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K-nearest neighbor
Open accessAbstract
Classification is a systematic grouping of objects into certain groups based on the same characteristics. The classification method used in this research are naive Bayes and K-Nearest Neighbor which has a relatively high degree of accuracy. This research aims to compare the level of classification accuracy on the status data of value-added tax (VAT) payment. The data used is data on corporate taxpayers at Samarinda Ulu Tax Office in 2018 with the status of VAT payment being compliant or non-compliant and used 3 independent variables are income, type of business entity and tax reported status. Measurement of accuracy using APER in the Naive Bayes method is 17.07% and in K-Nearest Neighbor method is 19,51%. The comparison results of accuracy measurements between the two methods show that the naive Bayes method has a higher level of accuracy than the K-Nearest Neighbor method
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Cited by
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Explainable artificial intelligence: an analytical review
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Metadata
- Authors
- Peterson, L.
- Venue
- Scholarpedia
- Publisher
- Scholarpedia
- Published
- 2009
- Volume
- 4
- Issue
- 2
- Pages
- 1883
- ISSN
- 1941-6016 (ISSN-L)
- OpenAlex
- W2088252378
- Cited by
- 2,433
- References
- 0
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- diamond
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