Model Deteksi Malware Pada Sistem Operasi Windows Menggunakan Teknik Machine Learning
Keywords:
Deteksi Malware, Machine Learning, Random Forest, SMOTE, Correlation Matrix, Sistem Operasi Windows.Abstract
Malware merupakan ancaman utama dalam dunia keamanan siber, khususnya pada sistem operasi Windows yang banyak digunakan. Penelitian ini bertujuan untuk membangun model deteksi malware berbasis machine learning dengan menggunakan algoritma Random Forest. Dataset yang digunakan adalah Optimized Malware Dataset, yang dianalisis melalui beberapa tahap preprocessing, termasuk penanganan missing value menggunakan imputasi mean, penanganan outlier dengan kombinasi teknik removal dan capping, serta normalisasi data menggunakan StandardScaler. Seleksi fitur dilakukan dengan Correlation Matrix untuk mengidentifikasi atribut yang paling relevan terhadap kelas target. Ketidakseimbangan kelas pada data ditangani menggunakan metode SMOTE (Synthetic Minority Over-sampling Technique), dan pembagian data dilakukan dengan train-test split. Model dilatih menggunakan Random Forest yang telah dioptimasi, kemudian dievaluasi melalui classification report, confusion matrix, serta 5-fold cross-validation. Hasil evaluasi menunjukkan bahwa model memiliki performa yang baik dalam mendeteksi dan mengklasifikasikan malware, ditunjukkan dengan nilai accuracy, precision, dan f1-score yang tinggi. Penelitian ini menunjukkan bahwa kombinasi teknik preprocessing dan algoritma Random Forest dapat menghasilkan sistem deteksi malware yang efektif dan adaptif, serta dapat dijadikan acuan dalam pengembangan sistem keamanan siber di masa depan
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