| Abstrak/Abstract |
It is known that the studies of peatlands fire occurrences in Indonesia are less studied before. In our previous
study, the prediction of the peatlands fire occurrence was modeled using various machine learning classification
approaches. It is found that using South Kalimantan Province data, in the empirical study we previously found that the
datasets are unbalanced, i.e., the occurrence and the nonoccurrence of fire hotspots areas. In the study presented in this
paper, to improve the classification performance, we consider Adaptive Neighbor Synthetic Majority Oversampling
Technique (ANS-SMOTE) approach to balance the data. Using the considered empirical data, we found that this method
did not always gives improvement in the classification results. |
