Currently, the development of mobile phones and mobile applications based on the Android operating system is growing rapidly. Many start-ups and startups are digitally transforming by using mobile apps to provide disruptive digital services to replace existing obsolete services. This transformation prompts attackers to create malicious software (malware) using sophisticated methods to target victims of Android phone users. Research in the field of security by analyzing Malware statically, has been very saturated and the accuracy results have reached 98% and many have even reached 99% accuracy. As a new challenger, the researcher wants to increase the accuracy of more than 99% by using the static method. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non-Neural Network (NNN). ANN is a Multi-Layer Perceptron Classifier (MLPC), while NNN is a method of KNN, SVM, Decision Tree. This study aims to make a comparison between the performance of Non-Neural Networks and Artificial Neural Networks. The problem that occurs when classifying using the Non-Neural Network algorithm has a problem with decreasing performance, where performance often decreases if it is done with a larger dataset. Answering the problem of decreasing model performance, a solution with the Artificial Neural Network algorithm is used. The artificial neural network algorithm chosen is the Multi_layer Perceptron Classifier (MLPC). Using the Non-Neural Network algorithm, K-Nearest Neighbor conducts training with the 600 APK dataset achieving 91.2% accuracy and training using the 14170 APK dataset reduces its accuracy to 88%. The use of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has decreased accuracy to 90.5%. The use of the Decision Tree algorithm to conduct training with the 600 APK dataset has an accuracy of 99.2% and training with the 14170 APK dataset has decreased accuracy to 90.8%. Experiments using the Multi-Layer Perceptron Classifier have improved accuracy performance with the 600 APK dataset reaching 99% accuracy and training using the 14170 APK dataset increasing the accuracy by reaching 100%.