Sentiment Analysis of Kampus Mengajar 2 Toward the Implementation of Merdeka Belajar Kampus Merdeka Using Naïve Bayes and Euclidean Distence Methods

Abdul Rozaq; Yessi Yunitasari; Kelik Sussolaikah; Eka Resty Novieta  Sari

doi:10.25008/ijadis.v3i1.1233

Authors

Abdul Rozaq Department of Informatics Engineering, Universitas PGRI Madiun, Indonesia
Yessi Yunitasari Department of Informatics Engineering, Universitas PGRI Madiun, Indonesia
Kelik Sussolaikah Department of Informatics Engineering, Universitas PGRI Madiun, Indonesia
Eka Resty Novieta Sari Department of Informatics Engineering, Universitas PGRI Madiun, Indonesia

DOI:

https://doi.org/10.25008/ijadis.v3i1.1233

Keywords:

Sentiment Analysis , Naïve Bayes , K-NN , k nearest neighbors, MBKM, merdeka belajar kampus merdeka

Abstract

The Ministry of Education and Culture initiated the Merdeka Belajar Kampus Merdeka (MBKM) program. Several programs in Merdeka Belajar Kampus Merdeka (MBKM) Program include industrial internships, independent projects, student exchanges, community service projects, humanitarian programs, and so on. Kampus Mengajar 2 is one of the programs had been running. The program received various responses from the public, which were expressed on social media. The Supervisor at kampus mengajar 2 was also active in providing various comments on kampus mengajar 2 telegram groups in the form of good, bad, and neutral comments. These comments have the potential to generate a growing sentiment among the general public and academics. Based on these issues, the researcher analyzed the kampus mengajar 2 sentiments toward the implementation of Merdeka Belajar Kampus Merdeka program with the data source being comments on the supervisors' telegram group. The data obtained from the telegram group is classified as good, bad, or neutral using the Naive Bayes method and K-Nearest Neighbors on up to 591 data points. The data is then divided into two parts: training data and testing data. Testing data can account for up to 20 percent of total data, with the remaining 80 percent serving as training data. The accuracy results on sentiment analysis show that the Naive Bayes method outperforms the KNN method, with 99.30 percent for Naive Bayes and 97.20 percent for K-Nearest Neighbors.

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