Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming
This research conducted by predicting soil moisture using Fuzzy Time Series (FTS) and soil moisture sensor technology on shallot farming. Well-controlled soil moisture affects the shallots and crops growth. It discusses soil moisture prediction and monitoring systems developed through Android-based...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2019-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/51/e3sconf_icenis2019_23002.pdf |
| _version_ | 1818414648950521856 |
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| author | Prehanto Dedy Rahman Indriyanti Aries Dwi Mashuri Chamdan Permadi Ginanjar Setyo |
| author_facet | Prehanto Dedy Rahman Indriyanti Aries Dwi Mashuri Chamdan Permadi Ginanjar Setyo |
| author_sort | Prehanto Dedy Rahman |
| collection | DOAJ |
| description | This research conducted by predicting soil moisture using Fuzzy Time Series (FTS) and soil moisture sensor technology on shallot farming. Well-controlled soil moisture affects the shallots and crops growth. It discusses soil moisture prediction and monitoring systems developed through Android-based mobile programming languages. Input data consists of sensor results obtained from automatic, online, and real-time acquisition using soil moisture sensor technology, then, sent to the server and stored in an online database. Furthermore, data acquisition is predicted using the FTS algorithm that applies a discourse universe to define and determine fuzzy sets. Fuzzy set results are continued to the process of sharing the discourse universe so that it becomes the final step. Prediction results are displayed on the information system dashboard developed. Using 24 data from soil moisture data, the predicted score is 760 at the beginning of 6:00. The results of the prediction are done by validating error deviations using the Mean Square Error of 1.5%. This proves that FTS is good enough in predicting soil moisture and safety to control soil moisture in shallots. For deeper analysis, researchers used various request data and U discourse universe at FTS to obtain various results based on the test data used. |
| first_indexed | 2024-12-14T11:22:27Z |
| format | Article |
| id | doaj.art-0c19d6540aca44deb7a92bc2213e09d3 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-14T11:22:27Z |
| publishDate | 2019-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-0c19d6540aca44deb7a92bc2213e09d32022-12-21T23:03:41ZengEDP SciencesE3S Web of Conferences2267-12422019-01-011252300210.1051/e3sconf/201912523002e3sconf_icenis2019_23002Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot FarmingPrehanto Dedy Rahman0Indriyanti Aries Dwi1Mashuri Chamdan2Permadi Ginanjar Setyo3Informatics Engineering Department, of Engineering Faculty, Surabaya State UniversityInformatics Engineering Department, of Engineering Faculty, Surabaya State UniversityInformation System Department of Information Technology Faculty, Hasyim Asy’ari UniversityInformatics Management Department of Information Technology Faculty, Hasyim Asy’ari UniversityThis research conducted by predicting soil moisture using Fuzzy Time Series (FTS) and soil moisture sensor technology on shallot farming. Well-controlled soil moisture affects the shallots and crops growth. It discusses soil moisture prediction and monitoring systems developed through Android-based mobile programming languages. Input data consists of sensor results obtained from automatic, online, and real-time acquisition using soil moisture sensor technology, then, sent to the server and stored in an online database. Furthermore, data acquisition is predicted using the FTS algorithm that applies a discourse universe to define and determine fuzzy sets. Fuzzy set results are continued to the process of sharing the discourse universe so that it becomes the final step. Prediction results are displayed on the information system dashboard developed. Using 24 data from soil moisture data, the predicted score is 760 at the beginning of 6:00. The results of the prediction are done by validating error deviations using the Mean Square Error of 1.5%. This proves that FTS is good enough in predicting soil moisture and safety to control soil moisture in shallots. For deeper analysis, researchers used various request data and U discourse universe at FTS to obtain various results based on the test data used.https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/51/e3sconf_icenis2019_23002.pdfsoil moisturepredictionfuzzy time seriesmoisture sensor technologyshallot farming |
| spellingShingle | Prehanto Dedy Rahman Indriyanti Aries Dwi Mashuri Chamdan Permadi Ginanjar Setyo Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming E3S Web of Conferences soil moisture prediction fuzzy time series moisture sensor technology shallot farming |
| title | Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming |
| title_full | Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming |
| title_fullStr | Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming |
| title_full_unstemmed | Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming |
| title_short | Soil Moisture Prediction using Fuzzy Time Series and Moisture sensor Technology on Shallot Farming |
| title_sort | soil moisture prediction using fuzzy time series and moisture sensor technology on shallot farming |
| topic | soil moisture prediction fuzzy time series moisture sensor technology shallot farming |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/51/e3sconf_icenis2019_23002.pdf |
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