Skripsi

SISTEM REKOMENDASI MENGGUNAKAN METODE COLLABORATIVE FILTERING DAN ALGORITMA APRIORI

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Penilaian

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The recommendation system is a system that is able to manage information from historical data and provide suggestions or recommendations to users. This research uses Collaborative Filtering and Apriori methods to produce product recommendations. Collaborative Filtering performs the process of calculating values similarity product based on user behavior in providing product ratings. Apriori performs data processing and produces data patterns that are used as reference recommendations based on the selected product andresults similarity using Collaborative Filtering. Collaborative Filtering and Apriori are implemented as applications in providing product recommendations to users. Tests were carried out on 39 products with varying rating values and product shopping patterns from 247 users by comparing the results of the recommendations given by the system with transaction data patterns that had occurred previously. Results Testing data using method Collaborative Filtering produces an accuracy value of 73.2% and data testing using Collaborative Filtering and Apriori Method Produces an accuracy value of 100%. The accuracy value using Collaborative Filtering and Apriormethodsiis higher than using methods Collaborative Filtering. It is concluded that the use of theAlgorithm Apriori and Collaborative Filtering is good in the recommendation system.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002550	T54754	T547542021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T547542021

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

xvi, 71 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Analisis - Komputer
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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