Rizaldi, Taufiq and Eko Purnomo, Fendik and Seto Arifianto, Aji (2018) PERBANDINGAN METODE K-NN DAN BAYES PADA MISSING IMPUTATION. Jurnal Teknologi Informasi dan Terapan (J-TIT), 5 (2). pp. 85-89. ISSN 2580-2291

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The problem of data loss in a dataset is experienced in surveys for data collection which are usually caused by no response from units or items during the survey data collection process. The loss of a data can significantly influence the results of a study. The inaccuracy in choosing a solution to overcome these problems can result in a less than optimal outcome that tends to be biased. Some methods that are widely used to solve these problems are using the K Nearest Neighbor (K-NN) and Naïve Bayes methods, the main purpose of this study is to compare the performance of the two methods. From the results of the K-NN, the results were better, where the Mean Square Error (MSE) is bigger than 1 and MAPE around 10-16%, while Naïve Bayes got MSE values bigger than 1 and MAPE ​​around 26%.

Item Type: Article
Subjects: 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 458 - Teknik Informatika
Divisions: Arsip Khusus
Depositing User: Taufiq Rizaldi
Date Deposited: 12 Sep 2021 14:41
Last Modified: 12 Sep 2021 14:44

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