Pattern Recognition of Human Activity Based on Smartphone Data Sensors Using SVM Multiclass Alman 1 , Armin Lawi 2 , Zulkifli Tahir 3 {alman16p@student.unhas.ac.id 1 , armin@unhas.ac.id 2 , zulkifli@unhas.ac.id 3 } 1 Department of Electrical Engineering, Universitas Hasanuddin, Indonesia. 92119 2 Departement of Computer Science, Universitas Hasanuddin, Indonesia. 92119 3 Department of Informatics Engineering, Universitas Hasanuddin, Indonesia. 92119 Abstract. Mobile devices are increasingly sophisticated while smartphones continue to make the latest generation that immerses the supporting tools needed in everyday life such as cameras, GPS, Microphones, and various sensors such as light sensors, a direction sensor, acceleration sensor (i.e., accelerometer) and the gyroscope sensor. This study aims to classify human activities from the accelerometer and gyroscope sensors on a Sony z3+ smartphone. To implement our system, we collect labeled accelerometer and gyroscope data from eight users when they carry out daily activity. Every activity was recorded for 22 seconds, total data that we use every activity is 2000 data with the total amount of data is 16000 data. This data we classify using the Multiclass Support Vector Machine (SVM) method reaches 97.40% accuracy using a 70% ratio as training data and 30% as test data, the classification process takes 5 seconds to classify the data. Keywords: Activity, Recognition, Accelerometer, Sensor, Support Vector Machine. 1 Introduction Mobile phones only can be used for call and sending a message. But as time goes by a mobile phone can be used to perform various tasks, for example for typing, internet, chatting, editing photos, sending emails, playing games, navigators, compasses, and we even use smartphones to monitor our health through recording activities, finding our location on maps, watching videos, and buying products on the Internet[1]. People-centric sensing says[2] using a smartphone as a sensing. Smartphones are now popular and its a need for people in recent years[3]. At first, the mobile phone components were very simple, but now a mobile phone has various sensors embedded in it. With this sophistication, today's mobile phones are called smartphones, the smartphone can be called because it has a variety of sensors that make a cell phone has many features. Embedded sensors on Smartphones can be utilized now. One of the advantages of introducing this activity is able to predict the potential for a user's fall[4] and it immediately sends notifications to the right people (i.e., family or medical staff)[5] and also it can provide information even though the user is sitting, walking, running, standing, sleeping, getting up, going upstairs and going downstairs. From the results of reading the sensor information can be used in this study to design the system in classifying the results of sensor data This sensor output is in the form of data where the data generated has hundreds or even thousands of records. Of course, the data cannot be read or understood only by looking at it or reading it directly. To find information that is not known before, potential[6] knowledge that ICOST 2019, May 02-03, Makassar, Indonesia Copyright © 2019 EAI DOI 10.4108/eai.2-5-2019.2284606