Comparison of the Results of Double Exponential Smoothing Method with Triple Exponential Smoothing for Predicting Chili Prices

Authors

  • Nadia Saphira Universitas Malikussaleh
  • Munirul Ula Universitas Malikussaleh
  • Sujacka Retno Universitas Malikussaleh

Keywords:

Double Exponential Smoothing, Triple Exponential Smoothing, Price Prediction Chili, MAPE, MAE.

Abstract

Double Exponential Smoothing (DES) is a forecasting method that combines two components level and trend, used for data with a trend pattern that tends to increase or decrease over time. In contrast, Triple Exponential Smoothing (TES) incorporates three components: level, trend, and seasonality, making it suitable for data with trend and seasonal patterns. This study uses historical chili price data from 2020 to 2023, obtained from the Bank Indonesia website, managed by the National Strategic Food Price Information Center (PIHPS), to compare the effectiveness of DES and TES in predicting chili prices in Medan City. Prediction accuracy was evaluated using MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error). The study results show MAPE values for DES as follows: Large Red Chili 1.25%, Curly Red Chili 1.39%, Green Bird’s Eye Chili 1.14%, and Red Bird’s Eye Chili 1.13%. TES produced slightly lower MAPE values: Large Red Chili 1.25%, Curly Red Chili 1.38%, Green Bird’s Eye Chili 1.12%, and Red Bird’s Eye Chili 1.10%. The MAE values for DES are as follows: Large Red Chili 447.9, Curly Red Chili 494.83, Green Bird’s Eye Chili 430.92, and Red Bird’s Eye Chili 423.36. TES showed better accuracy with MAE values of Large Red Chili at 447, Curly Red Chili at 493.02, Green Bird’s Eye Chili at 416.2, and Red Bird’s Eye Chili at 409.36. The results conclude that Triple Exponential Smoothing performs better than Double Exponential Smoothing in predicting chili prices.

References

[1] R. Afriani, R. S. Lubis, and H. Cipta, “Penduga harga Cabai Dengan Model Regresi Spline di Kota Medan,” Journal of Maritime and Education (JME), vol. 3, no. 2, pp. 245–249, Aug. 2022, doi: 10.54196/jme.v3i2.47.

[2] D. Sepri, A. Fauzi, R. Wandira, O. S. Riza, and Y. F. Wahyuni, “Prediksi Harga Cabai Merah Menggunakan Support Vector Regression,” Jl. Prof Yunus, 2020, [Online]. Available: http://ejournal.upbatam.ac.id/index.php/cbishttp://ejournal.upbatam.ac.id/index.php/cbis

[3] R. A. Chintia and R. Destiningsih, “Pengaruh Harga Komoditas Pangan Terhadap Inflasi Di Kota Semarang,” Jurnal Ilmiah Ekonomi Bisnis, vol. 27, no. 2, pp. 244–258, 2022, doi: 10.35760/eb.2022.v27i2.4948.

[4] M. David, I. Cholissodin, and N. Yudistira, “Prediksi Harga Cabai menggunakan Metode Long-Short Term Memory (Case Study : Kota Malang),” 2023. [Online]. Available: http://j-ptiik.ub.ac.id

[5] Sekar Setyaningtyas, B. Indarmawan Nugroho, and Z. Arif, “Penerapan Data Mining Teknik Clustering Algoritma K-Means,” Jurnal Teknoif Teknik Informatika Institut Teknologi Padang, vol. 10, no. 2, pp. 52–61, 2022, doi: 10.21063/jtif.2022.v10.2.52-61.

[6] F. R. Hariri and C. Mashuri, “Sistem Informasi Peramalan Penjualan dengan Menerapkan Metode Double Exponential Smoothing Berbasis Web,” Generation Journal, vol. 6, no. 1, pp. 68–77, 2022, doi: 10.29407/gj.v6i1.16204.

[7] R. J. Djami and Y. W. A. Nanlohy, “Peramalan Indeks Harga Konsumen di Kota Ambon Menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Double Exponential Smoothing,” VARIANCE: Journal of Statistics and Its Applications, vol. 4, no. 1, pp. 1–14, 2022.

[8] Fitria Viza and Anwar Samsul, “Penerapan Triple Exponential Smoothing Dalam Meramalkan Laju Inflasi Bulanan Provinsi Aceh Tahun 2019-2020,”

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Published

2024-12-27