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Rice plants (Oryza Sativa L.) are the main staple food commodity in Indonesia, as most of the Indonesian population relies on rice as their primary food. One of the causes of low rice production in Indonesia is that farmers generally cultivate rice improperly, such as in land preparation or land selection. Land suitability in rice cultivation greatly affects crop productivity. A process that can support decisions regarding rice land suitability is the development of a Decision Support System (DSS) website using a combination of the Simple Additive Weighting (SAW) method and the Technique for Order Performance of Similarity to Ideal Solution (TOPSIS). This combination is performed by taking the average (µ) of the final results from the SAW and TOPSIS methods. The final scores of each method are calculated separately, and then the average (µ) of these two results is taken to obtain the final ranking of the alternatives. The data used to determine the suitability of rice land is based on five criteria: soil type, soil pH, rainfall, temperature, irrigation and water supply. The alternative data used in the study includes six alternatives: Sungai Kunjang, Sambutan, Samarinda Utara, Palaran, Loa Janan Ilir, and Samarinda Seberang. The aim of this research is to provide information on alternative solutions to farmers or farmer groups in determining rice land suitability. The results of the combination of the SAW and TOPSIS methods show that the alternative with the highest final score is Samarinda Utara (A3), with a final score of 0.7337. Meanwhile, the alternative with the lowest final score is Sambutan (A2), with a final score of 0.4402.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.