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Paramudita, Ayu
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Pemanfaatan Machine Learning untuk Imputasi Data Lusmana, Putri Hera; Paramudita, Ayu
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2032

Abstract

The challenge that is often faced in large-scale company-based surveys is non-response. Non-response is one of the causes of missing data. One way that can be used to deal with missing data is to perform data imputation. So far, data imputation in the Annual Survey of Manufacturing Industries (STPIM) has been carried out by using a combination of various approaches, including historical, auxiliary unit, and clerical imputation. These methods, however, tend to be inefficient and are unable to measure the outcomes’ accuracy. In light of these limitations, we aim to introduce an innovative approach in data imputation processing by utilizing machine learning. By comparing various machine learning methods, we obtained the results that K-nearest neighbors imputation is the best method in terms of accuracy in imputing output data in STPIM 2021. Meanwhile, in terms of computing performance, linear Support Vector Machine (SVM) gives the most efficient processing time.
Co-Authors Lusmana, Putri Hera