Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : bit-Tech

Classification of Phishing URL Attacks Using Random Forest Algorithm Based on Feature Importance Melyana Hasibuan; Rahmad Abdillah; Surya Agustian; Reski Mai Candra
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3511

Abstract

The development of information technology and increasing digital activities have made URL-based phishing threats more complex and difficult to detect. Phishing attacks target not only individuals but also organizations, requiring detection systems that are accurate, efficient, and capable of handling high-dimensional data. Machine learning approaches, particularly Random Forest, have been widely applied for phishing detection; however, further evaluation is needed regarding the role of feature selection in improving efficiency without reducing performance. This study aims to evaluate the performance of the Random Forest algorithm for phishing URL detection and to analyze the impact of feature selection based on feature importance. This research adopts the Knowledge Discovery in Databases (KDD) framework, including data selection, preprocessing, feature selection, modeling, and evaluation stages. The PhiUSIIL-2024 dataset is used, with two modeling scenarios: Random Forest using all features (RF Full) and Random Forest using the top 30 features selected through feature importance (RF Top-30). Model performance is evaluated using accuracy, precision, recall, and F1-score metrics under different data split ratios. The experimental results show that both models achieve very high and stable classification performance, with evaluation metrics close to or reaching 100%. The RF Top-30 model maintains performance comparable to the RF Full model despite using fewer features. This study concludes that feature importance-based feature selection effectively simplifies the Random Forest model without sacrificing performance, making it suitable for efficient URL phishing detection systems.
Co-Authors .Safrizal, Safrizal Abdillah, Rahmad Afdhal Zikri Afriyanti, Liza Aftari, Dhea Putri AGUNG SUCIPTO Ahmad, Rizmah Zakiah Nur Alfitra Salam Arasy, Abdurrahman Ash Shiddicky Aulia Ramadhani Ayu Fransiska Baehaqi Delifah, Nur Dermawan, Jozu Dzaky Abdillah Salafy Eka Pandu Cynthia El Saputra, Yoga Elin Haerani Elvia Budianita Fahrezy, Irgi Faizah Husniah Fauzan Ray T Fauzi Ihsan Febi Yanto Febrian Rizki Adi Sutiyo Fitri Insani Fitri Insani Fitri Wulandari Fitri, Dina Deswara Fuji Astuti Gusti, Siska Kurnia Habib Hakim Sinaga Hadi, Mukhlis Halimah Heru Wibowo Idhafi, Zaky Iffa, Marwika Rifattul Ihsan, Miftahul Iis Afrianty Iis Afrianty Ilham Habibi Hasibuan Illahi, Ridho Iman Fauzi Aditya Sayogo Indri Pangestuti Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Lestari Handayani Lubis, Anggun Tri Utami BR. Melyana Hasibuan Miftah Farid Muhammad Fikry Muhammad Fikry Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Irsyad Muhammad Ravil Muktar Sahbuddin Mukti M Kusairi Mulyadi, Syahrul Nadila Handayani Putri naldi, Afri Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Novriyanto Nurul Fatiara Okfalisa Okfalisa Oktavia, Lola Pangestu, Yoga Pizaini Pizaini Pranata, Joni Prima Yohana Putri Zahwa Putri, Adilah Atikah Putri, Atika Rahmad Abdillah Rahmad Kurniawan Ramadhani, Siti Reski Mai Candra Reski Mai Candra Rizqa Raaiqa Bintana Safrizal, Afri Naldi Salam Kurniawan Saputra, Ikhsan Dwi Saputra, M Ridho Saputra, Nugroho Wahyu Sinaga, Habib Hakim Siti Ramadhani Siti Ramadhani Siti Ramadhani Sri Puji Utami A. Subhi, Yazid Abdullah Suci Rahayu Sulistia Ningsih, Sulistia Suwanto Sanjaya Syaiful Azhar Tarmizi, Veci Cahyono Trya Ayu Pratiwi Utari, Roid Fitrah Wan Sobri Amin Yusra Yusra Yusra, Yusra