<![CDATA[Until recently, the growth of the Android operating system on smartphone devices was popular. However, behind this popularity, the Android platform is also a target of opportunity for cybercrime against cybersecurity threats such as malware. Identifying this malware is very important to maintain user security and privacy. Due to the increasingly complicated process of malware identification, it is necessary to use machine learning for malware classification. This research collects static analysis features of safe and malicious applications. (malware). The dataset used in the research is the DREBIN malware dataset which is a publicly available malware dataset. The dataset consists of API CALL, system command, manifest permission, and Intent features. The data is then processed using various supervised machine learning algorithms including Support Vector Machine (SVM), Naive Bayes, Decision Tree and KNearest Neighbors. We also concentrate on maximising the achievement by evaluating various algorithms and adjusting some configurations to get the best combination of hyper-parameters. The experimental results show that SVM model classification gets the best result by achieving 96.94% accuracy and 95% AUC (Area Under Curve) value.]]>
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