Skripsi

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

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Penilaian

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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%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007000915	T40201	T402012020	Central Library (REFERENSI)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T402012020

Publisher

Inderalaya : Fakultas Ilmu Komputer, Universitas Sriwijaya., 2020

Collation

xix, IV-2 hlm.; ilus., tab.: 28 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika
Ilmu Komputer
Algoritma Klasifikasi k-Nearest Neighbor

Specific Detail Info

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Statement of Responsibility

EM

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