Flood mapping is important to do as part of emergency response efforts in the event of a disaster. Usually, there are limitations in flood mapping with a direct approach and ineffective mapping efforts, such as: lack of time, cost, and number of personnel in the field. Utilization of remote sensing data can be used as an alternative to overcome this problem with an indirect approach. One of the uses of remote sensing data can be used to provide objective information and can be used to monitor, detect and map floods spatially-temporally. In this paper we will discuss of flood inundation mapping with several color composite combinations from dual-polarization Sentinel 1-A data using three classification methods. The data used is Sentinel 1-A dual-polarization data, in vertical-vertical receive (VV) and vertical-horizontal receive (VH) transmission mode. These datasets were acquired pre- (08 December 2016) and post- (22 February 2017) the flood even of 20 - 22 February 2017. This method is the detection of flooding of surface changes, the classification process for flood inundation mapping (maximum likelihood, minimum distance, and Mahalanobis distance), and then calculating an accuracy assessment. Six kinds of color combinations polarization combination Red Green Blue from the three classification methods. Flood detection of surface changes from a combination of six types of color composites gives very diverse results. The six types are VH_pre-flood - VH_post-flood - VH_pre-flood / VH_post-flood. VH_preflood - VV_post-flood - VH_pre-flood / VV_post-flood. VV_pre-flood - VV_post-flood - VV_pre-flood / VV_postflood. VV_pre-flood - G: VH_post-flood - VV_pre-flood / VH_post-flood. VV_pre-flood - VH_post-flood - VV_postflood. VH_pre-flood - VH_post-flood - VV_post-flood. From the calculation of accuracy for each classification method, the combination of VV_pre-flood - VH_post-flood - VV_post-flood using the minimum distance classification has the highest level of accuracy compared to other combinations. Therefore, to make a flood inundation map using dualpolarization dual-sentinel 1-A data, this composite is the best.