PERBANDINGAN METODE PENGUKURAN JARAK PADA ALGORITMA K-NEAREST NEIGHBOR DENGAN DATASET TITANIC

DWINANDA, RIZQI SEPTIAN and Rini, Dian Palupi and Miraswan, Kanda Januar (2020) PERBANDINGAN METODE PENGUKURAN JARAK PADA ALGORITMA K-NEAREST NEIGHBOR DENGAN DATASET TITANIC. Undergraduate thesis, Sriwijaya University.

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Abstract

K-Nearest Neighbor algorithm is a classification algorithm that can be used to classify a data with good result. one of them is to classify the titanic dataset. The quality of the classification result of the k - Nearest Neighbor is very dependent on the distance between object and value of k specified, so the selection of method for distance measurement determines the result of classification.in this research a comparison of several methods of measuring distances, including Manhattan distance, Euclidean distance and Chebyshev distance were examined to see distance measurement methods that can be used optimally on the k - Nearest Neighbor algorithm with the predefined titanic dataset. This study produces a classification value with the highest accuracy in the Chebyshev distance method with an average accuracy of 58.89%. Meanwhile, for the measurement of the distance, the Manhattan distance with an average value of 54.60% and the Euclidean distance with an average value of 52.95%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Classification, Distance measurement, Manhattan Distance, Euclidean Distance, Chebyshev Distance.
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 9920 not found.
Date Deposited:	20 Jan 2021 06:07
Last Modified:	20 Jan 2021 06:07
URI:	http://repository.unsri.ac.id/id/eprint/40201

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