@inproceedings{27da5644d6524e3780b13779947971dd,
title = "Ensemble Imputation Method for Forecasting Indonesia Sugar Dataset Using Machine Learning",
abstract = "Imputing missing values in the Indonesian sugar dataset is crucial to mitigate biased predictions. In this study, we employ KNN imputation with N equal to 5 to address this issue and achieve optimal results. Multiple machine learning regression models are utilized to estimate sugar imports effectively, and a comparison is made with the deep learning method to determine the best-performing approach. After handling missing values, our research demonstrates improved accuracy and minimized errors when implementing machine and deep learning methods. Through KNN imputation, missing values are effectively handled, and the LSTM model is utilized to estimate sugar imports accurately. The LSTM model achieves a Mean Squared Error (MSE) of 0.0303 and Mean Absolute Error (MAE) of 0.1376 on the testing dataset. Additionally, KNN imputation reaches the highest average percentage value of 94.734, indicating the closest similarity to the original data.",
keywords = "Deep learning, Forecasting, Machine Learning, Sugar Import",
author = "Filsafan, {Mas Syahdan} and Riyanarto Sarno and {Bernadetta Raras}, {I. R.} and Haryono, {Agus Tri}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 ; Conference date: 14-10-2023",
year = "2023",
doi = "10.1109/ICONNIC59854.2023.10467599",
language = "English",
series = "2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "13--17",
booktitle = "2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding",
address = "United States",
}