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All Journal Jurnal Ilmiah Inovasi Jurnal Teknologi Informasi dan Terapan (J-TIT) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Taufiq Rizaldi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Jurnal Teknologi Informasi dan Terapan (J-TIT)

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PERBANDINGAN METODE K-NN DAN BAYES PADA MISSING IMPUTATION Taufiq Rizaldi; Fendik Eko Purnomo; Aji Seto Arifianto
Jurnal Teknologi Informasi dan Terapan Vol 5 No 2 (2018)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v5i2.84

Abstract

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%.
Co-Authors Aji Seto Arifianto Dwi Putro Sarwo Setyohadi Faisal Lutfi Afriansyah Fendik Eko Purnomo Hermawan Arief Putranto