Improving automatic fish freshness detection using TCS 230 and support vector machine

The freshness of fish is crucial in maintaining the quality of fish and preventing health risks associated with consuming stale fish. One of the methods that can be employed for fish freshness detection is using the TCS 230 sensor and classification techniques with a machine learning algorithm called SVM (Support Vector Machine).This research aims to develop a fish freshness detection system using initial sample data. The system utilizes the TCS 230 sensor and SVM classification technique. Measurements are taken by sampling fish and measuring the RGB values using the TCS 230 sensor. The measurement data is then processed and trained using the SVM algorithm to classify it into two categories: fresh and not fresh.The results of this research indicate that the fish freshness detection system using the TCS 230 sensor and SVM classification technique achieves a high level of accuracy with a dataset consisting of 12 RGB values for fish freshness. With this technique, fish freshness detection can be performed quickly and accurately for initial data, paving the way for further research with larger datasets and additional parameters in the future