ANNISA, SITI and Rini, Dian Palupi and Abdiansah, Abdiansah (2024) SISTEM REKOMENDASI COLLABORATIVE FILTERING MENGGUNAKAN KOMBINASI METODE CLUSTERING DAN ASSOCIATION RULE MINING. Masters thesis, Sriwijaya University.
Text
RAMA_55101_09012682024011.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (15MB) | Request a copy |
|
Text
RAMA_55101_09012682024011_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy |
|
Text
RAMA_55101_09012682024011_0023027804_0001108401_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (2MB) |
|
Text
RAMA_55101_09012682024011_0023027804_0001108401_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (868kB) | Request a copy |
|
Text
RAMA_55101_09012682024011_0023027804_0001108401_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (779kB) | Request a copy |
|
Text
RAMA_55101_09012682024011_0023027804_0001108401_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (869kB) | Request a copy |
|
Text
RAMA_55101_09012682024011_0023027804_0001108401_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (170kB) | Request a copy |
|
Text
RAMA_55101_09012682024011_0023027804_0001108401_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (406kB) | Request a copy |
|
Text
RAMA_55101_09012682024011_0023027804_0001108401_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (12MB) | Request a copy |
Abstract
A recommendation system helps collect and analyze user data to generate personalized recommendations for users. A recommendation system for movies has been implemented, considering the vast number of available films and the difficulty users face in finding movies that match their interests. One popular recommendation method is Collaborative Filtering (CF). Although widely applied, CF still has issues. Basic CF uses overlapping user data in evaluating items to calculate user similarity. This study aims to build a collaborative filtering recommendation system using clustering techniques to group users with similar interests into the same clusters. The next step in CF application is to gather recommendation candidate items by finding users with a high level of similarity to the target user. Subsequently, user pattern analysis is carried out by applying association rule mining to predict hidden correlations based on frequently watched items and the ratings given to those movies. This study uses rating data and movie data from the Movielens website. The evaluation of the recommendation results is measured using precision, recall, and f-measure. The evaluation results show that the proposed recommendation system achieves a hit rate of 95.08%, a precision of 81.49%, a recall of 98.06%, and an f-measure of 87.66%. Keywords: Recommendation System, Clustering, Collaborative Filtering, Association Rule Mining
Item Type: | Thesis (Masters) |
---|---|
Uncontrolled Keywords: | Sistem Rekomendasi, Collaborative Filtering, Clustering, Association Rule Mining |
Subjects: | Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.6.A2-Z General works Simulation Cf. QA76.9.C65 Computer science Cf. TA343 Engineering mathematics |
Divisions: | 09-Faculty of Computer Science > 55101-Informatics (S2) |
Depositing User: | Siti Annisa |
Date Deposited: | 24 Sep 2024 04:00 |
Last Modified: | 24 Sep 2024 04:00 |
URI: | http://repository.unsri.ac.id/id/eprint/157947 |
Actions (login required)
View Item |