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All Journal International Journal of Electrical and Computer Engineering Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing International Journal Software Engineering and Computer Science (IJSECS)
Hindarto, Djarot
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Public Health

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Journal : Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)

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Performance Comparison of Supervised Learning Using Non-Neural Network and Neural Network Hindarto, Djarot; Santoso, Handri
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol 11, No 1 (2022)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v11i1.40768

Abstract

Currently, the development of mobile phones and mobile applications based on the Android operating system is increasing rapidly. Many new companies and startups are digitally transforming by using mobile apps to provide disruptive digital services to replace existing old-fashioned services. This transformation prompted attackers to create malicious software (malware) using sophisticated methods to target victims of Android phone users. 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 the Non-Neural Network and the Neural Network. Problems that occur when classifying using the Non-Neural Network algorithm have problems with decreasing performance, where performance is often decreased if done with a larger dataset. Answering the problem of decreasing model performance, the solution is used with the Artificial Neural Network algorithm. The Artificial Neural Network Algorithm selected is 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 decreases its accuracy to 88%. The use of the Support Vector Machine algorithm with the 600 APK dataset has 99.1% accuracy and the 14170 APK dataset has decreased accuracy to 90.5%. The use of the Decision Tree algorithm to conduct training with a dataset of 600 APKs has an accuracy of 99.2% and training with a dataset of 14170 APKs has decreased accuracy to 90.8%. Experiments using the Multi-Layer Perceptron Classifier have increased accuracy performance with the 600 APK dataset achieving 99% accuracy and training using the 14170 APK dataset increasing the accuracy reaching 100%.
Co-Authors Afarah, Sabrina Fajrul Afarini, Nihayah Alim, Syariful Alwi, Muchamad Nurdin Amalia, Nadia Amanda, Djaja Anna Marina Arie Gunawan Arko Djajadi Dazki, Erick Djunarto Eko Hadianto, Eko Gustian, Yonathan Wijaya Handayani, Endah Tri Esti Haryono Haryono Hendrata, Ferial Indrajit, Eko ISWAHYUDI ISWAHYUDI Judijanto, Loso Nurfaizi, Kelfin Prawira, Khairul Thamrin R. Eko Indrajit Rahman, Ben Ramadhan, Duta Pramudya Ratih Titi Komala Sari, Ratih Titi Komala Ririn Ikana Desanti Santoso, Handri Saputra, Adi Dwifana Saputra, Adi Dwifana Saputra Sari Ningsih Sentot Imam Wahjono Sjah, William Surya Suherman, Endang Sze, Edward Usman, Sabrina Fajrul Ula Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa Wijaya, Yunan Fauzi Wirayasa, I Ketut Adi Yudhakusuma, Dudi