The precision of sea surface temperature (SST) from remote sensing data is essential to recognize SST fluctuations prompted by extreme weather conditions due to global climate warming, such as tropical cyclones (TCs). Since 1981 the active remote sensing of satellite-based SST measurements has been around and proliferating to date in Indonesia. However, there has not been much research on the validation of several remote sensing datasets in Indonesia's seas that has limited coverage of buoy observations. Moreover, no studies correspond to which data are the most precise in describing SST fluctuations in tropical storms. In this study, six remote sensing/satellite (Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), Regional Australian Multi-Sensor SST Analysis (RAMSSA), Global Australian Multi-Sensor SST Analysis (GAMSA), Microwave Infrared Optimally Interpolated (MWIROI), Multi-scale Ultra-high Resolution (MUR), and K10) data are validated and compared to analyze SST fluctuations in TC as a case study. The validation method uses the Haversine distance formula to reach the highest quality iQuam data with satellite data. The comparison analysis is performed by plotting the SST and wind slop in a TC area. Based on the validation, The OSTIA, RAMSSA, GAMSSA, and MWIROI datasets ranked in the top 4 of the smallest RMSEs with values < 0.5. Moreover, in the SST and wind slop in a TC area, TC affects SST cooling as detected in the MUR and K10 datasets where there is a decrease of > 2 ºC. In the MWIROI, the decline is more noticeable significant > 3 ºC.