This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images due to its continuous variation over time; and (2) the uncertainty of the change results propagating from object extraction and implementation of shoreline change detection method. Unsupervised FCM achieved the highest kappa (κ) value when threshold (t) was set at 0.5. The highest κ values were 0.96 for the 1994 image. For images in 2013, 2014 and 2015, the κ values were 0.95. Further, images in 2003, 2002 and 2000 obtained 0.93, 0.90 and 0.86, respectively. Gradual and abrupt changes were observed, as well as a measure of change uncertainty for the observed objects at the pixel level. These could be associated with inundations from 1994 to 2015 at the northern coastal area of Java, Indonesia. The largest coastal inundations in terms of area occurred between 1994 and 2000, when 739 ha changed from non-water and shoreline to water and in 2003–2013 for 200 ha. Changes from water and shoreline to non-water occurred between 2000 and 2002 (186 ha) and in 2013–2014 (65 ha). Urban development in flood-prone areas resulted in an increase of flood hazards including inundation and erosion leading to the changes of shoreline position. The proposed methods provided an effective way to present shoreline as a line and as a margin with fuzzy boundary and its associated change uncertainty. Shoreline mapping and monitoring is crucial to understand the spatial distribution of coastal inundation including its trend.