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Peramalan Produksi Singkong Provinsi Lampung Indonesia dengan Metode Kuantitatif menggunakan Aplikasi POM QM

JIA (Jurnal Ilmiah Agribisnis) : Jurnal Agribisnis dan Ilmu Sosial Ekonomi Pertanian · Vol. 10 No. 6 (2025) · hlm. 660–667

DOI
10.37149/jia.v10i6.2363
ISSN
2527-273X
Terbit
2025-12-29
Penerbit
Department of Agribusiness, Halu Oleo University Jointly with Perhimpunan Ekonomi Pertanian Indonesia - Indonesian Society of Agricultural Economics (PERHEPI/ISAE)
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0/

Abstrak

Cassava is considered a strategic commodity due to its versatility in processing into various products and its status as a significant source of carbohydrates, commonly consumed after rice and corn. Population growth indirectly increases overall consumption, leading to higher demand for cassava. To ensure this demand is met, maintaining a sufficient supply is essential. The objective of this study is to forecast cassava production trends in Lampung Province for 2025-2029 using data from 2019-2024. The forecasting process uses POM-QM for Windows Version 5 software and applies several forecasting methods, including Moving Average, Exponential Smoothing, Linear Regression, Weighted Moving Average, and Exponential Smoothing with Trend. These methods are evaluated using performance metrics including MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and MSE (Mean Squared Error) to determine the most accurate approach. Among the tested methods, Exponential Smoothing with Trend was selected as the most suitable, supported by the regression equation y = 656.34x + 4435.5, which indicates strong predictive accuracy. Based on this model, cassava production is expected to continue increasing in the coming years, assuming expansion in planting areas and a higher Planting Index (PI). This projected growth presents opportunities for industries relying on cassava as a raw material and potentially opens the door to international market expansion. Using multi-method quantitative analysis in this study contributes to accurate data, more targeted agroindustrial policy strategies, and better business decisions.

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  • Sumatera Institute of Technology
  • IPB University
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Rekan penulisOrganisasiArtikel bersama
Rechal PerdanaIPB University1
Nurbaiti NurbaitiSumatera Institute of Technology1
Ayu Rahayu SaraswatiSumatera Institute of Technology1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ejournal.agribisnis.uho.ac.id/index.php/JIA/oai, pertama di-harvest 4 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Peramalan Produksi Singkong Provinsi Lampung Indonesia dengan  Metode Kuantitatif menggunakan Aplikasi POM QM</dc:title>
	<dc:creator>Yogautami, Radya</dc:creator>
	<dc:creator>Izdihar, Ulfah</dc:creator>
	<dc:creator>Windiastuti, Elsa</dc:creator>
	<dc:subject xml:lang="en-US">cassava</dc:subject>
	<dc:subject xml:lang="en-US">forecasting</dc:subject>
	<dc:subject xml:lang="en-US">POM QM</dc:subject>
	<dc:subject xml:lang="en-US">production</dc:subject>
	<dc:description xml:lang="en-US">Cassava is considered a strategic commodity due to its versatility in processing into various products and its status as a significant source of carbohydrates, commonly consumed after rice and corn. Population growth indirectly increases overall consumption, leading to higher demand for cassava. To ensure this demand is met, maintaining a sufficient supply is essential. The objective of this study is to forecast cassava production trends in Lampung Province for 2025-2029 using data from 2019-2024. The forecasting process uses POM-QM for Windows Version 5 software and applies several forecasting methods, including Moving Average, Exponential Smoothing, Linear Regression, Weighted Moving Average, and Exponential Smoothing with Trend. These methods are evaluated using performance metrics including MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and MSE (Mean Squared Error) to determine the most accurate approach. Among the tested methods, Exponential Smoothing with Trend was selected as the most suitable, supported by the regression equation y = 656.34x + 4435.5, which indicates strong predictive accuracy. Based on this model, cassava production is expected to continue increasing in the coming years, assuming expansion in planting areas and a higher Planting Index (PI). This projected growth presents opportunities for industries relying on cassava as a raw material and potentially opens the door to international market expansion. Using multi-method quantitative analysis in this study contributes to accurate data, more targeted agroindustrial policy strategies, and better business decisions.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Agribusiness, Halu Oleo University  Jointly with  Perhimpunan Ekonomi Pertanian Indonesia - Indonesian Society of Agricultural Economics (PERHEPI/ISAE)</dc:publisher>
	<dc:date>2025-12-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ejournal.agribisnis.uho.ac.id/index.php/JIA/article/view/2363</dc:identifier>
	<dc:identifier>10.37149/jia.v10i6.2363</dc:identifier>
	<dc:source xml:lang="en-US">JIA (Jurnal Ilmiah Agribisnis) : Jurnal Agribisnis dan Ilmu Sosial Ekonomi Pertanian; Vol. 10 No. 6 (2025); 660 - 667</dc:source>
	<dc:source>2527-273X</dc:source>
	<dc:source>10.37149/jia.v10i6</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ejournal.agribisnis.uho.ac.id/index.php/JIA/article/view/2363/488</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Radya Yogautami, Ulfah Izdihar, Elsa Windiastuti</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0/</dc:rights>
</oai_dc:dc>