Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural Network
Climatic conditions of the rainy season, such as the clear difference between the rainy and dry seasons, greatly affect the meteorological characteristics, especially the temperature and rainfall in the territory of Indonesia. To maximize the availability of water and variations in rainfall for plan...
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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 |
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| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/63/bioconf_sage-grace2024_01004.pdf |
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| author | Salsalbilla Septya Riyadi Slamet Zaki Ahmad Nursetiawan Nursetiawan |
| author_facet | Salsalbilla Septya Riyadi Slamet Zaki Ahmad Nursetiawan Nursetiawan |
| author_sort | Salsalbilla Septya |
| collection | DOAJ |
| description | Climatic conditions of the rainy season, such as the clear difference between the rainy and dry seasons, greatly affect the meteorological characteristics, especially the temperature and rainfall in the territory of Indonesia. To maximize the availability of water and variations in rainfall for plant growth and development, plant cultivation requires a proper approach. The method used in this study is Artificial Neural Network which is implemented with the help of Matlab software version 2019 with nntools. This method is used to predict rainfall in the Bantul area. In this study, the data used were rainfall, minimum temperature, maximum temperature, average temperature, wind speed, humidity, and air pressure. This data is processed using Artificial Neural Networks to accurately predict rainfall in the region. The test results show that the comparison of the actual data results of rainfall prediction using the Levenberg Marquart algorithm with 1,080 training data of 80% data composition, validation data 10 and test data 10 with layer 4 size with layer 10 hidden neural produces predictions with a good level of accuracy and obtains a value of R = 0.900. |
| format | Article |
| id | doaj-art-a61de8c48b1146f88c21c363e5ef18e5 |
| institution | Kabale University |
| issn | 2117-4458 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-a61de8c48b1146f88c21c363e5ef18e52024-12-06T09:32:56ZengEDP SciencesBIO Web of Conferences2117-44582024-01-011440100410.1051/bioconf/202414401004bioconf_sage-grace2024_01004Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural NetworkSalsalbilla Septya0Riyadi Slamet1Zaki Ahmad2Nursetiawan Nursetiawan3Magister of Civil Engineering, Universitas Muhammadiyah YogyakartaDepartment of Information Technology, Universitas Muhammadiyah YogyakartaMagister of Civil Engineering, Universitas Muhammadiyah YogyakartaMagister of Civil Engineering, Universitas Muhammadiyah YogyakartaClimatic conditions of the rainy season, such as the clear difference between the rainy and dry seasons, greatly affect the meteorological characteristics, especially the temperature and rainfall in the territory of Indonesia. To maximize the availability of water and variations in rainfall for plant growth and development, plant cultivation requires a proper approach. The method used in this study is Artificial Neural Network which is implemented with the help of Matlab software version 2019 with nntools. This method is used to predict rainfall in the Bantul area. In this study, the data used were rainfall, minimum temperature, maximum temperature, average temperature, wind speed, humidity, and air pressure. This data is processed using Artificial Neural Networks to accurately predict rainfall in the region. The test results show that the comparison of the actual data results of rainfall prediction using the Levenberg Marquart algorithm with 1,080 training data of 80% data composition, validation data 10 and test data 10 with layer 4 size with layer 10 hidden neural produces predictions with a good level of accuracy and obtains a value of R = 0.900.https://www.bio-conferences.org/articles/bioconf/pdf/2024/63/bioconf_sage-grace2024_01004.pdfartificial neural networkmatlabrainfall |
| spellingShingle | Salsalbilla Septya Riyadi Slamet Zaki Ahmad Nursetiawan Nursetiawan Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural Network BIO Web of Conferences artificial neural network matlab rainfall |
| title | Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural Network |
| title_full | Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural Network |
| title_fullStr | Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural Network |
| title_full_unstemmed | Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural Network |
| title_short | Predicting Rainfall for Farming in the Bantul Region Using an Artificial Neural Network |
| title_sort | predicting rainfall for farming in the bantul region using an artificial neural network |
| topic | artificial neural network matlab rainfall |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/63/bioconf_sage-grace2024_01004.pdf |
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