Sri Wahyuni Kalumbang(1*)

(1) Universitas Gadjah Mada
(*) Corresponding Author


Classification analysis is a method that aims to group a number of observations or observations into certain classes based on features or independent variables from these observations. In Hasti et al. {\cite{Hasti}} it is stated that statistical researchers in 2003 competed various classification methods and various metrics to measure their goodness in line with the scientific activities carried out by the statisticians above.
Therefore, the researcher is interested in comparing 2 statistic methods and the now popular method, namely the random forest, using data sets taken from and using FPR and accuration to see the goodness of the model.
The results of this study indicate that the random forest method has better predictive results than the other two methods seen from its accuration.


machine learning, classification, classical method, random forest, FPR.

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Aisah, Lely Nur.(2019). Implementasi Naive Bayes dan Random Forest untuk Analisis Sentimen terhadap Data Imbalanced Review Produk Kosmetik pada Platform Online Sociolla, Skripsi, Program Studi S11 Statistika FMIPA UGM, Yogyakarta.

Berry, Michael W. and Murray Browne. (2006). Lecture Notes in DATA MINING. World Scientific, USA.

Breiman, Leo. (2001). Machine Learning. Berkeley : University of Calidornia.

Dangeti, Pratap.( 2017). Statistics for Machine Learning. Packt Publishing Ltd, UK.

Gladence, L. Mary, Karthi, M., Anu, V. Maria. (2015). A statistical comparison of logistic regression and different bayes classification methods for machine learning.ARPN Journal of Engineering and Applied Sciences: Vol. 10, No. 14,(AUGUST 2015).

Han, Jiawei and Kamber, Micheline. (2012). Data Mining concepts and Techniques, Third Edition. Elsevier Inc. All rights reserved, USA.

Hasti, Trevor, Tibshirani, Robert, Friedman,Jerome. (2009). The Elements of Statistical Learning, Second Edition. Springer, California.

Hosmer, David W.( 1989). Applied Logistic Regression. John Wiley & Sons, Inc., Canada.

Hosmer, David W.( 2013). Applied Logistic Regression, Third Edition. John Wiley & Sons, Inc., Canada.

James, Gareth, et al. (2017). An Introduction to Statistical Learning, Eighth Edition. Springer,New York Heidelberg Dordrecht London.

Setyawan, M.Y.H., Awangga, R.M., Efendi, S.R. (2018). Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot, Proceedings of the 2018 International Conference on Applied Engineering, ICAE 2018, art. no.8579372, . Cited 4 times.

Telnoni, P.A., Budiawan, R., Qana’a, M.(2019). Comparison of Machine Learning Classification Method on Text-based Case in Twitter, Proceeding - 2019 International Conference on ICT for Smart Society: Innovation and Transformation Toward Smart Region, ICISS 2019, art. no. 8969850,.

Tsangaratos, P., Ilia, I. (2016). Comparison of a logistic regression and Na¨ıve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size, Catena, 145, pp. 164-179. Cited 125 times.


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