Forecasting the Indonesian Coffee Production and Consumption Using the Modified Golden Section Search to Estimate the Smoothing Parameters

Triesha Syifahati; Anita Triska; Julita Nahar

doi:10.24198/jmi.v19.n1.44573.41-54

Forecasting the Indonesian Coffee Production and Consumption Using the Modified Golden Section Search to Estimate the Smoothing Parameters

Triesha Syifahati, Anita Triska, Julita Nahar

Abstract

The Double Exponential Smoothing (DES) and Triple Exponential Smoothing (TES) are forecasting methods that require two and three smoothing parameters, respectively. Smoothing parameters are often determined through a trial and error process that is not really efficient since many experiments need to be done. Therefore, in this study, a smoothing parameter estimation algorithm is conducted in the form of the modified Golden Section Search (GSS) to obtain the optimal smoothing parameters from the DES and TES methods. Forecasting is carried out on production, domestic consumption, and export consumption data of Indonesian coffee, which is one of the leading agricultural sub-sector commodities. The data is obtained from the Ministry of Agriculture of the Republic of Indonesia. The smoothing parameters obtained by applying the modified GSS are used to forecast production and domestic consumption data using the DES method, while the forecasting of the export consumption data is done with the TES method. All of the MAPE values are less than 20% which indicates that the smoothing parameters obtained by using the modified GSS are able to perform good forecasting. The results show that coffee production in Indonesia cannot meet its demand until 2024 since the total coffee consumption exceeds the production.

Keywords

double exponential smoothing;triple exponential smoothing;golden section search;Indonesian coffee forecast

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References

F. Akbari, A. Setyanto, and F. W. Wibowo. Optimasi parameter pemulusan algoritma Brown menggunakan metode golden section untuk prediksi data tren positif dan negatif. Jurnal Resti, 2(1):307–314, 2018.

A. R. Alias, N. Y. Zainun, and I. Abdul Rahman. Comparison between ARIMA and DES methods of forecasting population for housing demand in Johor. MATEC Web of Conferences, 81:1–5, 2016.

V. Andrean, P. C. Chang, and K. L. Lian. A review and new problems discovery of four simple decentralized maximum power point tracking algorithms-perturb and observe, incremental conductance, golden section search, and Newton’s quadratic interpolation. Energies, 11(11), 2018.

R. Anggrainingsih, G. R. Aprianto, and S. W. Sihwi. Time series forecasting using exponential smoothing to predict the number of website visitor of Sebelas Maret University. ICITACEE 2015 - 2nd International Conference on Information Technology, Computer, and Electrical Engineering: Green Technology Strengthening in Information Technology, Electrical and Computer Engineering Implementation, Proceedings, pages 14–19, 2016.

Badan Pusat Statistik. Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Desember 2014. Technical report, Badan Pusat Statistik, Jakarta, 2014.

J. Cai, D. Han, C. Chen, and S. Chen. Application of the golden section search algorithm in the nonlinear isoconversional calculations to the determination of the activation energy from nonisothermal kinetic conversion data. Solid State Sciences, 12(5):829–833, 2010.

A. Chusyairi, R. N. Pelsri, and E. Handayani. Optimization of exponential smoothing method using genetic algorithm to predict e-report service. Proceedings - 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2018, pages 292–297, 2018.

R. Crevits and C. Croux. Forecasting Using Robust Exponential Smoothing with Damped Trend and Seasonal Components. SSRN Electronic Journal, (November), 2018.

C. P. Da Veiga, C. R. P. Da Veiga, A. Catapan, U. Tortato, and W. V. Da Silva. Demand forecasting in food retail: A comparison between the Holt-Winters and ARIMA models. WSEAS Transactions on Business and Economics, 11(1):608–614, 2014. 12 Authors, JMI Vol x No y Okt/Apr 201x, pp. xx-yy,doi:10.24198/jmi.vxx.ny.xxxx.y-yy

A. M. de Livera, R. J. Hyndman, and R. D. Snyder. Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496):1513–1527, 2011.

X. Ding, H. Guo, and S. Guo. Efficiency Enhancement of Traction System Based on Loss Models and Golden Section Search in Electric Vehicle. Energy Procedia, 105:2923–2928, 2017.

