The application of chemometrics in the analyzing of chemical data from natural materials is essential in understanding complex chemical data. This study aims to apply the chemometric techniques of principal component analysis (PCA) and cluster observation (CA) for assisting the fingerprint profiling from 27 types of extracts from pagoda (Clerodendrum paniculatum L.) extraction using variations of solvent types (methanol, hexane, and ethanol), extraction techniques (maceration, Reflux, microwave-assisted extraction (MAE), and plant parts (flowers, leaves, and stems) which represent parts of plants. Each extract was subjected to thin-layer chromatography (TLC) using the mobile phase of hexane and ethyl acetate, and the measured Rf values of each spots obtained from densitometric evaluation at wavelength 254 nm was subjected to chemometrics analysis. The results exhibited that PCA showed variations in analyzed data describing more than 80% variances using thee principle components (PCs). Cluster analysis exhibited that all variables could be grouped into 10 clusters with the range of similarity indexes of 48.93%-99.78%. It can be concluded that chemometrics-assisted fingerprinting profile could differentiate the variations of Pagoda plant extracts according to solvent types, extraction techniques and parts of plants.