Indonesian Journal of Electrical Engineering and Computer Science Vol. 22, No. 2, May 2021, pp. 733~743 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v22.i2.pp733-743  733 Journal homepage: http://ijeecs.iaescore.com Robot movement controller based on dynamic facial pattern recognition Siti Nurmaini 1 , Ahmad Zarkasi 2 , Deris Stiawan 3 , Bhakti Yudho Suprapto 4 , Sri Desy Siswanti 5 , Huda Ubaya 6 1,2,3,5,6 Faculty of Computer Science, Universitas Sriwijaya, Indonesia 4 Faculty of Engineering, Universitas Sriwijaya, Indonesia Article Info ABSTRACT Article history: Received Mar 20, 2020 Revised Dec 3, 2020 Accepted Jan 11, 2021 In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the upper and lower frame areas, resulting in an accuracy rate of 95%, then for the right and left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms). Keywords: Face detection Mobile robots Navigation technique Ram based neural network Ram discriminator This is an open access article under the CC BY-SA license. Corresponding Author: Siti Nurmaini Department of Computer System Universitas Sriwijaya, Indonesia Email: sitinurmaini@gmail.com 1. INTRODUCTION The robot can work in a variety of environmental conditions, both in a conditioned environment and in dangerous environments. With this reliability, robots are often used in helping various human activities on mobile [1]-[3]. Thus, the development of mobile robots has experienced a very large increase. In terms of movement, mobile robots are equipped with various navigation techniques [4]. This navigation technique helps the robot is moving and recognizing the environment well. However, conventional navigation techniques always optimize robot movements based on time variables, whereas robots that blend socially with humans do not need such a thing [5]. Mobile robots move to adjust to the human social environment, for example, avoiding collisions in crowded situations, helping in carrying goods, delivering orders, and communicating with humans by recognizing facial features. For personal interaction with the robot, accuracy is needed on facial features that can be recognized by the robot.