ENHANCING WATER QUALITY CLASSIFICATION IN LAKE TOBA WITH DEEP LEARNING APPROACH

Lake Toba serves as a crucial water resource that is extensively utilized for various purposes. However, different water types have distinct uses, necessitating proper classification. Government Regulation No. 82 of 2001 establishes water quality standards to classify water into several categories, each indicating its appropriate use. This study proposes the use of a deep neural network (DNN) to classify the water quality of Lake Toba. The research explores different activation functions—Softmax, ReLU, and Sigmoid—along with the SGD, RMSProp, and Adam optimizers. To determine the most effective model architecture, each activation function was tested in combination with different optimizers. The findings indicate that deep neural networks (DNNs) can be effectively utilized for water quality classification, with accuracy and error rates influenced by the activation function, optimizer, number of neurons, and number of hidden layers. The dataset used in this study includes measurements of water temperature, pH level, dissolved oxygen concentration, oxidation-reduction potential, air temperature, and humidity, which are essential for monitoring the water quality of Lake Toba. The testing process consists of two approaches: (1) classification using three parameters, based on Government Regulation No. 82 of 2001, and (2) classification using six parameters. Each test is conducted using the same model architecture. The highest recorded accuracy in the experiments was 99% (0.998402), with the lowest recorded loss at 0.014616. These results were obtained from studies utilizing three parameters.