Prasetio, Andreas (2024) Penerapan Algoritma K-Nearest Neighbor Untuk Pencarian Produk Berdasarkan Kategori Produk Pada Website PT.Shima Prima Utama. Undergraduate thesis, Universitas Katolik Musi Charitas.
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Abstract
Information has become a basic need for humans. Various ways are done to make it easier to get information such as the use of information technology systems that implement information retrieval, as a process of finding related information to meet user information needs. The website of PT Shima Prima Utama is one of the technology systems to convey PT Shima Prima Utama product information to its customers. But over time, the stock of products from various categories that are made becomes more and more with a variety of product information that makes it difficult for customers to find the products they are looking for. For this problem, a product search system was built on the website by classifying product descriptions into their categories using the K-Nearest Neighbor (KNN) algorithm. Based on statistical testing in the research that has been done, it is proven that the K-Nearest Neighbor algorithm is successfully applied in the product search system on the PT. Shima Prima Utama website and helps customers find the products they are looking for.
Item Type: | Thesis (Undergraduate) |
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Uncontrolled Keywords: | Information Technology System, Information Retrieval, Website, Description Classification, K-Nearest Neighbor |
Subjects: | T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
Divisions: | Book > Informatics Study Program |
Depositing User: | Users 2883 not found. |
Date Deposited: | 14 Mar 2024 04:09 |
Last Modified: | 11 Jun 2024 11:19 |
URI: | http://eprints.ukmc.ac.id/id/eprint/11880 |
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