Fish meat is a source of minerals and protein and contains excellent nutrients for the human body. However, non-fresh (rotting) fish are sometimes in the market for sale. Consuming rotting fish puts people at risk of getting diseases. This paper describes research to build a smelling device (e-nose) to identify fish freshness. It aims at detecting unsafe fish flesh to sort them out from being sold. We cut red snapper into cubes and put them into an open space at room temperature for five days. During the period, a gas sensor array acquired data of gas smell from the rotting fish. The output voltage of the sensors was processed using the differential baseline method. Later, feature extraction took the maximum value from the response of the gas sensor array, while the Principle Component Analysis (PCA) method identified the pattern. The results suggest that the gas sensor array responds to changes in the smell of fish meat that undergo a decay process. The PCA method is capable of recognizing the pattern of the maximum value characteristic of the gas sensor array response, as evidenced by the cumulative values of PC1 and PC2 reaching 95.95% with an accuracy rate of 98.2%. It shows the correlation between the aroma profiles of fish meat during the spoilage process, which produces a sharper aroma due to microbiological growth in the fish meat.