[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123971-id":3,"doc-seo-123971-113":31,"detail-sidebar-cat-0-id-113":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},123971,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",54,"Penelitian & Laporan","Deteksi Phishing Website menggunakan Machine Learning - Metode Klasifikasi","Phishing website merupakan mekanisme kriminal berbasis social engineering dan dalih teknis untuk mengambil data identitas personal serta kredensial akun keuangan pengguna. Laporan Pandi menunjukkan jumlah phishing dalam lima tahun terakhir mencapai 34.622, dengan serangan unik pada Q3 2022 sebanyak 7.988. Penelitian ini membandingkan Decision Tree, Random Forest, dan KNN untuk klasifikasi berbasis fitur URL. Hasil menunjukkan Decision Tree dan Random Forest menghasilkan akurasi sekitar 0.83, sedangkan KNN lebih rendah, sehingga Random Forest menjadi metode terbaik.","Sistemasi: Jurnal Sistem Informasi ISSN:2302-8149  \nVolume 13, Nomor 4, 2024: 1368-1380 e-ISSN:2540-9719  \nDeteksi Phishing Website menggunakan Machine Learning  \nMetode Klasifikasi  \nPhishing Website Detection using Machine Learning Classification  \nMethod  \n1Azzam Fawwaz Mahmud*, 2 Setia Wirawan  \n1Perangkat Lunak dan Sistem Informasi, Manajemen Sistem Informasi, Universitas Gunadarma 2Sistem Informasi, Ilmu Komputer dan TI, Universitas Gunadarma  \n*[e-mail:](e-mail: azzamfmahmud@gmail.com)[ ](e-mail: azzamfmahmud@gmail.com)[azzamfmahmud@gmail.com](e-mail: azzamfmahmud@gmail.com)  \n(received: 24 August 2023, revised: 10 March 2024, accepted: 19 July 2024)  \nAbstrak  \nPhishing website merupakan mekanisme kriminal yang menggunakan social engineering serta dalih teknis untuk mengambil data identitas personal dan kredensial akun keuangan dari pelanggan. Di Indonesia sendiri menurut laporan Pengelola Nama Domain Internet Indonesia (Pandi), tercatat jumlah phishing dalam kurun waktu 5 tahun terakhir mencapai 34.622. Jumlah serangan phishing unik yang dilaporkan pada Q3 2022 sebanyak 7.988. Penelitian ini bertujuan untuk mencari algoritma machine learning klasifikasi dengan performa terbaik untuk mendeteksi phishing website menggunakan fiturfitur URL. Algoritma klasifikasi yang akan dibandingkan adalah Decision Tree, Random Forest, dan KNN. Hasil dari penelitian ini adalah model pertama yang menggunakan Decision Tree didapat akurasisebesar 0.833, presisi sebesar 0.86, recall sebesar 0.83, dan F1-score sebesar 0.83. Model kedua yang menggunakan algoritma Random Forest mendapat akurasi sebesar 0.834, presisi sebesar 0.86, recall sebesar 0.83, dan F1-score sebesar 0.83. Model terakhir yang menggunakan algoritma K-Nearest Neighbors mendapat akurasi sebesar 0.482, presisi sebesar 0.24, recall sebesar 0.50, dan F1-scoresebesar 0.48. Maka, dari ketiga algoritma tersebut random forest merupakan algoritma terbaik untuk mendeteksi phishing website  \nKata kunci: phishing website, machine learning, klasifikasi, decision tree, random forest, KNN  \nAbstract  \nPhishing websites are criminal mechanisms that use social engineering and technical pretexts to extract personal identification data and financial account credentials from customers. In Indonesia alone, according to a report by the Indonesian Internet Domain Name Manager (Pandi), the number of phishing recorded in the last 5 years has reached 34,622. The number of unique phishing attacks reported in Q3 2022 was 7,988. This study aims tofind a classification machine learning algorithm with the best performance for detecting phishing websites using URL features. The classification algorithms to be compared are the decision tree, random forest, and KNN. The results of this study are that the