PERAMALAN BAHAN BAKU KELAPA DENGAN MOVING AVERAGE, EXPONENTIAL SMOOTHING DAN TREND ANALYSIS
Keywords:
peramalan persediaan, bahan baku kelapa, moving average, exponential smoothing, trend analysis
Abstract
Model peramalan persediaan sangat penting untuk mengoptimalkan dan mengelola jumlah produk yang diproduksi karena peramalan yang akurat dapat menghindari kelebihan pasokan bahan baku yang mahal atau kurangnya pengendalian persediaan yang menghambat Anda dalam memenuhi permintaan konsumen. Salah satu bahan baku yang potensial untuk diramalkan adalah bahan baku kelapa. Metode yang digunakan untuk peramalan persediaan kelapa moving average, exponential smoothing, Trend Analysis. Data yang digunakan merupakan data tahunan produksi kelapa Tahun 1980-2023. Hasil penelitian menunjukkan bahwa peramalan tiga tahun kedepan moving average sebesar 2862170 Ton/Tahun, exponential smoothing sebesar 2887527 Ton/Tahun. Peramalan Trend Analysis untuk Tahun 2024 sebesar 3376062, Tahun 2025 sebesar 3406679 Ton/Tahun dan Tahun 2027 3437296 Ton/Tahun. Moving average dan exponential smoothing termasuk peramalan yang sangat akurat karena nilai MAPE kurang dari 10 % sedangkan trend analysis termasuk peramalan yang bagus karena nilai MAPE di atas 10 %
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References
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[4] S. Wardah and I. Iskandar, “ANALISIS PERAMALAN PENJUALAN PRODUK KERIPIK PISANG KEMASAN BUNGKUS (Studi Kasus : Home Industry Arwana Food Tembilahan),” J@ti Undip J. Tek. Ind., vol. 11, no. 3, p. 135, 2017, doi: 10.14710/jati.11.3.135-142.
[5] D. Effrosynidis, E. Spiliotis, G. Sylaios, and A. Arampatzis, “Time series and regression methods for univariate environmental forecasting: An empirical evaluation,” Sci. Total Environ., vol. 875, no. November 2022, p. 162580, 2023, doi: 10.1016/j.scitotenv.2023.162580.
[6] C. J. Nathania, F. R. Iskandar, A. F. Wicaksonoputra, and R. Nurcahyo, “Production Planning Forecasting using Single Moving Average and Exponential Smoothing Method in PT. Semen Indonesia,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, 2021, pp. 324–330.
[7] T. Hariguna and Ajiono, “Comparison of Three Time Series Forecasting Methods on Linear Regression, Exponential Smoothing and Weighted Moving Average,” Int. J. Informatics Inf. Syst., vol. 6, no. 2, pp. 89–102, 2023.
[8] C. J. Lynch and R. Gore, “Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods,” Data Br., vol. 35, p. 106759, 2021, doi: 10.1016/j.dib.2021.106759.
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[17] J. Heizer, B. Render, and C. Munson, Principles of Operations Management: Sustainability and Supply Chain Management. 2017.
[18] S. Chopra and M. Peter, Supply chain management: strategy, planning, and operation - third edition, Sixth Edit. Pearson, 2016.
[19] L. Neubauer and P. Filzmoser, “Improving forecasts for heterogeneous time series by ‘averaging’, with application to food demand forecasts,” Int. J. Forecast., no. xxxx, 2024, doi: 10.1016/j.ijforecast.2024.02.002.
[20] G. Tirkeş, C. Güray, and N. Çelebi, “Demand Forecasting: A Comparison Between The Holt-Winters, Trend Analysis And Decomposition Models,” Teh. Vjesn., vol. 24, pp. 503–509, 2017, doi: 10.17559/TV-20160615204011.
Published
2024-06-19
How to Cite
Wardah, S. W., & Taufik Baidawi. (2024). PERAMALAN BAHAN BAKU KELAPA DENGAN MOVING AVERAGE, EXPONENTIAL SMOOTHING DAN TREND ANALYSIS. JURNAL PERANGKAT LUNAK, 6(2), 190-197. https://doi.org/10.32520/jupel.v6i2.3196
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Articles
Copyright (c) 2024 Siti Wardah Wardah, Taufik Baidawi
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