KLASTERISASI ONLINE SHOP BERDASARKAN CAPTION DENGAN ALGORITMA JARO WINKLER DISTANCE DAN K-NEAREST NEIGHBOR

JUSADI, HIKMAH and Abdiansah, Abdiansah and Yusliani, Novi (2020) KLASTERISASI ONLINE SHOP BERDASARKAN CAPTION DENGAN ALGORITMA JARO WINKLER DISTANCE DAN K-NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

Captions on uploaded photos and videos on the online shop account in Instagram are not equipped with category filters, so the customers are often confused about determining the online shop category based on the caption. In this study the Jaro Winkler Distance and K-Nearest Neighbor algorithms were used to cluster 10 captions data from three different categories. Before the clustering process, pre-processing of text was carried out, namely case folding, tokenizing, and stopword removal which aimed to restore the standard form of text documents, break the document into words, and remove common words that has a high frequency of appearance. Jaro Winkler Distance is used to calculate the string similarity value between test data and training data and K-Nearest Neighbor is used for the clustering process based on predetermined K values, namely 3, 53,103, 153, 203, 249, and 449. The K value with the highest accuracy result is K 249. Based on the analysis conducted from seven tests of 450 training data and 10 test data, the results obtained Precision 0.419, Recall 0.567, F-Measure 0.398, and 66% accuracy value.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Clustering, Caption, Instagram, Jaro Winkler Distance, K-Nearest Neighbor
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 9933 not found.
Date Deposited:	15 Jan 2021 04:57
Last Modified:	15 Jan 2021 04:57
URI:	http://repository.unsri.ac.id/id/eprint/40228

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