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Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 1 Documents
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Search results for , issue "Vol 17 No 6 (2026): Computer Science" : 1 Documents clear
Comparative Study of CatBoost, XGBoost, Random Forest, and Decision Tree for Phishing Web Page Classification Haryani, Haryani; Agustyaningrum, Cucu Ika
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

Phishing is a fraudulent method in which attackers using fake websites steal user information such as login credentials and sensitive financial data. Therefore, this study compares four machine learning algorithms, namely CatBoost, XGBoost, Random Forest, and Decision Tree, in classifying phishing websites efficiently and accurately. In this study, the dataset used is the Web Page Phishing Dataset, which begins with exploration and preprocessing, which includes data cleaning, handling missing values, normalization, feature selection, and testing. Post-split. The data used has been divided into training data and test data, namely 80:20. The model was implemented using Python in Google Colaboratory. Model performance evaluation was measured in five main metrics, such as accuracy, precision, recall, F1-score, and AUC. The experimental results indicate that CatBoost achieved the best position with a performance of 89.57% in accuracy, 85.74% in F1-score, 88.73% in precision, 88.78% in recall, and 89.00% in AUC. XGBoost ranked second with a very competitive performance, followed by Random Forest, which was relatively stable with an accuracy value of 89.41% and an F1-score of 85.35%. On the other hand, the decision tree achieved the lowest performance with an accuracy of 88.69% and an F1-score of 84.10%. These performance results indicate limitations in handling complex data, as well as a tendency to overfit. Overall, ensemble boosting-based algorithms, especially CatBoost and XGBoost, outperform single trees in detecting phishing websites. These results will be benefical to?progress in the next generation for the construction of intelligent based phishing detection system under machine learning. In addition, the outcomes of this study will gain momentum for future works where hyperparameter optimization, larger datasets and real-time applications for phishing detection systems?can be focused. Furthermore, this work will contrast the application of ensemble?algorithm in the cybersecurity field.

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