El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can reduce the amount of rainfall in Indonesia. The previous study found that ENSO and IOD derived from the OISST dataset have an association with hotspots in Indonesia, especially in southern Sumatra dan Kalimantan. But the correlation results are still too small, and the correlation strength between regions has not been analyzed. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. We use a singular value decomposition method to quantify this HCM. Besides OISST, ERA5 is an estimation data often used for weather forecast analysis. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. Based on variance explained and correlation strength, the hotspot in Indonesia is more sensitive to ENSO and IOD derived from ERA5 than OISST. Consequently, the ERA5 data more useful to statistical analysis that requiring a substantial correlation.