<mets:mets OBJID="eprint_31874" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2026-07-05T22:22:09Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_31874_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>DETEKSI MALICIOUS LINK PADA KONTEN DEWASA DI&#13;
PLATFORM X MENGGUNAKAN METODE &#13;
MACHINE LEARNING</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Muhammad Iqbal</mods:namePart><mods:namePart type="family">Ashari</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Perubahan kebijakan platform X pada Juni 2024 yang mengizinkan konten dewasa&#13;
secara konsensual telah menciptakan celah keamanan siber baru. Pelaku kejahatan&#13;
siber sering memanfaatkan konten tersebut sebagai umpan (bait) untuk&#13;
menyebarkan tautan phishing yang tersamar melalui layanan pemendek URL t.co.&#13;
Penelitian ini bertujuan untuk membangun sistem deteksi otomatis menggunakan&#13;
metode Machine Learning guna mengatasi keterbatasan sistem moderasi&#13;
konvensional. Dataset yang digunakan berjumlah 1.000 tautan yang dikumpulkan&#13;
melalui teknik crawling pada platform X, dengan komposisi seimbang antara&#13;
kategori aman dan berbahaya. Fitur leksikal dan statistik, khususnya Shannon&#13;
Entropy, diekstraksi dari struktur URL untuk melatih model klasifikasi berbasis&#13;
algoritma XGBoost. Hasil penelitian menunjukkan bahwa fitur entropi memiliki&#13;
pengaruh paling signifikan dalam mendeteksi anomali pada tautan pendek.&#13;
Performa model dievaluasi menggunakan Confusion Matrix, menghasilkan tingkat&#13;
akurasi dan stabilitas metrik yang optimal dalam mengidentifikasi ancaman siber&#13;
pada ekosistem platform X. Sistem deteksi ini kemudian diimplementasikan ke&#13;
dalam aplikasi purwarupa bernama "Unmask" sebagai solusi perlindungan&#13;
pengguna secara real-time.</mods:abstract><mods:classification authority="lcc">000 Ilmu komputer, informasi dan pekerjaan umum</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-02-03</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Universitas AMIKOM Yogyakarta;Fakultas Ilmu Komputer</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_31874"><mets:rightsMD ID="rights_eprint_31874_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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