[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120299-id":3,"doc-seo-120299-113":31,"detail-sidebar-cat-0-id-113":85},{"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},120299,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",54,"Penelitian & Laporan","Phishing Detection on Ethereum Network - Menggunakan Metode Machine Learning","Penelitian ini membahas deteksi phishing pada jaringan Ethereum menggunakan metode machine learning, dengan fokus pada Graph Convolutional Networks (GCNs) dan Enhanced Graph Attention Networks (EGAT). Latar belakang penelitian didorong oleh meningkatnya serangan phishing dalam ekosistem blockchain yang berpotensi mengancam keamanan finansial pengguna. Tujuan penelitian adalah menganalisis tingkat kejadian serangan phishing serta mengembangkan metode deteksi yang efektif dan efisien. Penelitian menggunakan pengumpulan data transaksi Ethereum dan data phishing, ekstraksi fitur, pelatihan model, serta evaluasi memakai metrik akurasi, precision, recall, dan F-score. Hasil menunjukkan EGAT mencapai akurasi 93,6% lebih tinggi dibanding GCNs 91,2%, sehingga disimpulkan EGAT lebih unggul untuk mendeteksi aktivitas phishing.","Jurnal Pendidikan Indonesia  \n p-ISSN : 2745-7141 e-ISSN : 2746-1920 Vol. 6 No. 3 Maret 2025   \nPhishing Detection on Ethereum Network Menggunakan Metode Machine Learning  \nWindhy Rokhmat Rosmantyo*, Dhani Ariatmanto  \nUniversitas AMIKOM Yogyakarta, Indonesia  \n[Email: windhy.rosmantyo@students.amikom.ac.id](Email: windhy.rosmantyo@students.amikom.ac.id)*  \n\n| ABSTRAK |  |  |\n| --- | --- | --- |\n| Kata kunci: Phishing,\u003Cbr>Ethereum, Machine\u003Cbr>Learning, Graph\u003Cbr>Convolutional Networks, Enhanced Graph Attention Networks, Deteksi\u003Cbr>Keamanan |  | Penelitian ini membahas tentang deteksi phishing pada jaringan Ethereum menggunakan metode machine learning, khususnya Graph Convolutional Networks (GCNs) dan Enhanced Graph Attention Networks (EGAT) . Latar belakang penelitian ini didasari oleh meningkatnya serangan phishing di ekosistem blockchain yang dapat mengancam keamanan finansial pengguna. Tujuan penelitian adalah untuk menganalisis tingkat kejadianserangan phishing serta mengembangkan metode deteksi yang efektif dan efisien. Metode yang digunakan mencakuppengumpulan data transaksi Ethereum dan data phishing, diikuti dengan ekstraksi fitur, pelatihan model machine learning, danevaluasi menggunakan metrik seperti akurasi, precision, recall, dan F-score. Gap research yang diidentifikasi adalah kurangnyafokus pada deteksi phishing tahap awal di jaringan Ethereum sertatidak optimalnyametode yang ada dalam mengenali pola transaksi yang kompleks. Hasil penelitian menunjukkan bahwa EGATmemiliki akurasi sebesar 93,6%, lebih baik dibandingkan GCNs yang mencapai 91,2% . Kesimpulan dari penelitian ini adalah bahwa metode EGAT lebih unggul dalam mendeteksi aktivitas phishing, memberikan kontribusi signifikan terhadap keamanan di jaringan Ethereum. |\n| Keywords: Ethereum, Learning, Convolutional | Phishing, Machine Graph Networks, | ABSTRACT\u003Cbr>This study discusses phishing detection on the Ethereum network using machine learning methods, specifically Graph Convolutional Networks (GCNs) and Enhanced Graph Attention Networks (EGAT). The background of this research is based on the increasing number of phishing attacks in the blockchain ecosystem that can threaten the financial security of users. The research aims to analyze the incidence rate of phishing attacks and develop effective and efficient detection methods. The methodology includes data collection fromEthereum transactions and phishing activities, followed by feature extraction, machine learning model training, and evaluation using metrics such as accuracy, precision, recall, and F-score. The identified research gap is the lack of focus on early-stage phishing detection in the Ethereum network and the suboptimal performance of existing methods in recognizing complex transaction patterns. The results indicate that EGAT achieves an accuracy of 93.6%, outperforming GCNs, which reach 91.2%. The conclusion of this research is that the EGAT method is superior in detecting |\n| Enhanced Graph Attention Networks, Security Detection |  |  |\n\nWindhy Rokhmat Rosmantyo*, Dhani Ariatmanto  \nphishing activities, providing significant contributions to security in the Ethereum network.  \nPENDAHULUAN  \nBlockchain adalah basis data terdistribusi yang menyimpan catatan transaksi secarakronologis dan terenkripsi, dan data tersebut bersifat terdesentralisasi, saling mendukung, dan terhubung dalam rantai terkunci sehingga tidak dapat dimanipulasi. Untuk waktu yang cukup lama, data dan interaksi masyarakat disimpan dan dieksekusi melalui pihak ketiga yang terpusat dan terpercaya, seperti pemerintah atau perusahaan. Hal ini menyebabkan masyarakat menghadapi biaya tinggi, silo data, dan ketidakstabilan akibat perubahan organisasi. Dengan menggunakan teknologi blockchain, data didistribusikandi seluruh dunia, dan masyarakat tidak perlu bergantung pada pihak ketiga tradisional. Ini memberikan cara yang lebih dapat diandalkan untuk kepercayaan masyarakat, sambil juga mengurangijumlah biaya yang dibebankan oleh pe","cbCaiio6ezMt1z0S","https://ap.wps.com/l/cbCaiio6ezMt1z0S","pdf",252941,8,1,13,"Indonesian","id",113,"# Abstrak\n# Pendahuluan\n## Latar belakang blockchain dan Ethereum\n## Social engineering dan phishing\n## Definisi phishing","[{\"question\":\"Bagaimana hasil kinerja model yang diperoleh?\",\"answer\":\"EGAT mencapai akurasi 93,6%, lebih baik daripada GCNs yang mencapai 91,2%. (Evaluasi menggunakan metrik akurasi, precision, recall, dan F-score.)\"}]","Phishing Detection on Ethereum Network - Menggunakan Metode Machine Learning | PDF",1785729299,20,{"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":80,"head_meta":82,"extra_data":84,"updated_unix":29},"phishing-detection-on-ethereum-network-using-machine-learning-methods","",{"@graph":37,"@context":79},[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-detection-on-ethereum-network-using-machine-learning-methods/120299/",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","2026-08-17","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73],{"name":74,"@type":75,"acceptedAnswer":76},"Bagaimana hasil kinerja model yang diperoleh?","Question",{"text":77,"@type":78},"EGAT mencapai akurasi 93,6%, lebih baik daripada GCNs yang mencapai 91,2%. (Evaluasi menggunakan metrik akurasi, precision, recall, dan F-score.)","Answer","https://schema.org",{"og:url":53,"og:type":81,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":83,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":86},[87,92,96,100,104,108,110,114,118,122,126],{"id":88,"doc_module":4,"doc_module_name":47,"category_name":89,"show_sort_weight":90,"slug":91},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":93,"doc_module":4,"doc_module_name":47,"category_name":94,"show_sort_weight":90,"slug":95},48,"Cerita & Novel","story-novel",{"id":97,"doc_module":4,"doc_module_name":47,"category_name":98,"show_sort_weight":90,"slug":99},56,"Gaya Hidup","lifestyle",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":90,"slug":103},51,"Komik","comic",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":90,"slug":107},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":90,"slug":109},"research-report",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":90,"slug":113},49,"Sastra","literature",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":90,"slug":117},52,"Teknologi","technology",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":90,"slug":121},50,"Ujian","exam",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":90,"slug":125},57,"Umum","general",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":4,"slug":129},181,"Formulir","formulir"]