PERBANDINGAN KINERJA ALGORITMA GRADIENT BOOSTING DAN MULTILAYER PERCEPTRON DALAM KLASIFIKASI WEBSITE PHISHING

ROMADI, SAHRUL (2025) PERBANDINGAN KINERJA ALGORITMA GRADIENT BOOSTING DAN MULTILAYER PERCEPTRON DALAM KLASIFIKASI WEBSITE PHISHING. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Phishing is one of the most prevalent cyber threats, involving fraudulent attempts to obtain sensitive victim information, such as passwords or credit card details, through fake websites. According to data from the Indonesia Domain Abuse Data Exchange (IDADX), the number of phishing cases in Indonesia continues to increase significantly each year. Various machine learning algorithms have been implemented to detect phishing websites. Previous research has indicated that Gradient Boosting and Multilayer Perceptron exhibit superior performance compared to other algorithms. However, no prior study has directly compared the performance of these two models. This research aims to compare the performance of the Gradient Boosting and Multilayer Perceptron algorithms in detecting phishing websites. The model evaluation was conducted using accuracy, precision, recall, and F1-Score metrics. The dataset was sourced from the UCI Machine Learning Repository, comprising 11,055 instances and 12 features, and was split into an 80:20 ratio for training and testing data. The results show that Gradient Boosting is superior, achieving an accuracy of 95.48%, a precision of 0.95, a recall of 0.95, and an F1-Score of 0.95. Meanwhile, Multilayer Perceptron recorded an accuracy of 94.93%, a precision of 0.95, a recall of 0.95, and an F1-Score of 0.95. These findings are expected to serve as a reference for selecting a more reliable classification algorithm for detecting phishing websites. Keywords: Phishing Websites, Machine Learning, Classification, Gradient Boosting, Multilayer Perceptron Phishing merupakan salah satu ancaman siber yang paling marak terjadi, dengan modus penipuan untuk mendapatkan informasi sensitif korban, seperti kata sandi atau kartu kredit melalui website palsu. Berdasarkan data dari Indonesia Domain Abuse Data Exchange (IDADX), jumlah kasus phishing di Indonesia terus mengalami peningkatan signifikan setiap tahunnya. Berbagai algoritma machine learning telah diterapkan untuk mendeteksi website phishing. Penelitian sebelumnya menunjukkan bahwa Gradient Boosting dan Multilayer Perceptron memiliki kinerja yang lebih unggul dibanding dengan algoritma lainnya. Namun, hingga penelitian ini dilakukan belum terdapat penelitian yang secara khusus membandingkan kinerja kedua model tersebut secara langsung. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Gradient Boosting dan Multilayer Perceptron dalam mendeteksi website phishing. Evaluasi model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-Score. Dataset yang digunakan berasal dari situs UCI Machine Learning Repository dengan total 11.055 data dan 12 fitur, serta dibagi dalam rasio data pelatihan dan data pengujian sebesar 80:20. Hasil penelitian menunjukkan bahwa Gradient Boosting lebih unggul dengan akurasi mencapai 95.48%, precision 0.95, recall 0.95, dan F1-Score 0.95. Sementara itu, Multilayer Perceptron mencatat akurasi 94.93%, precision 0.95, recall 0.95, dan F1-Score 0.95. Temuan ini diharapkan dapat menjadi referensi dalam pemilihan algoritma klasifikasi yang lebih andal dalam mendeteksi website phishing. Kata kunci: Website Phishing, Machine Learning, Klasifikasi, Gradient Boosting, Multilayer Perceptron

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 097
NIM/NIDN Creators:	41521010084
Uncontrolled Keywords:	Website Phishing, Machine Learning, Klasifikasi, Gradient Boosting, Multilayer Perceptron
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.4 Subject Analysis and Control/Subjek Analisis dan Kontrol Perpustakaan > 025.46 Classification of Specific Subject/Klasifikasi Khusus 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	02 Aug 2025 03:13
Last Modified:	02 Aug 2025 03:13
URI:	http://repository.mercubuana.ac.id/id/eprint/96467

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