W. E. Dwiguna. Peramalan Material Polyster Textured 75D pada Periode November 2016 sampai dengan Mei 2017 PT Tiga Manunggal Synthetic dengan Metode Time Series. Portal E-Journal Karya Ilmiah Undip, (November 2016), 2017.

et. all Amelia. Forecasting Annual Coffee and Rubber Production in Aceh Using Exponential Smoothing. Regular Proceeding 3rd ISIMMED, pages 3–10, 2019.

A. Fahlevi, F. A. Bachtiar, and B. D. Setiawan. Perbandingan Holt ’ s dan Winter ’ s Exponential Smoothing untuk Peramalan Indeks Harga Konsumen Kelompok Transportasi , Komunikasi dan Jasa Keuangan. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(12):6136–6145, 2018.

C. P. Ginting and F. Kartiasih. ANALISIS EKSPOR KOPI INDONESIA KE NEGARA-NEGARA ASEAN. Jurnal Ilmiah Ekonomi Dan Bisnis, 16(2):143–157, 2019.

D. N. Gujarati and D. Porter. Basic Econometrics. McGraw Hill Inc., New York, 5 edition, 2009.

H. D. P. Habsari, I. Purnamasari, and D. Yuniarti. Forecasting Uses Double Exponential Smoothing Method and Forecasting Verification Uses Tracking Signal Control Chart (Case Study: Ihk Data of East Kalimantan Province). BAREKENG: Jurnal Ilmu Matematika dan Terapan, 14(1):013–022, 2020.

E. J. Hughes. Many-objective directed evolutionary line search. Genetic and Evolutionary Computation Conference, GECCO’11, pages 761–768, 2011.

R. J. Hyndman and G. Athanasopoulos. Forecasting: Principles and Practice. Principles of Optimal Design, page 504, 2018.

N. Ilmayasinta. Forecasting Arrival of Foreign Tourists Using Seasonal Arima Box-Jenkins. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 15(2):223–230, 2021.

I. Irmeilyana, N. Ngudiantoro, and D. Rodiah. Correspondence Analysis Pada Hubungan Faktor-Faktor Yang Mempengaruhi Pendapatan Petani Kopi Pagaralam. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 15(1):179–192, 2021.

W. Jiang, X. Wu, Y. Gong, W. Yu, and X. Zhong. Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption. Energy, 193:116779, 2020.

M. D. Kartikasari. FORECASTING OF CURRENCY CIRCULATION IN INDONESIA. 16(2):635–642, 2022.

Kementerian Pertanian Republik Indonesia. Buku Outlook Komoditas Perkebunan Kopi. Technical report, Jakarta, 2020.

D. M. Khairina, Y. Daniel, and P. P. Widagdo. Comparison of double exponential smoothing and triple exponential smoothing methods in predicting income of local water company. Journal of Physics: Conference Series, 1943(1), 2021.

J. A. Koupaei, S. M. Hosseini, and F. M. Ghaini. A new optimization algorithm based on chaotic maps and golden section search method. Engineering Applications of Artificial Intelligence, 50:201–214, 2016.

C. D. Lewis. Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. Butterworth-Heinemann, Oxford, 1982.

R. J. Makridakis, S; Wheelwright, S. C.; Hyndman. Forecasting Methods and Applications. Forecasting methods and applications, pages 1–632, 1997.

D. A. I. Maruddani and Trimono. Modeling stock prices in a portfolio using multidimensional geometric brownian motion. Journal of Physics: Conference Series, 1025(1), 2018.

J. J. Monta˜no Moreno, A. Palmer Pol, A. Ses´e Abad, and B. Cajal Blasco. El ´ındice R-MAPE como medida resistente del ajuste en la previsio´nn. Psicothema, 25(4):500–506, 2013.

A. W. Omer, H. T. A. Blbas, and D. H. Kadir. A Comparison between Brown’s and Holt’s Double Exponential Smoothing for Forecasting Applied Generation Electrical Energies in Kurdistan Region. Cihan University-Erbil Scientific Journal, 5(2):56–63, 2021.

