###### Abstract
Another study used the Decision Tree Classifier (DTC) to differentiate surface types based on light reflection [21; 22], a condition that is difficult to meet because flooding occurs with cloudy skies. A seasonal disaster mapping system based on field observations is practical for generating initial data [23; 24] but impractical for periodic events. Research on radar satellites for disaster management to date still has elevation resolution in the range of 2 m [6; 7; 25; 26]. For Jakarta, especially in the city center to the northern coast, which often experiences floods, this is not appropriate because the flood depth is less than 2 m [27]. To map floods, previous methods were initially dominated by thresholding [6; 11; 13; 14; 28], Probability Density Function (PDF) [9; 10], and more recently, Logistic Regression [29] and the Storm Water Management Model (SWMM) [30]. More specifically, in remote sensing image segmentation applications, some researchers use the Normalized Difference Vegetation Index (NDVI) to classify vegetation, classify water levels with the Normalized Difference Water Index (NDWI), and the classification of floods using Normalized Difference Flood Index (NDFI) [31; 32]. Other researchers detected changes in land surface with radar images using the principle of interferometry to find coherence between images [15; 16; 33]. Research in the last three years has led to the use of Machine Learning (ML) in order to segment and classify increasingly complex datasets, such as the Adaptive Neuro-fuzzy Inference System (ANFIS) [34], Support Vector Machine (SVM) [35; 36], Convolutional Neural Network (CNN) [37; 38; 36], and more recently with various Swarm Intelligence (SI) variants such as Particle Swarm Optimization (PSO) [39; 40]. [PERSON] and [PERSON] used a Residual Neural Network to detect the potential for landslides, where this method is intended to detect changes in soil texture as the initiation of landslides [38]. The CNN model with this residual block is used to process LiDAR data. The neurons on CNN are activated using the RectifiedLinear Unit (ReLU). This study proposes three-dimensional CNN in mapping flood areas to filter and weigh neurons and map the potential flood areas in urban areas with better accuracy and fewer number of images. [PERSON] et al. proposed an Active Self Learning method on CNN to detect floods in urban areas from the SAR image ensemble [37]. The dataset used is four TerraSAR-X images of HH polarization with the composition of one pre-event image, one co-event image, and two post-event images. [PERSON] et al. proposed a high-resolution urban flood mapping method (Super-Resolution Mapping of Urban Flood, SMUF) with the fusion of the Support Vector Machine and General Regression Neural Network (FSVM-GRNN) [35]. Because the urban flood area in the observation area is not very dense, the accuracy of this FSVM-GRNN is 80.2%. [PERSON] et al. proposed a machine learning process to make corrections to the mapping of flood inundation areas in near-real-time (NRT) using SAR, where the observation area is an open area without many obstacles on the surface [41]. At the time of segmentation, there are difficulties in classifying areas that are flooded with areas that have surface reflection properties similar to those of the water surface. ML is performed to correct speckle noise and another scattering, which can interfere with data reading and classification. The filtering method is used in most SAR image processing but its effect is to reduce the effective resolution and change the signal statistics and cannot completely remove noise. To overcome this, [PERSON] et al. used the Logistic Binary Classifier (LBC) in a correction step to practice detecting the presence of water in the pixels contained in the water bodies and the surrounding buffer areas. The objective of this work is to investigate the mapping of flood potential in Jakarta and nearby coastal areas using three-dimensional CNN on co-polarized (VV) and cross-polarized (VH) Sentinel-1a SAR images.