Pemetaan Potensi Lahan Jagung Menggunakan Citra Satelit Dan Random Forest Pada Cloud computing Google Earth Engine - Dwi Wahyu Triscowati; Widyo Pura Buana; Arif Handoyo Marsuhandi (Politeknik Statistika STIS, 2021)
The availability of information on the potential of corn fields that is quickly updated is important to support economic recovery after covid 19. Maize mapping is a challenge in agriculture because the corn planting area does not have special characteristics such as rice fields, corn does not have a standard area map, and planting can be done in rice fields and dry forest lands. Another problem is the need for high computational resources if the mapping of maize is done directly or manually identified. In this study, mapping the potential of maize in East Java in selected districts automatically using machine learning on cloud computing google earth engine. With the use of GEE cloud computing, maize mapping can be carried out in large areas without being constrained by computer capabilities. This study uses a pixel-based Random Forest (RF) machine learning algorithm with input data from the Landsat-8, Sentinel-1 and Sentinel-2 satellites. Reference data to train the classification model using maize ASF results. The best accuracy of Machine learning results comes from the combination of Landsat-8 and Sentinel-2 with an average accuracy of 0.79. The classification model was then applied to 10 districts where the best result was Banyuwangi Regency with an accuracy of 0.89. Judging from the potential area of ​​corn in the Banyuwangi area, the area ranges from 22,256.82 – 58,992.3 Ha based on pixels that are predicted to be corn at least 3 times/month. From the results of this study, it is evident that the use of cloud computing can perform calculations in 10 districts quickly, both in terms of model development and predictions. In addition, because it uses cloud computing, the use of satellite imagery can be utilized as soon as possible after the satellite image is published/released so that predictions of the potential of corn can be quickly and accurately generated. This study also highlights the shortcomings that occur, namely in terms of the number of samples for training data and the limitations of the algorithm used so that in the future it can be developed even better.