Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques
This research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2024-08-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1948 |
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| _version_ | 1850052413580378112 |
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| author | Yudha Islami Sulistya Aina Musdholifah Chrissandy Sapuletea Elsi Titasari Br Bangun Hizbullah Hamda Sarah Anjani Abednego Dwi Septiadi |
| author_facet | Yudha Islami Sulistya Aina Musdholifah Chrissandy Sapuletea Elsi Titasari Br Bangun Hizbullah Hamda Sarah Anjani Abednego Dwi Septiadi |
| author_sort | Yudha Islami Sulistya |
| collection | DOAJ |
| description | This research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning techniques significantly improve prediction performance. For instance, the ensemble model for predicting area harvested, combining Model 6 (linear regression) and Model 10 (ARIMA), achieved of coefficient of determination outperforming the individual models. Similarly, for predicting yield, the ensemble model combining Model 4 (linear regression) and Model 9 (ARIMA) achieved of coefficient of determination indicating superior prediction accuracy. For predicting production, the ensemble model combining Model 2 (linear regression) and Model 8 (ARIMA) achieved of coefficient of determination. These results demonstrate the effectiveness of ensemble learning in enhancing prediction accuracy with lower MSE and RMSE values. By analyzing various factors influencing rice yields, this research provides valuable insights for increasing rice production and yield, supporting efforts to improve the efficiency and effectiveness of rice farming, and contributing to achieving the United Nations Sustainable Development Goals (SDGs). |
| format | Article |
| id | doaj-art-63731b4457bf4b999e59bfa8d3ccb8d4 |
| institution | DOAJ |
| issn | 2087-1716 2548-7779 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj-art-63731b4457bf4b999e59bfa8d3ccb8d42025-08-20T02:52:49ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792024-08-0116211512410.33096/ilkom.v16i2.1948.115-124636Prediction and Analysis of Rice Production and Yields Using Ensemble Learning TechniquesYudha Islami Sulistya0Aina Musdholifah1Chrissandy Sapuletea2Elsi Titasari Br Bangun3Hizbullah Hamda4Sarah Anjani5Abednego Dwi Septiadi6Universitas Gadjah MadaUniversitas Gadjah MadaInstitut Teknologi TelkomUniversitas Gadjah MadaUniversitas Gadjah MadaUniversitas Gadjah MadaInstitut Teknologi TelkomThis research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning techniques significantly improve prediction performance. For instance, the ensemble model for predicting area harvested, combining Model 6 (linear regression) and Model 10 (ARIMA), achieved of coefficient of determination outperforming the individual models. Similarly, for predicting yield, the ensemble model combining Model 4 (linear regression) and Model 9 (ARIMA) achieved of coefficient of determination indicating superior prediction accuracy. For predicting production, the ensemble model combining Model 2 (linear regression) and Model 8 (ARIMA) achieved of coefficient of determination. These results demonstrate the effectiveness of ensemble learning in enhancing prediction accuracy with lower MSE and RMSE values. By analyzing various factors influencing rice yields, this research provides valuable insights for increasing rice production and yield, supporting efforts to improve the efficiency and effectiveness of rice farming, and contributing to achieving the United Nations Sustainable Development Goals (SDGs).https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1948agricultural efficiencyarimaensemble learningpredictionrice productionrice yield |
| spellingShingle | Yudha Islami Sulistya Aina Musdholifah Chrissandy Sapuletea Elsi Titasari Br Bangun Hizbullah Hamda Sarah Anjani Abednego Dwi Septiadi Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques Ilkom Jurnal Ilmiah agricultural efficiency arima ensemble learning prediction rice production rice yield |
| title | Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques |
| title_full | Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques |
| title_fullStr | Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques |
| title_full_unstemmed | Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques |
| title_short | Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques |
| title_sort | prediction and analysis of rice production and yields using ensemble learning techniques |
| topic | agricultural efficiency arima ensemble learning prediction rice production rice yield |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1948 |
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