REDUKSI DIMENSI FITUR DENGAN METODE FEATURE SELECTION INFORMATION GAIN PADA KLASIFIKASI MODIFIED K NEAREST NEIGHBOR

SARI, ERINDAH NURAPRILLIANA and Primartha, Rifkie and Jambak, Muhammad Ihsan (2020) REDUKSI DIMENSI FITUR DENGAN METODE FEATURE SELECTION INFORMATION GAIN PADA KLASIFIKASI MODIFIED K NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

The Classification algorithm Modified k-Nearest Neighbor (MkNN) is a development of the kNN algorithm where MkNN can solve the outlier problem in ordinary KNN. MkNN has several disadvantages such as requiring large computation and memory costs in its application and not good to dealing high-dimensional data. From this arises the question, whether dimension reduction by feature selection using information gain has an effect that can overcome these weaknesses. Moreover, it is known that feature selection has a direct effect with reduced processing time for data mining algorithms, improves performance in classification and also results that are easier to understand. To determine the effect of this dimension reduction, the method will be tested on dataset LSVT Voice Rehabilitation. MkNN classification using dimensional reduction with Information Gain results in an average accuracy of 83.46%, the average time of 6 seconds and the average memory of 120130765 bytes while MkNN classification without dimensional reduction results in an average accuracy of 80.78%, the average time is 12.5 seconds and the average memory is 121313689 bytes.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Modified k-Nearest Neighbor, Information Gain, Klasifikasi, Seleksi Fitur, Reduksi Dimensi
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 9561 not found.
Date Deposited:	05 Jan 2021 07:01
Last Modified:	05 Jan 2021 07:01
URI:	http://repository.unsri.ac.id/id/eprint/39239

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