first model that uses a decision tree obtains an accuracy of 0.833, aprecision of 0.86, a recall of 0.83, and an F1-score of 0.83. The second model that uses the random forest algorithm gets an accuracy of 0.834, aprecision of 0.86, a recall of 0.83, and an F1-score of 0.83. The last model that uses the K-Nearest Neighbors algorithm gets an accuracy of 0.482, aprecision of 0.24, a recall of 0.50, and an F1-score of 0.48. Thus, of the three algorithms random forest is the best algorithm for detecting phishing websites  \nKeywords: phishing website, machine learning, classification, decision tree, random forest, KNN  \n1 Pendahuluan  \nDalam era digital saat ini, internet telah menjadi bagian tak terpisahkan dari kehidupan sehari-hari, dimana banyak transaksi dan komunikasi dilakukan secara online. Pemanfaatan internet telah merasuki hampir semua sektor dan industri, seperti e-commerce, transportasi, pariwisata, kesehatan, [http://sistemasi.ftik.unisi.ac.id](http://sistemasi.ftik.unisi.ac.id)  \nSistemasi: Jurnal Sistem Informasi ISSN:2302-8149  \nVolume 13, Nomor 4, 2024: 1368-1380 e-ISSN:2540-9719  \npemerintahan (e-government), dan industri keuangan. Di Indonesia, pada tahun 2019, jumlah pengguna internet menca","cbCainjsDtYDSx96","https://ap.wps.com/l/cbCainjsDtYDSx96","pdf",515892,8,1,13,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang internet dan serangan siber\n## Definisi phishing dan phishing website\n## Dampak dan data kasus phishing","[{\"question\":\"Apa tujuan penelitian deteksi phishing website dalam dokumen ini?\",\"answer\":\"Mencari algoritma machine learning klasifikasi dengan performa terbaik untuk mendeteksi phishing website menggunakan fitur URL.\"},{\"question\":\"Algoritma machine learning apa saja yang dibandingkan untuk klasifikasi phishing website?\",\"answer\":\"Decision Tree, Random Forest, dan K-Nearest Neighbors (KNN) dibandingkan untuk menentukan performa deteksi.\"},{\"question\":\"Bagaimana kesimpulan performa algoritma berdasarkan metrik akurasi, presisi, recall, dan F1-score?\",\"answer\":\"Decision Tree dan Random Forest memperoleh metrik yang tinggi (akurasi sekitar 0.833–0.834), sedangkan KNN jauh lebih rendah. Random Forest dinyatakan sebagai algoritma terbaik untuk mendeteksi phishing website.\"}]","Deteksi Phishing Website menggunakan Machine Learning - Metode Klasifikasi | PDF",1785819497,20,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"phishing-website-detection-using-machine-learning-classification-method","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/phishing-website-detection-using-machine-learning-classification-method/123971/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-18","2026-08-04",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Apa tujuan penelitian deteksi phishing website dalam dokumen ini?","Question",{"text":77,"@type":78},"Mencari algoritma machine learning klasifikasi dengan performa terbaik untuk mendeteksi phishing website menggunakan fitur URL.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Algoritma machine learning apa saja yang dibandingkan untuk klasifikasi phishing website?",{"text":82,"@type":78},"Decision Tree, Random Forest, dan K-Nearest Neighbors (KNN) dibandingkan untuk menentukan performa deteksi.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana kesimpulan performa algoritma berdasarkan metrik akurasi, presisi, recall, dan F1-score?",{"text":86,"@type":78},"Decision Tree dan Random Forest memperoleh metrik yang tinggi (akurasi sekitar 0.833–0.834), sedangkan KNN jauh lebih rendah. Random Forest dinyatakan sebagai algoritma terbaik untuk mendeteksi phishing website.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]