Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice
Rice is a staple food in Indonesia. Although Indonesia produces a large amount of rice, it cannot meet domestic rice needs. The unpredictable domestic rice supply prompted the government to import rice. Moreover, rice imports are one of the efforts to provide rice stock. On the other hand, importing...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/65/bioconf_btmic2024_01036.pdf |
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| author | Novianti Trisita Agustina Fitri Indriartiningtias Retno |
| author_facet | Novianti Trisita Agustina Fitri Indriartiningtias Retno |
| author_sort | Novianti Trisita |
| collection | DOAJ |
| description | Rice is a staple food in Indonesia. Although Indonesia produces a large amount of rice, it cannot meet domestic rice needs. The unpredictable domestic rice supply prompted the government to import rice. Moreover, rice imports are one of the efforts to provide rice stock. On the other hand, importing rice can decrease domestic rice prices because it creates a market competitor. This study uses backpropagation artificial neural networks to develop a prediction system for rice supply crises in Indonesia based on models similar to currency crisis prediction systems. The study identified key variables and indicators for predicting rice supply crises, including rice production, consumption, prices, land area, and population. Data from January 2012 to December 2022 was analyzed. The optimal neural network architecture achieved a Mean Squared Error (MSE) of 0.209192. The analysis revealed that rice consumption, land area, and total population are the most strongly correlated indicators of a rice commodity crisis |
| format | Article |
| id | doaj-art-3d67248bf611439ab7fd2d3ea74668ff |
| institution | OA Journals |
| issn | 2117-4458 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-3d67248bf611439ab7fd2d3ea74668ff2025-08-20T02:19:38ZengEDP SciencesBIO Web of Conferences2117-44582024-01-011460103610.1051/bioconf/202414601036bioconf_btmic2024_01036Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported riceNovianti Trisita0Agustina Fitri1Indriartiningtias Retno2Industrial Engineering Department, Universitas Trunojoyo Madura, Jl. Raya TelangIndustrial Engineering Department, Universitas Trunojoyo Madura, Jl. Raya TelangIndustrial Engineering Department, Universitas Trunojoyo Madura, Jl. Raya TelangRice is a staple food in Indonesia. Although Indonesia produces a large amount of rice, it cannot meet domestic rice needs. The unpredictable domestic rice supply prompted the government to import rice. Moreover, rice imports are one of the efforts to provide rice stock. On the other hand, importing rice can decrease domestic rice prices because it creates a market competitor. This study uses backpropagation artificial neural networks to develop a prediction system for rice supply crises in Indonesia based on models similar to currency crisis prediction systems. The study identified key variables and indicators for predicting rice supply crises, including rice production, consumption, prices, land area, and population. Data from January 2012 to December 2022 was analyzed. The optimal neural network architecture achieved a Mean Squared Error (MSE) of 0.209192. The analysis revealed that rice consumption, land area, and total population are the most strongly correlated indicators of a rice commodity crisishttps://www.bio-conferences.org/articles/bioconf/pdf/2024/65/bioconf_btmic2024_01036.pdf |
| spellingShingle | Novianti Trisita Agustina Fitri Indriartiningtias Retno Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice BIO Web of Conferences |
| title | Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice |
| title_full | Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice |
| title_fullStr | Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice |
| title_full_unstemmed | Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice |
| title_short | Early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice |
| title_sort | early warning system modeling for rice supply using backpropagation artificial neural network to manage imported rice |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/65/bioconf_btmic2024_01036.pdf |
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