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All Journal Jurnal Sistem dan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Fountain of Informatics Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Repositor Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Perempuan & Anak Jurnal Sistem Informasi
Mahar Faiqurahman
Universitas Muhammadiyah Malang
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Fountain of Informatics Journal

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Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine Hendra Saputra; Setio Basuki; Mahar Faiqurahman
Fountain of Informatics Journal Vol 3, No 1 (2018): Mei
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v3i1.1875

Abstract

Android Malware has grown significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each type of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type will be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver.  To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used is Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). A result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicates that permission and broadcast receiver can be used in classifying type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection.
Co-Authors Abdul Rauf Akbi, Denar Regata Anak Agung Gede Sugianthara Arya, Tri Fidrian Diah Risqiwati Dwi Nurmasari Pratiwi Fidiyanto, Akhsanul Gita Indah Marthasari Hanum Shirotu Nida Haris Zulfikar Islam Hendra Saputra Hidayatullah, Rakhmad Arif Hidayatullah, Rakhmad Arif inung bagus prasetyo Jurdan Wahyu Adi Saputra Luqman Hakim Miftakhudin Kusuma Wijaya Muhammad Malik Madani Muhammad Malik Madani Novitasari, Diyan Anggraini Novitasari, Diyan Anggraini Rino Nugroho Sari, Zamah Sari, Zamah Setio Basuki Sudirman Sudirman Sumadi, Fauzi Dwi Setiawan Supriyanto Supriyanto Supriyanto Tri Fidrian Arya Vicky Dian Pramana Putra Wahyu Andhyka Kusuma Wildan Suharso Wildan Suharso Yufis Azhar Zamah Sari