Skripsi

PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN DECISION TREE UNTUK KLASIFIKASI KUALITAS UDARA BERDASARKAN BOOTSTRAP SAMPLING

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Penilaian

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Air can change based on factors that affect it and have an impact on air quality. Air quality plays an important role for organisms on the surface of the earth, especially for humans. Air quality must be clean so as not to have harmful impacts. Classification is a way to determine the level of air quality. Classification testing in this study uses two different methods, namely, K-Nearest Neighbor (KNN) and Decision Tree based on air quality data that has gone through a Bootstrap Sampling process. Therefore, the author tried to conduct research by comparing KNN and Decision Tree methods. The final accuracy of the study was measured by accuracy, precision, recall, and Fscore. The accuracy produced by the KNN method is 97%, 97.03%, 84.25%, and 89.60% respectively The accuracy produced by the Decision Tree method is 95.18%, 89.39%, 80.72%, and 84.45% respectively so that the KNN method shows better accuracy results than the Decision Tree method.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002177	T107128	T1071282023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1071282023

Publisher

Indralaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Sriwijaya., 2023

Collation

xiii, 67 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.285 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)

Jurusan Matematika
Pengolahan dan Analisa Data di Bidang Matematika

Specific Detail Info

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Statement of Responsibility

PITRIA

Other version/related

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