Prediction of Wastewater Treatment Revenue Based on Volume and Number of Transactions Using the Long Short-Term Memory (LSTM) Method - Maulana, Aashif Amiruddin; Khaulasari, Hani; Novitasari, Dian Candra Rini; Pramono, Wahyu Joko (Faculty of Computer Science (FILKOM) Brawijaya University, 2025)
This study aims to develop a prediction model for the total Revenue value of the operational activities of the Keputih Surabaya Sewage Sludge Treatment Plant (IPLT) using the Long Short-Term Memory (LSTM) method. The data used is daily data on total transactions and total Revenue from January 2022 to April 2025. Data normalization using the Min-Max method and outlier detection and handling using the IQR and median imputation techniques are examples of preprocessing steps. The model input structure is formed by utilizing Partial Autocorrelation Function (PACF) analysis to ascertain the number of lags. In this study, 405 model combinations are tested with different parameters, including activation function, number of Epochs, learning rate, and ratios of training and testing data. According to the findings, the model that has the optimal parameters a training and testing data ratio of 80:20, 50 Epochs, a learning rate of 0.002, a Tanh activation function, and 100 neurons can produce predictions for total Revenue with a Mean Absolute Percentage Error (MAPE) of 18.18%. The revenue for the following six months was then forecast using this model; the highest revenue forecast was IDR 3,740,085.00, while the lowest was IDR 1,966,628.25. According to these results, LSTM can accurately forecast time series-based income fluctuations and may find use in the waste management industry's financial decision-making and strategic planning processes.