Strategy for Services Anticipation at the Rehabilitation Center of BNN Based on Moving Average and Exponential Smoothing Forecasting Method

Rosita Dewi Eka Renel () and Palupi Lindiasari Samputra ()
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Rosita Dewi Eka Renel: School of Strategic and Global Studies, University of Indonesia.
Palupi Lindiasari Samputra: School of Strategic and Global Studies, University of Indonesia.

Technium Social Sciences Journal, 2023, vol. 42, issue 1, 264-282

Abstract: One of the Indonesian government’s responses to the COVID-19 pandemic is making policies related to restrictions on public services which affects the organizational resilience of the Rehabilitation Center of the National Narcotics Board (BNN). This research aimed to determine the historical pattern of the influence of public service policies during the COVID-19 pandemic on the client population, to forecast the client population for 3 (three) months ahead, and to analyze strategies for anticipating rehabilitation services at the Rehabilitation Center of BNN. This research method is quantitative by using a moving average (MA) and exponential smoothing forecasting model. Based on the validity test, MA is the best forecasting model, which indicates a possibility of a spike in male clients with the same amount in the pre-pandemic period, as many as 310 people, and the average female client is 7 people. Meanwhile, adolescent clients show inaccurate prediction results with MAPE 149.825. Strategies that can be implemented to anticipate a spike in the number of clients if it reaches the highest forecasting point are: increasing the budget, modifying the rehabilitation program for female and adolescent clients, a balanced staff composition, and the availability of facilities and infrastructure.

Keywords: Forecasting; Moving Average; Exponential Smoothing; Rehabilitation Center (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:tec:journl:v:42:y:2023:i:1:p:264-282

DOI: 10.47577/tssj.v42i1.8700

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