Sentiment analysis of computer-based national examination policy with data mining approach

Sentiment analysis of computer-based national examination policy with data mining approach

• By Iqbal Dzulfiqar Iskandar,a) Agung Baitul Hikmah,b) Deddy Supriadi,c) Taufik Wibisono,d) and Yani Sri Mulyani);
• Jurnal Internasional
• 167

Abstract. The Coronavirus pandemic has resulted in the elimination of the implementation of the Computer-Based National
Examination throughout Indonesia, from this case it has presented various kinds of sentimental conversations among the public,
especially on the Twitter social media network. From these problems. So research on sentiment analysis of computer-based
national exam elimination policies was conducted. The method used in this research uses the Naïve Bayes algorithm. The purpose
of this research is to identify public sentiment through the Twitter social network regarding the elimination of the Computer-Based
National Examination and as a development of research that has been done previously to discuss the analysis of public sentiment
regarding the application of the Computer-Based National Examination. as well as knowing the accuracy value of the results of
the validation of the Naïve Bayes algorithm on the effectiveness of data processing using K-fold Cross-Validation. The results of
the research based on the classification data showed that the Negative (N) class sentiment group towards the policy of eliminating
computer-based national exams was greater with a value of 0.780, compared to the Sentiment Positive (P) class group which had
a distribution value of 0.196, while the lowest distribution value is in the Neutral Sentiment Class (NEU) group with a value of
0.024. So that it can be seen that public sentiment towards the policy of eliminating computer-based national exams leads to
negative sentiment (N). After doing the data, it shows that the average accuracy value of the Naïve Bayes Algorithm, in this case,
is 78.023% with an average Classification Error of 21.97%. Based on these data. Then it can be categorized as a fair classification.
The impact of this research is to provide knowledgeable information to see the patterns of language style in the community that
result in an assessment of an issue, place, and event, including an assessment of people’s perspectives on government policies

• Katakunci
• URL http://
• Penerbit AIP Publishing,

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