Article Sidebar

Submitted
Oct 31, 2024
Accepted
Dec 23, 2024
Published
Dec 31, 2024
Download
PDF
Statistic
Read Counter : 20 Download : 24

Main Article Content

Abstract

PT. BPD Kaltim Kaltara is one of the regional development banks that plays a crucial role in supporting regional economic development in East Kalimantan and North Kalimantan. The company's total assets reflect significant financial stability and growth, making it an interesting topic to analyze in the context of strategic financial planning. The purpose of this study is to use the Single Exponential Smoothing (SES) approach to forecast PT. BPD Kaltim Kaltara's total assets. In the forecasting process, alpha 0,3, alpha 0,6, alpha 0,7, and alpha 0,8 are tested to determine the best value that gives the most accurate results. Based on the forecasting accuracy analysis, the SES method with alpha = 0,7 proved to be the most optimal in predicting the company's total assets, achieving MAE = 1454272,737, MSE = 4764920751283, and MAPE = 4,0433% (excellent forecasting ability). The forecasting results show an upward trend in assets, with total assets in September 2024 estimated to reach IDR 48.440.683,75. This method provides valuable guidance in thecompany's financial strategic planning, helping to anticipate future asset developments more precisely.These forecasting results also emphasize the importance of selecting the right parameters in the forecasting model to improve prediction accuracy.

Keywords

Forecasting Single Exponential Smoothing Time Series Analysis Total Assets

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Nurmayanti, W. P., Ningsih, E. L., Arif, Z., Fathurahman, M., & Hasanah, S. H. (2024). FORECASTING TOTAL ASSETS OF PT. BPD KALTIM KALTARA USING THE SINGLE EXPONENTIAL SMOOTHING METHOD. Parameter: Journal of Statistics, 4(2), 103-109. https://doi.org/10.22487/27765660.2024.v4.i2.17473

References

  1. Aijaz, I., & Agarwal, P. (2020). A study on time series forecasting using hybridization of time series models and neural networks. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 13(5), 827–832.
  2. Bankaltimtara. (2022). Strengthening Acceleration Through the Development of Digital Services.
  3. Capelli, P., Ielasi, F., & Russo, A. (2021). Forecasting volatility by integrating financial risk with environmental, social, and governance risk. Corporate Social Responsibility and Environmental Management, 28(5), 1483–1495.
  4. Chandra, N. E., & Rohmaniah, S. A. (2022). Perbandingan Metode Moving Average dan Single Exponential Smoothing Pada Peramalan Inflasi Kota Purwokerto. Jurnal Matematika, 12(1), 49–55. https://doi.org/10.24843/jmat.2022.v12.i01.p148
  5. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science, 7, e623.
  6. Juwita, Harjadi, D., & Purnama, D. (2021). Total Aset, Risiko Bisnis, Pertumbuhan Aset Dan Profitabilitas Terhadap Harga Saham. Jurnal Media Komunikasi Dan Bisnis, 12(2), 33–41. https://doi.org/10.52659/medikonis.v12i2.46
  7. Karunasingha, D. S. K. (2022). Root mean square error or mean absolute error? Use their ratio as well.
  8. Information Sciences, 585, 609–629.
  9. Kothari, S. P., So, E., & Verdi, R. (2016). Analysts’ forecasts and asset pricing: A survey. Annual Review of Financial Economics, 8(1), 197–219.
  10. Liu, Y., Wu, H., Wang, J., & Long, M. (2022). Non-stationary transformers: Exploring the stationarity in time series forecasting. Advances in Neural Information Processing Systems, 35, 9881–9893.
  11. Liu, Z., Zhu, Z., Gao, J., & Xu, C. (2021). Forecast methods for time series data: a survey. Ieee Access, 9, 91896–91912.
  12. Prastyabudi, W. A., Shamaradewa, S. A., Industri, F. R., Studi, P., & Industri, T. (2024). Perbandingan Metode Moving Average dan Exponential Smoothing untuk Mengestimasi Jumlah Gangguan Layanan Internet. Jurnal Rekayasa Sistem Industri, 13(2), 157–176.
  13. Riauwanto, S., & Sulastiningsih, S. (2019). Pengaruh Total Aset Dan Bagi Hasil Perbankan Terhadap Volume Dana Pihak Ketiga (Dpk) Pada Bank Umum Syariah. Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen, 6(2), 131–146. https://doi.org/10.32477/jrm.v6i2.354
  14. Romaita, D., Bachtiar, F. A., & Furqon, M. T. (2019). Perbandingan Metode Exponential Smoothing Untuk Peramalan Penjualan Produk Olahan Daging Ayam Kampung (Studi Kasus : Ayam Goreng Mama Arka). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 3(11), 10387–10392.
  15. Sulastri, H., Anwar, G. S., & Dewi, E. N. F. (2023). Peramalan Stok Barang Percetakan dan ATK Menggunakan Single Moving Average. Jurnal Rekayasa Teknologi Informasi (JURTI), 7(1), 59–
  16. 69. https://doi.org/10.30872/jurti.v7i1.11876
  17. Utama, C. A. (2012). Company disclosure in Indonesia: Corporate governance practice, ownership structure, competition and total assets. Asian Journal of Business and Accounting, 5(1).