and Higher Education is 37 % before and after data cleansing was the same. The novelty knowledge or research finding can be derived from Netizen, firstly, the cluster can be derived based on Positive Sentiment,. Secondly, the cluster from Netizen and Cluster from Directorate General of Higher Education, Ministry of Education and Culture of higher education in Indonesia is only match around 37 % with cluster form Directorate General of Higher Education. And after data cleansing from 169 university was only match around 33 %..
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