Performance achievement analysis using linear regression and ARIMA (case study: KSP Credit Union Pancur Solidaritas)
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Abstract
Continuous performance measurement is important for organizations, especially cooperatives such as KSP (Koperasi Simpan Pinjam) Credit Union Pancur Solidaritas. This ensures work programs are planned and completed effectively and allows for ongoing success evaluation. Linear Regression and ARIMA are methods applied to set targets for organizational work programs and measure the cooperative's performance over time. This study aimed to examine performance achievement using Linear Regression and ARIMA (Auto Regressive Integrated Moving Average). The research used a quantitative descriptive approach. Study data included documentation on assets, member numbers, outreach activities, loan disbursements, overdue loans, and staff count for KSP Credit Union Pancur Solidaritas from 2021 to 2024. Data analysis employed Linear Regression and ARIMA tests performed using Python software. The study results showed that combining Linear Regression and ARIMA can produce three different performance possibilities: the highest anticipated performance (upper performance), the predicted performance (predicted performance), and the lowest possible performance (lower performance). Based on this analysis, the prediction for KSP CUPS member growth indicates an increase each month, with growth predicted to be 1,426 members by June 2025.
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