O. Oral. Comparison of The Winters’ Seasonality Exponential Smoothing Method With The Pegels’ Classification: Forecasting of Turkey’s Economic Growth Rates. Anadolu Universitesi Sosyal Bilimler ¨ Dergisi, 19(3):275–294, 2019.

E. Paparoditis and D. N. Politis. The asymptotic size and power of the augmented Dickey–Fuller test for a unit root. Econometric Reviews, 37(9):955–973, 2018.

D. Pejic and M. Arsic. Minimization and Maximization of Functions: Golden-Section Search in One Dimension. Springer International Publishing, 2019.

B. G. Prianda and E. Widodo. Perbandingan Metode Seasonal Arima Dan Extreme Learning Machine Pada Peramalan Jumlah Wisatawan Mancanegara Ke Bali. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 15(4):639–650, 2021.

M. W. Prihatmono and E. Utami. Analysis of moving average and holt-winters optimization by using golden section for ritase forecasting. Journal of Theoretical and Applied Information Technology, 95(23):6575– 6584, 2017.

A. Rahmawati, N. Desviona, and T. P. Sari. Estimation of the Open Unemployn Rate on Province Jambi Using the Double Exponential Smootthing. Nucleus, 3(1):10–19, 2022. Forecasting Indonesian coffee with modified GSS 13

G. Sandhya Rani, S. Jayan, and K. V. Nagaraja. An extension of golden section algorithm for n-variable functions with MATLAB code. IOP Conference Series: Materials Science and Engineering, 577(1), 2019.

N. D. Saputra, A. Aziz, and B. Harjito. Parameter optimization of Brown’s and Holt’s double exponential smoothing using golden section method for predicting Indonesian Crude Oil Price (ICP). Proceedings - 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2016, pages 356–360, 2017.

N. D. Saputra, A. Aziz, and B. Harjito. Perhitungan Kompleksitas Metode Golden Section dalam Optimasi Parameter Pemulusan Eksponensial Ganda Brown dan Holt. Jurnal Algoritma, 18(2):330–341, 2022.

T. Scherrer, S. Y. Kim, and C. Yi. Low complexity, real-time adjusted power management policy using Golden Section Search. ISOCC 2013 - 2013 International SoC Design Conference, pages 229–232, 2013.

R. Shao, R. Wei, and L. Chang. A multi-stage MPPT algorithm for PV systems based on golden section search method. Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC, pages 676–683, 2014.

S. Singh. Maximum power point tracking technique for PV system by using golden section search method. Advances in Systems Science and Applications, 18(3):111–122, 2018.

A. Sioofy Khoojine, M. Shadabfar, and Y. Edrisi Tabriz. A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices. Mathematics, 10(17), 2022.

P. M. Swamidass. Encyclopedia of Production and Manufacturing Management. Kluwer Academic Publishers, Boston, 2000.

S. J. Taylor and B. Letham. Forecasting at Scale. American Statistician, 72(1):37–45, 2018.

S. B. Tsai, Y. Xue, J. Zhang, Q. Chen, Y. Liu, J. Zhou, and W. Dong. Models for forecasting growth trends in renewable energy. Renewable and Sustainable Energy Reviews, 77:1169–1178, 2017.

D. A. Vieira, R. H. Takahashi, and R. R. Saldanha. Multicriteria optimization with a multiobjective golden section line search. Mathematical Programming, 131(1-2):131–161, 2012.

R. Windiarti and A. Kusmiati. Analisis Wilayah Komoditas Kopi Di Indonesia. Jurnal Sosial Ekonomi Pertanian, 5(2):47–58–58, 2011.

G. Yapar, I. Yavuz, and H. T. Selamlar. Why and how does exponential smoothing fail? An in depth comparison of ATA-simple and simple exponential smoothing. Turkish Journal of Forecasting, 01(1):30– 39, 2017.

X. Zhu, Q. Xu, M. Tang, W. Nie, S. Ma, and Z. Xu. Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in Sichuan Province, China. Engineering Geology, 218:213–222, 2017.

DOI: https://doi.org/10.24198/jmi.v19.n1.44573.41-54