<![CDATA[This research aims to design and develop an Internet of Things (IoT)-based monitoring system for the quality of retail petroleum products capable of operating in real time, in order to address the public’s limitations in independently verifying the suitability of retail petroleum products. The system was developed using a proximity sensor as an initial trigger to detect the presence of objects or liquids, and a TDS sensor as a fuel quality sensor to measure changes in conductivity values indicating the water content in fuel samples; each reading is locked for 5 seconds to ensure the stability of the proximity and TDS sensor values before the data is sent to a Flask-based server and analysed using a Support Vector Machine (SVM) algorithm to classify the fuel condition into the categories ‘Suitable’ and ‘Unsuitable’. Tests were carried out on 100 retail fuel samples, comprising 50 samples of pure fuel and 50 samples of fuel mixed with water, with classification rules based on TDS values: a value close to or equal to zero indicates that the fuel shows no signs of water contamination (Acceptable), whilst a value above zero which in the tests varied from 1 to over 700 depending on the level of contamination indicates the presence of water admixture (Unfit). The research results show that the system is capable of performing sensor readings, data transmission and the classification process effectively in real time; furthermore, based on an evaluation using a confusion matrix, the SVM model achieved an accuracy of 0.94, a precision of 0.946, a recall of 0.94 and an F1-score of 0.94. With this performance, the developed system has the potential to be utilised by the public and retail fuel businesses to verify fuel suitability quickly, automatically and objectively without the need for laboratory testing.]]>
