<![CDATA[Sea surface temperature (SST) is one of the features of climate variability that has a significant role in human activities. This study aims to predict and determine whether weather and climate variables with their measuring indicators can predict changes in SST by comparing daily and monthly data. This study uses a partial least square-structural equation modeling (PLS-SEM) approach which can predict the causality relationship between exogenous latent variables and endogenous latent variables. The results obtained from this study are, from the nine indicators used there are only 6 significant indicators with a loading factor value 0.7, namely sea surface temperature (oC) as a measure of latent variables SST changes, wind speed (m/s) and humidity relative (%) as a measure of the latent variable of weather, and air temperature (oC), short-wave solar radiation (w/m2) for daily data, and long-wave solar radiation (w/m2) for monthly data as a measure of climate latent variable. Inner model obtained on daily data: SST change (η) = -0.285 weather + 0.650 climate + and on monthly data SST change (η) = -0.330 weather + 0.793 climate +. In monthly data, weather and climate latent variables and their measuring indicators have a greater influence on changes in SST with the coefficient values in the model obtained being greater than in daily data. Latent variables that have a significant effect on changes in SST are weather and climate. This shows that if there is an increase or decrease in weather and climate it can cause significant changes to the SST. The value of the criteria on the outer model and inner model on daily and monthly data obtained better results on monthly data. The presence of more missing data in daily data can be one of the causes of this happening.]]>
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