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IMPLEMENTASI CONTEXT-AWARE RECOMMENDER SYSTEM (CARS) DENGAN METODE BEST-WORST UNTUK OPTIMASI PROMOSI BISNIS

ROMADHON, Zainur and Sediyono, Eko and Widodo, Catur Edi (2020) IMPLEMENTASI CONTEXT-AWARE RECOMMENDER SYSTEM (CARS) DENGAN METODE BEST-WORST UNTUK OPTIMASI PROMOSI BISNIS. Masters thesis, School of Postgraduate Studies.

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Abstract

Context-Aware Recommender System (CARS) merupakan sebuah sistem yang dapat memberikan saran atau rekomendasi informasi yang sesuai dengan kebutuhan dan minat pengguna secara real time berdasarkan informasi kontekstual pengguna. Penelitian ini bertujuan untuk membangun sebuah sistem informasi untuk memberikan sebuah rekomendasi yang lebih relevan kepada pelanggan atau pengguna dengan tujuan untuk optimalisasi promosi bisnis. Pendekatan yang digunakan dalam pengembangan sistem rekomendasi yaitu pendekatan kontekstual (contextual-aware) dan metode best-worst. Data yang digunakan untuk memberikan rekomendasi meliputi data menu fast food, data restoran, data rating menu pelanggan, data kriteria, data rating menu pengguna dan data kontekstual pengguna. Model yang digunakan dalam pengembangan sistem rekomendasi adalah model waterfall, model ini melakukan pendekatan secara sistematis dan berurutan. Hasil dari penelitian ini berupa sistem yang dapat menentukan prediksi rating dan memberikan ranking rekomendasi berdasarkan informasi kontektual masing-masing pengguna.
Kata kunci : Context-Aware Recommender System (CARS), sistem rekomendasi, metode best-worst, waterfall.

Context-Aware Recommender System (CARS) is a system that can provide suggestions or recommendations of information according to user needs and interests in real time based on user contextual information. This study aims to build an information system to provide a recommendation that is more relevant to customers or users in order to optimize business promotions. The approach used in the recommendation system development is the contextual approach (contextual-aware) and the best-worst method. The data used to provide recommendations includes fast food menu data, restaurant data, customer menu rating data, criteria data, user menu rating data and user contextual data. The model used in the development of the recommendation system is the waterfall model, this model takes a systematic and sequential approach. The results of this study are a system that can determine rating predictions and provide ranking recommendations based on the contextual information of each user. Keywords: Context-Aware Recommender System (CARS), recommendation system, best-worst method, waterfall.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Context-Aware Recommender System (CARS), sistem rekomendasi, metode best-worst, waterfall
Subjects: Sciences and Mathemathic
Divisions: Postgraduate Program > Master Program in Information System
Depositing User: ekana listianawati
Date Deposited: 28 Apr 2022 03:52
Last Modified: 28 Apr 2022 03:52
URI: https://eprints2.undip.ac.id/id/eprint/6089

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