In the past years, the amount of Android malware is on the increase. This statement is supported by the data from VirusShare showing an increase in the amount of malware each passing year. Hence it is necessary to classify the malware for identifying types of malware attacking smartphone which consequently will help resolve the issue easier. For addressing the issue, this research classifies Android malware based on their types. The attributes employed are activities, permission, and receiver located inside the Androidmanifest.xml file. This research obtains the malware data from VirusShare database in 2018. Classification algorithms used was Support Vector Machine (SVM) with RBF kernel and k-fold = 10. In addition, this research also employed gain ratio feature selection to minimize unnecessary attributes on the data. The accuracy of classification using feature selection was 72.5%. This number was 0.3 lower compared to the classification result without feature selection with the accuracy of 72.8%. However, the data classified using feature selection can reduce the process of classification model creation by 206 seconds.