Model Tree with Modified L1 Loss Function for Predicting Missing Attendance Data of Faculties
Mohammad Arif Rasyidi, Rachmadita Andreswari
Mohammad Arif Rasyidi
Available Online March 2019.
- https://doi.org/10.2991/icoiese-18.2019.10How to use a DOI?
- model tree; loss function; prediction; attendance
- 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.
- 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 -