<mets:mets OBJID="eprint_31900" 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="2026-07-05T22:22:02Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_31900_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>OPTIMASI DAN DETEKSI INTRUSI MENGGUNAKAN &#13;
HYBRID FEATURE SELECTION DAN DECISION TREE&#13;
RULE-BASED PADA DATA NSLKDD</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Riki Nur Indra</mods:namePart><mods:namePart type="family">Putra</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Keamanan jaringan komputer menghadapi tantangan besar akibat&#13;
meningkatnya volume lalu lintas data dan kecanggihan serangan siber. Penggunaan&#13;
Intrusion Detection System (IDS) berbasis Machine Learning menjadi solusi vital,&#13;
namun sering terkendala oleh dimensi data yang tinggi (seperti 41 fitur pada dataset&#13;
NSL-KDD) yang membebani komputasi, serta sifat black-box dari model AI yang&#13;
sulit divalidasi logikanya oleh manusia. Masalah ini menyebabkan ketidakefisienan&#13;
sistem dan keraguan administrator terhadap keputusan blokir yang diambil oleh&#13;
IDS. Penelitian ini bertujuan untuk mengoptimalkan kinerja IDS sekaligus&#13;
memberikan transparansi keputusan. Metode yang digunakan adalah Hybrid&#13;
Feature Selection yang menggabungkan kecepatan Random Forest (Filter) dan&#13;
ketelitian Recursive Feature Elimination with Cross-Validation (Wrapper) untuk&#13;
menyeleksi fitur terbaik. Selanjutnya, algoritma Decision Tree digunakan untuk&#13;
mengekstraksi aturan logika (Rule Extraction) dari setiap serangan. Hasil penelitian&#13;
menunjukkan bahwa sistem mampu mereduksi 41 fitur menjadi 23 fitur optimal&#13;
dengan mempertahankan akurasi validasi sebesar 99,73%. Sistem juga berhasil&#13;
menghasilkan "kamus aturan" yang transparan untuk 40 jenis serangan berbeda,&#13;
sehingga dapat dimanfaatkan oleh praktisi keamanan jaringan untuk memvalidasi&#13;
insiden keamanan secara cepat dan akurat.</mods:abstract><mods:classification authority="lcc">000 Ilmu komputer, informasi dan pekerjaan umum</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-01-28</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_31900"><mets:rightsMD ID="rights_eprint_31900_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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