Analisis Klasifikasi Kekeruhan Air Berbasis Citra Dengan K-NN Pada Variasi Pencahayaan

Abstract

Clean and high-quality water is an essential requirement for public health and the continuity of industrial processes, including at PT Pupuk Sriwidjaja Palembang. One of the main parameters of water quality is turbidity, which is related to the presence of suspended particles such as mud, organic matter, and microorganisms. This study aims to analyze the effect of lighting intensity variations on the performance of water turbidity classification based on digital image processing using the K-Nearest Neighbor (K-NN) algorithm. The experiment was conducted under five lighting intensity levels: 10, 30, 50, 80, and 100 lux. The research stages included image acquisition, pre-processing (resizing, color conversion, and normalization), feature extraction of color and texture using mean, standard deviation, and Gray Level Co-occurrence Matrix (GLCM), followed by classification using the K-NN algorithm. The value of k = 5 was selected because it provides a balance between sensitivity to noise and classification stability, and preliminary testing showed more consistent performance compared to smaller or larger k values. System performance evaluation was carried out using accuracy, precision, F1-score, and confusion matrix. The results showed that the best performance was achieved at 100 lux lighting intensity with an accuracy of 91.67%, precision of 93.33%, and F1-score of 91.53%, while the lowest performance occurred at 10 lux with an accuracy of 61.54%. These findings indicate that lighting intensity significantly affects turbidity classification performance, with optimal conditions found in the range of 80–100 lux. This study proves that proper lighting adjustment can improve the reliability of digital image-based classification systems for automatic water quality monitoring.