Peramalan Jumlah Produksi Tebu Menggunakan Metode Time Series Model Moving Averages

Authors

  • Nabila Azahra Institut Teknologi Telkom Purwokerto, Purwokerto
  • Salsabila Cahya Alifia Institut Teknologi Telkom Purwokerto, Purwokerto
  • Nevandra Putra Andyka Institut Teknologi Telkom Purwokerto, Purwokerto
  • Sena Wijayanto Institut Teknologi Telkom Purwokerto, Purwokerto
  • M Yoka Fathoni Institut Teknologi Telkom Purwokerto, Purwokerto http://orcid.org/0000-0001-7651-6351

DOI:

https://doi.org/10.30865/jurikom.v9i4.4388

Keywords:

Forecasting, Time Series, Moving Averages, Production, Sugarcane

Abstract

Plantation is all activities that use certain and specific plants with soil or other parts of plants in adapted ecosystems. Other plantation activities also include processing and marketing of these crops. In the plantation sub-sector, sugar cane is an important plantation and national economic development strategy and contributes significantly to the plantation sub-sector. Sugarcane is a plantation crop that is widely grown in Indonesia, such as in Java, North and South Sumatra. One of the sugarcane producing areas in Purworejo Regency is Loano District. There are many sugar factories scattered throughout the sugar cane development area and the leading sugarcane plants are in Purworejo Regency. The purpose of this research is to predict production to meet market demand using the Time Series method with the Moving Average model. This study uses a Moving Average model consisting of: Single Moving Average (SMA) and Weighted Moving Average (WMA) with forecasting accuracy using Mean Square Error (MSE) and Mean Absolute Error (MAE) as the selection of the best model to be used for forecasting. From this study, the results of forecasting the amount of sugarcane production in Loano District for the next 4 periods, after 2015 from the WMA model, are: 113,91 ton; 135,62 ton; 101,96 ton; and 89,88 ton. The best model result is the Weighted Moving Average (WMA) model with the smallest forecasting accuracy value, namely the MSE value of 1.833,07 and the MAE of 36,07.

References

M. Lestari, “ANALISA USAHATANI TEBU (Studi Kasus di Kecamatan Ngantru Kabupaten Tulungagung),†Agribis, vol. 13, no. 15, pp. 48–54, 2017.

A. Latifa, “Digital Repository Universitas Jember,†p. 27, 2015.

M. A. D. N. Achadin, “Analisis Faktor Yang Mempengaruhi Produksi Tebu Pada Sub Sektor Perkebunan Di Provinsi Jawa Timur Tahun 2011-2015,†J. Ekon. Pembang., vol. 15, no. 2, p. 193, 2017.

Ø£. Ø«. Ù. Ùˆ. س. Ù…. الدرس, “No Titleتنمية الحكمة كمدخل للصمود Ø§Ù„Ù†ÙØ³ÙŠ Ù„Ø¯Ù‰ طلاب الملرحلة الثانوية المتÙوقين عقليا,†المنهل, pp. 1–6, 2019.

Z. Arrahman, “Tataniga Perkebunan Tebu Rakyat Di Kabupaten Situbondo,†Skripsi, pp. 1–62, 2018.

P. Ipm, “Analisis arah kebijakan ekonomi terhadap sektor pendidikan dalam peningkatan ipm,†vol. 5, no. 62, pp. 271–279, 2012.

F. Martínez, F. Charte, M. P. Frías, and A. M. Martínez-Rodríguez, “Strategies for time series forecasting with generalized regression neural networks,†Neurocomputing, vol. 491, pp. 509–521, 2022.

S. Y. Kuo and Y. H. Chou, “Building Intelligent Moving Average-Based Stock Trading System Using Metaheuristic Algorithms,†IEEE Access, vol. 9, pp. 140383–140396, 2021.

H. Shi, M. Yang, and P. Jiang, “Social production system: A three-layer smart framework for implementing autonomous human-machine collaborations in a shop floor,†IEEE Access, vol. 9, pp. 26696–26711, 2021.

H. Prapcoyo, “Peramalan Jumlah Mahasiswa Menggunakan Moving Average,†Telematika, vol. 15, no. 1, p. 67, 2018.

A. D. Andriana and R. Susanto, “Peramalan Jumlah Produksi Teh Menggunakan Metode Single Moving Average ( SMA ),†Pros. Saintiks FTIK UNIKOM, vol. 2, pp. 1–6, 2017.

A. Nasution, “Metode Weighted Moving Average Dalam M-Forecasting,†JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 5, no. 2, pp. 119–124, 2019.

M. Latif and R. Herdiansyah, “Peramalan Persediaan Barang Menggunakan Metode Weighted Moving Average dan Metode Double Exponential Smoothing,†J. Inf. Syst. Res., vol. 3, no. 2, pp. 137–142, 2022.

M. Y. Fathoni, “Forecasting Penjualan Gas LPG di Toko Sembako Menggunakan Metode Fuzzy Time Series,†vol. 13 No 2, pp. 87–96, 2021.

M. Y. Fathoni, “Implementasi Metode Fuzzy Time Series Cheng untuk prediksi Kosentrasi Gas NO2 Di Udara,†J. Sist. Inf. Bisnis, vol. 7, no. 1, p. 17, 2017.

H. A. K. Rady, “Shannon Entropy and Mean Square Errors for speeding the convergence of Multilayer Neural Networks: A comparative approach,†Egypt. Informatics J., vol. 12, no. 3, pp. 197–209, 2011.

A. A. Suryanto, “Penerapan Metode Mean Absolute Error (Mea) Dalam Algoritma Regresi Linear Untuk Prediksi Produksi Padi,†Saintekbu, vol. 11, no. 1, pp. 78–83, 2019.

N. Maaroufi, M. Najib, and M. Bakhouya, “Predicting the Future is like Completing a Painting: Towards a Novel Method for Time-Series Forecasting,†IEEE Access, vol. 9, pp. 119918–119938, 2021.

P. Li et al., “Dynamic Similar Sub-Series Selection Method for Time Series Forecasting,†IEEE Access, vol. 6, pp. 32532–32542, 2018.

E. Y. Nugraha and I. W. Suletra, “Analisis Metode Peramalan Permintaan Terbaik Produk Oxycan pada PT. Samator Gresik,†Semin. dan Konf. Nas. IDEC, pp. 2579–6429, 2017.

Additional Files

Published

2022-08-30

How to Cite

Azahra, N., Alifia, S. C., Andyka, N. P., Wijayanto, S., & Fathoni, M. Y. (2022). Peramalan Jumlah Produksi Tebu Menggunakan Metode Time Series Model Moving Averages. JURNAL RISET KOMPUTER (JURIKOM), 9(4), 840–845. https://doi.org/10.30865/jurikom.v9i4.4388