Optimalisasi Peramalan Total Aset PT. BPD Kaltim Kaltara dengan Double Exponential Smoothing Brown

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DOI:

https://doi.org/10.32665/statkom.v3i2.3525

Keywords:

Forecasting, DES Brown, MAPE, Total Assets

Abstract

Background: Total assets can provide a comprehensive picture of the wealth owned by a company or institution, with total assets also helping to assess the scale of operations, stability, and the company’s ability to meet its financial responsibilities. Study on the total assets held by PT. BPD Kaltim Kaltara is interesting to do because it has an important role in advancing economic growth in the East Kalimantan and North Kalimantan regions. Digital transformation can influence how assets grow and how capital is structured.

Objective: Predicting PT BPD Kaltim Kaltara’s total assets over the next three periods using the DES Brown method with the optimal constant.

Methods: Double Exponential Smoothing Brown (DES Brown) with constants α = β = 0.3; 0.6; 0.7; 0.8.

Results: The smallest MAPE value is obtained at the constant α = β = 0.3, indicating that the DES Brown method with this constant provides the most accurate forecasting results.

Conclusion: The forecasting results for the next three periods show a stable upward trend, namely September at Rp48,389,055.93, October at Rp48,480,301.62, and November at Rp48,571,547.30. Thus, the DES Brown method has proven effective in forecasting the total assets of PT. BPD Kaltim Kaltara and can be used to support the company's financial decision making.

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Published

2024-12-31

How to Cite

Ningsih, E. L., Nurmayanti, W. P., Widyaningrum, E. N., & Pangruruk, T. A. (2024). Optimalisasi Peramalan Total Aset PT. BPD Kaltim Kaltara dengan Double Exponential Smoothing Brown. Jurnal Statistika Dan Komputasi, 3(2), 72–79. https://doi.org/10.32665/statkom.v3i2.3525

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Vol. 3 No. 2 (2024): Jurnal Statistika dan Komputasi

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Copyright (c) 2024 Eva Lestari Ningsih, Wiwit Pura Nurmayanti, Erlyne Nadhilah Widyaningrum, Thesya Atarezcha Pangruruk

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Abstract View: 64, PDF Download: 30 SIMILARITY INDEX Download: 0