@inproceedings{3f03563997914c7794de5d90bd33b54a,
title = "Handling Missing Value and Outlier to Improve Model Performance for Predicting Logistic Case",
abstract = "The world of logistics is a world that urgently needs speed and accuracy in operational calculations. In order to support these operational needs, the logistics industry needs to keep up with the times by utilizing machine learning in its business processes. However, problems when utilizing machine learning in the industrial world include incomplete data and abnormal or outlier data caused by certain factors. This researcher will focus on improving machine learning models using KNN-Imputer to overcome incomplete data or missing values and Local Factor Outlier to overcome abnormal data. The results of the study show that when the extraction feature is utilized, the accuracy level of machine learning increases by 34.86% and decreases error by MAE 97.47%, MSE 99.72%, and RMSE 96.75%.",
keywords = "KNN-Imputer, Local Factor Outlier, Logistics, Machine Learning",
author = "Afif Amirullah and Riyanarto Sarno and Sungkono, {Kelly Rossa} and Haryono, {Agus Tri} and Septiyanto, {Abdullah Faqih} and Sholiq",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024 ; Conference date: 19-12-2024",
year = "2024",
doi = "10.1109/BTS-I2C63534.2024.10942018",
language = "English",
series = "2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "77--82",
editor = "Wibowo, {Ferry Wahyu}",
booktitle = "2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024",
address = "United States",
}