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


  • 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



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


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.


Download data is not yet available.

Plum Analytics





E. Purike, “Political Communications of The Ministry of Education and Culture about ‘Merdeka Belajar, Kampus Merdeka (Independent Learning, Independent Campus)’ Policy: Effective?,” EduLine J. Educ. Learn. Innov., vol. 1, no. 1, pp. 1–8, 2021, doi: 10.35877/454ri.eduline361.

K. Krishnapatria, “Merdeka Belajar-Kampus Merdeka (MBKM) curriculum in English studies program: Challenges and opportunities,” ELT Focus, vol. 4, no. 1, pp. 12–19, 2021, doi: 10.35706/eltinfc.v4i1.5276.

S. Andari, W. Windasari, A. Chandra Setiawan, and A. Rifqi, “Student Exchange Program of Merdeka Belajar-Kampus Merdeka (MBKM) in Covid-19 Pandemic,” JPP (Jurnal Pendidik. dan Pembelajaran), vol. 28, no. 1, pp. 30–37, 2021, doi: 10.17977/um047v28i12021p030.

A. A. Sihombing, S. Anugrahsari, N. Parlina, and Y. S. Kusumastuti, “Merdeka Belajar in an Online Learning during The Covid-19 Outbreak: Concept and Implementation,” Asian J. Univ. Educ., vol. 17, no. 4, pp. 35–48, 2021, doi: 10.24191/ajue.v17i4.16207.

C. M. Yudhawasthi and L. Christiani, “Challenges of Higher Educational Documentary Institutions in Supporting Merdeka Belajar Kampus Merdeka Program,” Khizanah al-Hikmah J. Ilmu Perpustakaan, Informasi, dan Kearsipan, vol. 9, no. 2, p. 193, 2022, doi: 10.24252/kah.v9cf2.

I. Lhutfi and R. Mardiani, “Merdeka Belajar - Kampus Merdeka Policy: How Does It Affect the Sustainability on Accounting Education in Indonesia?,” Din. Pendidik., vol. 15, no. 2, pp. 243–253, 2020, doi: 10.15294/dp.v15i2.26071.

D. Kodrat, “Industrial Mindset of Education in Merdeka Belajar Kampus Merdeka (MBKM) Policy,” J. Kaji. Perad. Islam, vol. 4, no. 1, pp. 9–14, 2021, doi: 10.47076/jkpis.v4i1.60.

I. H. Batubara et al., “Bibliometric Mapping on the Research ‘Merdeka Belajar’ Using Vosviewer,” J. Pendidik. Progresif, vol. 12, no. 2, pp. 477–486, 2022, doi: 10.23960/jpp.v12.i2.202207.

B. K. Prahani et al., “The Concept of ‘Kampus Merdeka’ in Accordance with Freire’s Critical Pedagogy,” Stud. Philos. Sci. Educ., vol. 1, no. 1, pp. 21–37, 2020, doi: 10.46627/sipose.v1i1.8.

D. Sunitha, R. K. Patra, N. V. Babu, A. Suresh, and S. C. Gupta, “Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries,” Pattern Recognit. Lett., vol. 158, pp. 164–170, 2022, doi: 10.1016/j.patrec.2022.04.027.

Y. Zhang, K. Chen, Y. Weng, Z. Chen, J. Zhang, and R. Hubbard, “An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US,” Expert Syst. Appl., vol. 198, no. May 2021, p. 116882, 2022, doi: 10.1016/j.eswa.2022.116882.

W. He and G. Xu, “Social media analytics: unveiling the value, impact and implications of social media analytics for the management and use of online information,” Online Inf. Rev., vol. 40, no. 1, p. OIR-12-2015-0393, Feb. 2016, doi: 10.1108/OIR-12-2015-0393.

M. Kuhkan, “A Method to Improve the Accuracy of K-Nearest Neighbor Algorithm,” Int. J. Comput. Eng. Inf. Technol., vol. 8, no. 6, pp. 90–95, 2016, [Online]. Available:

R. S. Moorthy and P. Pabitha, “Optimal Detection of Phising Attack using SCA based K-NN,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 1716–1725, 2020, doi: 10.1016/j.procs.2020.04.184.

