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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
M. Elrico Afdila
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Computer Science & IT Engineering

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Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System Sri Lestari; M. Elrico Afdila; Yan Aditiya Pratama
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5300

Abstract

A recommendation system is a system that provides suggestions or recommendations to a product or service for its users. One of the problems encountered in the recommendation system is sparsity, namely the lack of available data for analysis, resulting in poor performance of the recommendation system because it cannot provide the proper recommendations. On this basis, this study proposes the mean method and the stochastic hot-deck method to calculate missing values to improve the quality of the recommendations. The experimental results show that the hot-deck imputation method gives better results than the mean imputation method with smaller RMSE and MAE values, namely 2,706 and 2,691.
Co-Authors SRI LESTARI Yan Aditiya Pratama