ABIDA, REIHAN SETYA (2023) IMPLEMENTASI ALGORITMA KLASIFIKASI RANDOM FOREST DAN KNN UNTUK MENDETEKSI WEBSITE PHISHING. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
In industry 4.0, where the internet is easily accessible, the internet really makes it easy for everyone, from children, students, adults, and parents who have access to it, one example is opening a website. There is such a thing as a phishing website, which is a fake website where the goal is to trap the person who opens it. The purpose of this study is to classify the phishing website dataset to detect whether this website is fake or not, with the Random Forest and KNN algorithms, the dataset used is named malicious phish, by making several parameters obtained from previous journals for the test process, each prediction model is implemented with an algorithm Random Forest and KNN as well as calculating the confusion matrix to get the accuracy value of the two algorithms, it is known that the Random Forest algorithm has a higher accuracy of 86% while the KNN algorithm gets an accuracy of 82.3%, the research ends by testing 10 phishing urls and 10 safe urls, the results state that the Random Forest algorithm can classify phishing websites properly. Keywords : random forest, knn, phishing website, classification Di industry 4.0 dimana internet mudah diakses, Internet benar-benar membuat kemudahan bagi seluruh kalang mulai dari anak-anak, pelajar, orang dewasa, dan orang tua yang memiliki akses untuk kesana salah satu contohnya adalah membuka website. Ada yang Namanya website phishing yaitu website palsu dimana tujuannya untuk menjebak orang yang membukanya. Tujuan penelitian ini untuk mengklasifikasikan dataset website phishing untuk mendeteksi website ini palsu atau tidak, dengan algoritma Random Forest dan KNN, dataset yang digunakan bernama malicious phish, dengan membuat beberapa parameter yang didapatkan dari jurnal terdahulu untuk proses uji, setiap model prediksi di implementasikan dengan algoritma Random Forest dan KNN serta melakukan perhitungan confusion matrix untuk mendapatkan nilai akurasi kedua algoritma, diketahui bahwa algoritma Random Forest memiliki akurasi lebih tinggi yaitu 86% sedangkan algoritma KNN mendapatkan akurasi sebesar 82.3%, penelitian diakhiri dengan melakukan tes 10 url phishing dan 10 url safe, hasilnya menyatakan bahwa algoritma Random Forest dapat mengklasifikasikan website phishing dengan baik. Kata Kunci : Phishing, Random Forest, KNN, Klasifikasi
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