R. Blanquero, E. Carrizosa, P. Ramírez-Cobo, and M. R. Sillero-Denamiel, “Variable selection for Naïve Bayes classification,” Comput. Oper. Res., vol. 135, p. 105456, 2021, doi: 10.1016/j.cor.2021.105456.

N. Deepa, J. Sathya Priya, and T. Devi, “Towards applying internet of things and machine learning for the risk prediction of COVID-19 in pandemic situation using Naive Bayes classifier for improving accuracy,” Mater. Today Proc., no. xxxx, 2022, doi: 10.1016/j.matpr.2022.03.345.

M. Bilal, H. Israr, M. Shahid, and A. Khan, “Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques,” J. King Saud Univ. - Comput. Inf. Sci., vol. 28, no. 3, pp. 330–344, 2016, doi: 10.1016/j.jksuci.2015.11.003.

V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” Procedia Comput. Sci., vol. 161, pp. 765–772, 2019, doi: 10.1016/j.procs.2019.11.181.

S. Farhana, “Classification of Academic Performance for University Research Evaluation by Implementing Modified Naive Bayes Algorithm,” Procedia Comput. Sci., vol. 194, pp. 224–228, 2021, doi: 10.1016/j.procs.2021.10.077.

Hubert, P. Phoenix, R. Sudaryono, and D. Suhartono, “Classifying Promotion Images Using Optical Character Recognition and Naïve Bayes Classifier,” Procedia Comput. Sci., vol. 179, no. 2020, pp. 498–506, 2021, doi: 10.1016/j.procs.2021.01.033.

E. M. M. van der Heide, R. F. Veerkamp, M. L. van Pelt, C. Kamphuis, I. Athanasiadis, and B. J. Ducro, “Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle,” J. Dairy Sci., vol. 102, no. 10, pp. 9409–9421, 2019, doi: 10.3168/jds.2019-16295.

D. van Herwerden, J. W. O’Brien, P. M. Choi, K. V. Thomas, P. J. Schoenmakers, and S. Samanipour, “Naive Bayes classification model for isotopologue detection in LC-HRMS data,” Chemom. Intell. Lab. Syst., vol. 223, no. November 2021, p. 104515, 2022, doi: 10.1016/j.chemolab.2022.104515.

A. Jhamtani, R. Mehta, and S. Singh, “Size of wallet estimation: Application of K-nearest neighbour and quantile regression,” IIMB Manag. Rev., vol. 33, no. 3, pp. 184–190, 2021, doi: 10.1016/j.iimb.2021.09.001.

Y. Amonkar, D. J. Farnham, and U. Lall, “A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling,” Patterns, vol. 3, no. 3, p. 100454, 2022, doi: 10.1016/j.patter.2022.100454.

T. Olsson, M. Ericsson, and A. Wingkvist, “To automatically map source code entities to architectural modules with Naive Bayes,” J. Syst. Softw., vol. 183, p. 111095, 2022, doi: 10.1016/j.jss.2021.111095.

Z. E. Rasjid and R. Setiawan, “Performance Comparison and Optimization of Text Document Classification using k-NN and Naïve Bayes Classification Techniques,” Procedia Comput. Sci., vol. 116, pp. 107–112, 2017, doi: 10.1016/j.procs.2017.10.017.

A. Islam, S. B. Belhaouari, A. U. Rehman, and H. Bensmail, “K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation,” Softw. Impacts, vol. 12, no. February, p. 100272, 2022, doi: 10.1016/j.simpa.2022.100272.

A. Rafdi, H. Mawengkang, and S. . Efendi, "Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm", Int. J. Adv. Data Inf. Syst., vol. 2, no. 2, pp. 96-104, Oct. 2021.

D. Saputra, W. Irmayani, D. Purwaningtias, J. Sidauruk, and B. Gurbuz, "A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction", Int. J. Adv. Data Inf. Syst., vol. 2, no. 2, pp. 84-95, Oct. 2021.




How to Cite

Rozaq, A., Yunitasari, Y., Sussolaikah, K., & Sari, E. R. N. . (2022). Sentiment Analysis of Kampus Mengajar 2 Toward the Implementation of Merdeka Belajar Kampus Merdeka Using Naïve Bayes and Euclidean Distence Methods. International Journal of Advances in Data and Information Systems, 3(1), 30-37.
Abstract views : 119 times