Similarity-AF-Single Imputation Using Statistics-Based and K Nearest Neighbor Methods for Numerical Datasets

Fadlil, Abdul and Herman, Herman and Praseptian M, Dikky Similarity-AF-Single Imputation Using Statistics-Based and K Nearest Neighbor Methods for Numerical Datasets. Ingénierie des Systèmes d’Information, 28 (2). pp. 451-459. ISSN 1633-1311 (Print); 2116-7125 (Online)

Text Similarity-AF-Single Imputation Using Statistics-Based and K Nearest Neighbor Methods for Numerical Datasets.pdf Download (2MB)

Abstract

Handling missing values is often an unavoidable problem. Imputation is a preferred option in handling missing values compared to removing all row records which will reduce the
number of datasets and can lead to poor research results if the size of the remaining data is too small. The problem that often occurs is that there are often wrong conclusions due to
some records that have missing values, therefore this study will test several simple imputation methods, namely statistical-based imputation and kNNI. The results of testing
the error value with RMSE and MAPE show that kNNI imputation results are much better than statistical-based imputation. Based on the standard used in the MAPE test, the kNNI test results (error values) are almost entirely very good because the error value is <10% except for three test results in dataset 1 at k=10, k=15 and k=20, while the statistical-based
imputation results are only good because the error value is between 10% and 20%, even one of the results exceeds 20% Although kNNI is better than statistical-based imputation, it is
necessary to choose the right k value to get the best imputation results.

Item Type:	Artikel Umum
Subjects:	T Technology > T Technology (General)
Divisi / Prodi:	Master (Magister) > Master of Technology Informatica (Magister Teknologi Informatika)
Depositing User:	Drs. Abdul Fadlil, M.T., Ph.D.
Date Deposited:	15 Jul 2023 01:29
Last Modified:	15 Jul 2023 01:29
URI:	http://eprints.uad.ac.id/id/eprint/43602

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