## 5 Conclusions  
The work presented in this paper focuses on predicting deforestation related to oil palm, an issue that has not been explored in existing research studies. The work proposes an approach that aims to enhance the prediction accuracy of deforestation related to oil palm. The approach consists of a framework for model training and a set of criteria: area,  
Figure 11: Average values of features versus the probability of being labeled as oil palm.  
distance to oil palm, distance to mills, distance to roads, distance to water, distance to oil palm concessions, related alerts, number of patches, and distance between patches.  
The model is implemented using data about forest and oil palm plantations in the Aceh province of Indonesia, which faces one of the highest deforestations in the world.  
The model implementation showed an acceptable accuracy of 0.82. It also indicates an F1 score of 0.69, a precision of 0.72, and a recall of 0.66 for deforestation related to oil palm and an F1 score of 0.97, a precision of 0.97, and a recall of 0.98 for deforestation related to other drivers.  
The most important criterion is the area; then comes the proximity to oil palm plantations, the distance to water, and the distance to roads. The distance to water criterion is also significant and could help to make a distinction between oil palm and other crops.  
We should note that this set of criteria assessments does not aim to be complete, but rather to allow us to predict deforestation related to palm oil. Future implementations should include other criteria, such as plantations for other crops, logging, and urbanization.  
The proposed approach can support decision makers to define strategies concerning deforestation related to palm oil and the implementation of a sustainable forest. Providing information about deforestation prediction can help authorities to make appropriate decisions about where and when they can establish new plantations to ensure a sustainable oil palm. Additionally, based on the deforestation prediction, the companies of palm oil production can quickly determine which parts of their supply chain are not in line with their principles with regard to the sustainability of forests.  
Although the model shows an acceptable accuracy for predicting deforestation related to oil palm, there are still some limitations.  
* First, relatively large clustering periods (e.g., 14, 45, or 140 days) may affect the performance of the model as it may lead to considering several criteria at the same time.
* Second, larger datasets are needed to accurately train the model and more clearly distinguish oil palm from other plantations.  
Further research using larger datasets will result in better prediction of deforestation related to oil palm. In addition, for future work, it is highly recommended to reduce the clustering period in order to avoid considering several criteria at the same time.  
Conceptualization, T.S.; methodology, T.S. and S.S.; software, A.L. and T.S.; validation, T.S., S.S. and A.L.; formal analysis, T.S. and S.S.; investigation, T.S. and A.L.; resources, T.S. and A.L.; data curation, A.L.; writing--original draft preparation, T.S., S.S. and A.L.; writing--review and editing, T.S. and S.S.; visualization, A.L., T.S. and S.S.; supervision, T.S.; project administration, T.S. and S.S.; funding acquisition, n/a. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Many thanks to the stuff of Satelligence who helped with of this work. Big thanks to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.