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primary topic: RECOMMENDER SYSTEMS USING HYBRID DEMOGRAPHIC AND CONTENT-BASED FILTERING METHODS FOR UMKM PRODUCTS

RECOMMENDER SYSTEMS USING HYBRID DEMOGRAPHIC AND CONTENT-BASED FILTERING METHODS FOR UMKM PRODUCTS
type: Thesis
type: Article
abstract: RECOMMENDER SYSTEMS USING HYBRID DEMOGRAPHIC AND CONTENT-BASED FILTERING METHODS FOR UMKM PRODUCTS Salsa Nadira Putri1), Tjut Awaliyah Zuraiyah2), Dinar Munggaran Akhmad3) 1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan University, Bogor, West Java, 16143, Indonesia Abstract Marketing digitization such as e-commerce is needed by micro, small and medium enterprises (UMKM) in Bogor City and Regency so that the products are more easily accessible to consumers. One of the digital marketing that is commonly used by consumers is an e-commerce website. The Recommendation System is implemented into e-commerce websites to increase consumer convenience in online shopping. The recommendation systems method applied is Demographic Filtering and Content-based Filtering. Demographic Filtering uses IMDB Weighted Rating calculations which generate recommendations globally and give recommendations based on each product's IMDB Weighted score. Content-based Filtering uses Cosine Distance calculations which generate personal recommendations and give recommendations based on the score cosine distance of each product in the form of a presentation of the similarity of products that have been purchased with other products. This research uses 107 UMKM fashion and craft product data that was obtained from Bogor City Regional Craft Center which sells various kinds of UMKM products from Bogor City and Regency. Data preprocessing is then carried out on the raw data, with the Data Cleaning, Data Transforming and Data Splitting stages which divide the data in a ratio of 80:20. The accuracy of Demographic Filtering Recommendation System reaches 82.7% and Content-based Filtering Recommendation System reaches 100%. Keywords: UMKM; Recommender System; Demographic Filtering; Content-based Filtering
date: 2024-07-10
status: published
author list: authors
type: EPrint
type: ThesisEPrint
is part of: repository
has document: RECOMMENDER SYSTEMS USING HYBRID DEMOGRAPHIC AND CONTENT-BASED FILTERING METHODS FOR UMKM PRODUCTS (Text)
subject: Ilmu Komputer

RECOMMENDER SYSTEMS USING HYBRID DEMOGRAPHIC AND CONTENT-BASED FILTERING METHODS FOR UMKM PRODUCTS (Text)
type: Document
has file: Laporan - Salsa Nadira Putri - 065118203.pdf
has part: Laporan - Salsa Nadira Putri - 065118203.pdf

Universitas Pakuan
type: Organization

Fakultas Matematika dan Pengetahuan Alam, Universitas Pakuan
type: Organization

Dinar Dinar Munggaran Ahmad
type: Person
given name: Dinar
family name: Dinar Munggaran Ahmad

Tjut Awaliyah Zuraiyah
type: Person
given name: Tjut
family name: Awaliyah Zuraiyah

Salsa Nadira Putri
type: Person
given name: Salsa
family name: Nadira Putri

Fakultas Matematika dan Ilmu Pnegetahuan Alam
type: Organization

Program Studi Ilmu Komputer
type: Organization

HTML Summary of #8899 RECOMMENDER SYSTEMS USING HYBRID DEMOGRAPHIC AND CONTENT-BASED FILTERING METHODS FOR UMKM PRODUCTS
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primary topic: RECOMMENDER SYSTEMS USING HYBRID DEMOGRAPHIC AND CONTENT-BASED FILTERING METHODS FOR UMKM PRODUCTS

Ilmu Komputer
type: Concept