Copyright : © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Science, Engineering and Technology Print ISSN: 2395-1990 | Online ISSN : 2394-4099 (www.ijsrset.com) doi : https://doi.org/10.32628/IJSRSET207624 204 Performance Evaluation of Support Vector Machine Algorithm for Human Gesture Recognition Vina Ayumi Faculty of Computer Science, Universitas Mercu Buana, Jakarta Barat, Indonesia vina.ayumi@mercubuana.ac.id Article Info Volume 7 Issue 6 Page Number: 204-210 Publication Issue: November-December-2020 Article History Accepted: 10 Dec 2020 Published: 24 Dec 2020 ABSTRACT Research on human motion gesture recognition has been widely used for several technological devices to support monitoring of human-computer interaction, elderly people and so forth. This research area can be observed by conducting experiments for several body movements, such as hand movements, or body movements as a whole. Many methods have been used for human motion gesture recognition in previous studies. This paper attempted to collect data of performance evaluation of support vector machine algorithms for human motion recognition. We developed research methodology that is adapted PRISMA. This methodology is consisted of four main steps for reviewing scientific articles, including identification, screening, eligibility and inclusion criteria. After we obtained result of systematic literature review. We also conducted pilot study of SVM implementation for human gesture recognition. Based on the previous study result, the accuracy performance of vector machine algorithms for body gesture dataset is between 82.88% - 99.92% and hand gesture dataset 88.24% - 95.42%. Based on our pilot experiment, recognition accuracy with the SVM algorithm for human gesture recognition achieved 94,50% (average) accuracy. Keywords: Human Gesture, Support Vector Machine, PRISMA I. INTRODUCTION The implementation of human motion gesture recognition has been widely used for several technological devices to support monitoring of human-computer interaction, elderly people, sport training movements and so on (Kale & Patil, 2016; Kumari, Mathew, & Syal, 2017; Zhou & Hu, 2008). Applications that utilize human motion gesture recognition have had a positive impact and great business value, such as applications on watches or smart phones as part of a smart home device that can monitor or monitor the activities of people inside (Del Rio, Sovacool, Bergman, & Makuch, 2020; Sovacool &