<![CDATA[The point of this study is to look at how well Holt's Linear Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA) can predict time series data that have trend and non-seasonal characteristics. The information on the power capacity available for sale (kWh) at DKI Jakarta serves as the case study. It is anticipated that this study will serve as a guide for choosing efficient techniques for data types with trend and non-seasonal characteristics. This study uses a quantitative methodology with the application of Holt's Linear Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA). A total of 36 data points—monthly data from January 2020 to December 2022—were used in this study. From the analysis results, the error accuracy level was obtained based on the MAPE calculation, namely 3.18% for Holt's Linear Exponential Smoothing. Meanwhile, the best model with the ARIMA method is ARIMA(3,1,1) with a MAPE value of 3.124%. Based on the forecast results from January to March 2023, the predictions with the best model, namely ARIMA(3,1,1), are 3,140,106,571 kWh, 3,149,746,276 kWh and 3,154,664,915 kWh.]]>
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