Deteksi URL phishing menggunakan kombinasi model XGBoost dan Random Forest

Ilham Maulana Hadinanda; Sindhu Rakasiwi

doi:10.24246/aiti.v23i3.363-377

Authors

Ilham Maulana Hadinanda Universitas Dian Nuswantoro https://orcid.org/0009-0006-7632-7749
Sindhu Rakasiwi Universitas Dian Nuswantoro

DOI:

https://doi.org/10.24246/aiti.v23i3.363-377

Keywords:

phising, URL, XGBoost, Random Forest, ensemble learning

Abstract

Phishing is one form of cyber attack in today's digital era. This attack uses manipulation techniques to deceive users, so that they unknowingly disclose sensitive data through fake websites that mimic the appearance of official websites. Detecting phishing URLs is an important step in improving cyber security and reducing user losses. This study uses an ensemble learning approach by combining two models, Extreme Gradient Boosting (XGBoost) and Random Forest, into one combined model. Both models were trained using a dataset consisting of 11,430 URLs, divided equally between phishing and legitimate classes. The model was tested using accuracy, precision, recall, and F1-score metrics. The results of the experiment show that the combined XGBoost and Random Forest model produced the highest accuracy, at 0.944. This is superior to the single XGBoost (0.942) and Random Forest (0.928) models. These findings reinforce the evidence that the ensemble approach provides better generalization than single models for phishing detection.

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Author Biographies

Ilham Maulana Hadinanda, Universitas Dian Nuswantoro

Student, Informatics, Dian Nuswantoro University

Sindhu Rakasiwi, Universitas Dian Nuswantoro

Final Project Advisor, Informatics, Dian Nuswantoro University

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