<![CDATA[Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are generally classifiedinto two types: step function and pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis identifiedARIMA (2, 1, 0), as the best-fitting model, characterized by a step function intervention with parametersb = 0, s = 2, and r = 0. Theaccuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.]]>
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