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Muhamad Malik Matin, Iik
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Computer Science & IT

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Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware Muhamad Malik Matin, Iik
MULTINETICS Vol. 9 No. 1 (2023): MULTINETICS Mei (2023)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v9i1.5578

Abstract

Random forest is one of the popular machine learning algorithms used for classification tasks. In malware detection tasks, random forest can help identify malware with good accuracy. However, to improve model performance, a hyperparameter tuning process is required. GridsearchCV is a hyperparameter tuning method that allows the user to scan a number of selected hyperparameters. In this paper, we conduct experiments using GridsearchCV to perform hyperparameter tuning on Random forests for malware detection tasks. The experimental results show that by performing hyperparameter tuning, we can improve the model's accuracy in identifying malware
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