KLASIFIKASI ADWARE DENGAN PRINCIPAL COMPONENT ANALYSIS (PCA) MENGGUNAKAN RANDOM FOREST

MARLIANSYAH, RIZKY and Heryanto, Ahmad (2022) KLASIFIKASI ADWARE DENGAN PRINCIPAL COMPONENT ANALYSIS (PCA) MENGGUNAKAN RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

Unsupervised feature extraction and selection algorithms, which are widely used to perform dimensionality reduction tasks to avoid overfitting. Machine Learning is a machine learning system in an artificial intelligence system approach or Artificial Intelligence with a simulation of the intelligence possessed by humans which is modeled in machines and programmed to think like humans. In this study, it is explained that the Adware classification using Random Forest is successful and this time it will use the algorithm from Principal Component Analysis (PCA) which functions as a dimension reduction process in the data used. From this research, the results obtained from the components are quite good with an accuracy value of 98.85%. While the recall value is 98.34%, the precision value is 98.52% and the FPR value is 0.44% and the OOB-error is 1.05%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Machine Learning, Klasifikasi, Adware, Algoritma PCA, Random Forest.
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Rizky Marliansyah
Date Deposited:	28 Nov 2022 08:19
Last Modified:	28 Nov 2022 08:19
URI:	http://repository.unsri.ac.id/id/eprint/82752

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