<mets:mets OBJID="eprint_32892" 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="2026-09-28T20:52:14Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>EPrints Universitas Amikom Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_32892_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>SISTEM REKOMENDASI DESTINASI WISATA&#13;
MENGGUNAKAN ALGORITMA HYBRID FILTERING PADA&#13;
DAERAH ISTIMEWA YOGYAKARTA</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">Novebrian Setya</mods:namePart><mods:namePart type="family">Purwanto</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Lonjakan perjalanan wisata domestik di Indonesia, khususnya di Provinsi Daerah&#13;
Istimewa Yogyakarta (DIY), menuntut adanya sistem rekomendasi destinasi yang&#13;
mampu memberikan saran secara personal dan kontekstual. Penelitian ini&#13;
mengembangkan sistem rekomendasi destinasi wisata berbasis hybrid filtering&#13;
yang menggabungkan content-based filtering, collaborative filtering, serta variabel&#13;
cuaca real-time untuk meningkatkan relevansi rekomendasi. Data destinasi wisata&#13;
DIY diperoleh dari dataset publik Kaggle, sementara data cuaca diperoleh melalui&#13;
Application Programming Interface (API) Open-Meteo. Kemiripan konten dihitung&#13;
menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF)&#13;
dan cosine similarity, sedangkan kesamaan antar pengguna dianalisis menggunakan&#13;
algoritma k-nearest neighbors (k-NN). Sistem diimplementasikan menggunakan&#13;
basis data MYSQL dan antarmuka web interaktif berbasis PHP. Hasil pengujian&#13;
menunjukkan bahwa pendekatan hybrid filtering menghasilkan nilai Mean&#13;
Absolute Error (MAE) sebesar 0,1366 dan Root Mean Square Error (RMSE)&#13;
sebesar 0,1638, yang berarti lebih akurat dibandingkan pendekatan tunggal. Sistem&#13;
ini juga mampu menyesuaikan rekomendasi terhadap kondisi cuaca aktual, seperti&#13;
memprioritaskan destinasi indoor saat hujan. Integrasi komponen content-based&#13;
dan collaborative filtering terbukti efektif dalam mengatasi permasalahan cold-start&#13;
serta meningkatkan kepuasan pengguna dalam memilih destinasi wisata di DIY.</mods:abstract><mods:classification authority="lcc">005 Pemrograman komputer, program dan data</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-02-23</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_32892"><mets:rightsMD ID="rights_eprint_32892_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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