Wart treatment method selection using AdaBoost with random forests as a weak learner

Main Article Content

M. Azka Putra
Noor Akhmad Setiawan
Sunu Wibirama


Selection of wart treatment method using machine learning is being a concern to researchers. Machine learning is expected to select the treatment of warts such as cryotherapy and immunotherapy to patients appropriately. In this study, the data used were cryotherapy and immunotherapy datasets. This study aims to improve the accuracy of wart treatment selection with machine learning. Previously, there are several algorithms have been proposed which were able to provide good accuracy in this case. However, the existing results still need improvement to achieve better level of accuracy so that treatment selection can satisfy the patients. The purpose of this study is to increase the accuracy by improving the performance of weak learner algorithm of ensemble machine learning. AdaBoost is used in this study as a strong learner and Random Forest (RF) is used as a weak learner. Furthermore, stratified 10-fold cross validation is used to evaluate the proposed algorithm. The experimental results show accuracy of 96.6% and 91.1% in cryotherapy and immunotherapy respectively.

Article Details

Author Biographies

M. Azka Putra, Department of Electrical Engineering and Information Technology, University Gadjah Mada

M. Azka Putra graduated bachelor degree from Bina Nusantara University. Then he is studying to get master degree programs in Gadjah Mada University

Noor Akhmad Setiawan, Department of Electrical Engineering and Information Technology, University Gadjah Mada

Noor Akhmad Setiawan received his Bachelor and Master degree in Electrical Engineering from Universitas Gadjah Mada in 1998 and 2003 respectively. He received his PhD degree in Electrical and Electronics Engineering from Universiti Teknologi PETRONAS in 2009. He is with the Department of Electrical Engineering and Information Technology Universitas Gadjah Mada. His research interests are Soft Computing and Machine Learning.

Sunu Wibirama, Department of Electrical Engineering and Information Technology, University Gadjah Mada

Sunu Wibirama serves as faculty member in Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Indonesia. His primary research area includes computer vision, eye-gaze tracking applications, human-computer interaction, and technology commercialization. He is also keen to work in usability testing, user experience (UX), and marketing research based on eye tracking analysis.

Dr. Sunu was appointed as a research associate under JICA and Erasmus Mundus fellowship, member of IEEE Engineering in Medicine and Biology Society (EMBS), IEEE Consumer Electronics, IEEE Biometrics Council, and IEEE Sensors Council. He is an Associate Editor in Indonesian Scholar Journal. In 2015, he served as Vice Head of Academic Affairs for Undergraduate Program in Department of Electrical Engineering and Information Technology, Faculty of Engineering, UGM. In 2017, he was appointed as a Senate Member of Faculty of Engineering, Universitas Gadjah Mada.


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