The Effect of Lighting Variations on the Accuracy of Formalin Detection in Milkfish Using HSV Color Space and k-Nearest Neighbors (kNN) Algorithm - Noor Falih; Ruth Mariana Bunga Wadu; Andhika Octa Indarso; Hastie Audytra; Ahmad Ali Hakam Dani (APIC, 2025)
Milkfish (Chanos chanos) is a widely consumed fish commodity in Indonesia, often subject to preservation using formalin, a chemical with serious health risks when misused. This study proposes a non-destructive formalin detection method using HSV (Hue, Saturation, Value) color features extracted from eye images of milkfish, classified via the k-Nearest Neighbor (kNN) algorithm. The research investigates the impact of varying illumination levels low, medium, and high on the consistency of HSV features and the accuracy of kNN classification. Results show that medium lighting conditions yield the highest classification accuracy, suggesting an optimal illumination range for field deployment. The system's simplicity and potential for real-time implementation on mobile or embedded platforms make it suitable for use by non-technical personnel in traditional markets. Challenges such as environmental temperature, image angle, and surface reflectivity are addressed through calibration strategies and operational guidelines. This study contributes practical insights into lighting control and feature stability, enhancing the reliability of image-based formalin detection systems.