ALGORITMA ITEM BASED COLLABORATIVE FILTERING

Rouf, Abdur (2022) ALGORITMA ITEM BASED COLLABORATIVE FILTERING. S1 thesis, Universitas Mercu Buana Jakarta-Menteng.

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Abstract

Tiap daerah pasti mempunyai spot wisata atau kuliner yang biasanya hanya diketahui oleh warga setempat, tak terkecuali di Jakarta. padahal, hidden spot atau wisata kuliner ini dapat menjadi spot yang sangat impresive atau berpotensi jadi salah satu destinasi yang menyenangkan untuk dikunjungi oleh wisatawan dari luar jakarta. Namun karena terbatasnya informasi yang dimiliki oleh wisatawan, banyak hidden spot atau wisata kuliner yang mereka lewatkan. Penelitian ini berfokus dalam mengulas algoritma Metode Item Based Collaborative Filtering. Penggunaan item based collaborative filtering bertujuan untuk melakukan prediksi item yang akan direkomendasikan ke seorang pengguna berdasarkan preferensi pengguna sebelumnya dan opini dari pengguna lain yang mirip. Hasilnya menunjukan bahwa algoritma Item Based Collaborative Filtering cukup efisien digunakan untuk memberikan rekomendasi p suatu item karena mempunyai akurasi ketepatan hasil mencapai 76%. Kata kunci: penelitian, panduan, ilmu komputer, universitas mercu buana Each area must have a tourist or culinary spot that is usually only known by local residents, and Jakarta is no exception. In fact, this hidden spot or culinary tour can be a very impressive spot or has the potential to be a fun destination for tourists from outside Jakarta to visit. However, due to the limited information held by tourists, they miss many hidden spots or culinary tours. This research focuses on reviewing the Item Based Collaborative Filtering Method algorithm. The use of item based collaborative filtering aims to predict items that will be recommended to a user based on the preferences of previous users and opinions from other similar users. The results show that the Item Based Collaborative Filtering algorithm is efficient enough to be used to provide recommendations for an item because it has an accuracy of 76% of the results. Key words: research, guidance, computer science, universitas mercu buana

Item Type:	Thesis (S1)
NIM/NIDN Creators:	41517110077
Uncontrolled Keywords:	research, guidance, computer science, universitas mercu buana,penelitian, panduan, ilmu komputer, universitas mercu buana
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	Maulana Arif Hidayat
Date Deposited:	19 Apr 2022 07:32
Last Modified:	19 Apr 2022 07:32
URI:	http://repository.mercubuana.ac.id/id/eprint/60141

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