Predicting Coal Quality Using Decision Tree Algorithm

Reza Faishal; Kiagus Arsyad; Ariana Yunita

Proceedings of the International Conference on Sustainable Engineering, Infrastructure and Development, ICO-SEID 2022, 23-24 November 2022, Jakarta, Indonesia

Research Article

Predicting Coal Quality Using Decision Tree Algorithm

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@INPROCEEDINGS{10.4108/eai.23-11-2022.2338880,
    author={Reza Naquib Faishal and Kiagus Muhammad Arsyad and Ariana  Yunita},
    title={Predicting Coal Quality Using Decision Tree Algorithm},
    proceedings={Proceedings of the International Conference on Sustainable Engineering, Infrastructure and Development, ICO-SEID 2022, 23-24 November 2022, Jakarta, Indonesia},
    publisher={EAI},
    proceedings_a={ICO-SEID},
    year={2023},
    month={12},
    keywords={coal classification decision tree machine learning prediction},
    doi={10.4108/eai.23-11-2022.2338880}
}

Reza Naquib Faishal
Kiagus Muhammad Arsyad
Ariana Yunita
Year: 2023
Predicting Coal Quality Using Decision Tree Algorithm
ICO-SEID
EAI
DOI: 10.4108/eai.23-11-2022.2338880

Reza Naquib Faishal¹, Kiagus Muhammad Arsyad², Ariana Yunita²^,*

1: Department of Geophysical Engineering, Universitas Pertamina, Jakarta, Indonesia
2: Department of Computer Science, Universitas Pertamina, Jakarta, Indonesia

*Contact email: ariana.yunita@universitaspertamina.ac.id

Abstract

Predictions are often criticized for the lack of interpretability, which is often in many real-world applications. High-quality coal is required to meet industrial demands, increase national energy security, and export. This research aims to show that the Decision Tree algorithm can classify coal quality based on volatile matter, fixed carbon, and heating values. The dataset used in this study is synthetic data generated based on the ASTM (America Society for Testing and Materials) rankings. The model's accuracy for predicting coal quality is 96 percent, and the tree has a depth of 5. This study demonstrates how decision tree algorithms produce reasonable predictions.

Keywords: coal classification decision tree machine learning prediction

Published: 2023-12-29
Publisher: EAI

: http://dx.doi.org/10.4108/eai.23-11-2022.2338880