Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

Model Tree with Modified L1 Loss Function for Predicting Missing Attendance Data of Faculties

Authors
Mohammad Arif Rasyidi, Rachmadita Andreswari
Corresponding Author
Mohammad Arif Rasyidi
Available Online March 2019.
DOI
https://doi.org/10.2991/icoiese-18.2019.10How to use a DOI?
Keywords
model tree; loss function; prediction; attendance
Abstract
The problem of missing attendance data in our university often arises due to the negligence of faculties. In this study, we address the problem by directly predicting the work duration of faculties. The nature of the problem require us to not only make accurate predictions, but also minimize the rate of overestimation. To address the problem, we propose the implementation of model tree with modified L1 loss function and simple prediction result reduction. Experimental results show that our proposed method is able to lower the overestimation rate while maintaining accuracy within acceptable range.
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Proceedings
2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
Part of series
Atlantis Highlights in Engineering
Publication Date
March 2019
ISBN
978-94-6252-689-1
ISSN
2589-4943
DOI
https://doi.org/10.2991/icoiese-18.2019.10How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Mohammad Arif Rasyidi
AU  - Rachmadita Andreswari
PY  - 2019/03
DA  - 2019/03
TI  - Model Tree with Modified L1 Loss Function for Predicting Missing Attendance Data of Faculties
BT  - 2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
PB  - Atlantis Press
SN  - 2589-4943
UR  - https://doi.org/10.2991/icoiese-18.2019.10
DO  - https://doi.org/10.2991/icoiese-18.2019.10
ID  - Rasyidi2019/03
ER  -