Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor
Measuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user bu...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-01-01
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| Series: | Journal of Applied Informatics and Computing |
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| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8905 |
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| author | Iman Setiawan Mohammad Dahlan Th. Musa Dini Aprilia Afriza Siti Nur Hafidah |
| author_facet | Iman Setiawan Mohammad Dahlan Th. Musa Dini Aprilia Afriza Siti Nur Hafidah |
| author_sort | Iman Setiawan |
| collection | DOAJ |
| description | Measuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user but lacks in accuracy. This research aims to compare and identify the best machine learning model that can improve indirect measurement (soil moisture sensor prediction) using direct measurement (gravimetric test) as a response variable. This research uses linear regression, K-Nearest Neighbours (KNN) and Decision Tree models. The three models were then compared based on Root Mean Square Error (RMSE). The results suggested that KNN (0.02939128) had the smallest RMSE value followed by decision tree (0.05144186) and linear regression model (0.05172371). |
| format | Article |
| id | doaj-art-28dd0a1ca6d14f758785eb894b2b8056 |
| institution | OA Journals |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-28dd0a1ca6d14f758785eb894b2b80562025-08-20T02:15:32ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-01-019114014510.30871/jaic.v9i1.89056502Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture SensorIman Setiawan0Mohammad Dahlan Th. Musa1Dini Aprilia Afriza2Siti Nur Hafidah3Statistic Study Program, Tadulako UniversityGeophysical Engineering, Tadulako UniversityStatistic Study Program, Tadulako UniversityStatistic Study Program, Tadulako UniversityMeasuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user but lacks in accuracy. This research aims to compare and identify the best machine learning model that can improve indirect measurement (soil moisture sensor prediction) using direct measurement (gravimetric test) as a response variable. This research uses linear regression, K-Nearest Neighbours (KNN) and Decision Tree models. The three models were then compared based on Root Mean Square Error (RMSE). The results suggested that KNN (0.02939128) had the smallest RMSE value followed by decision tree (0.05144186) and linear regression model (0.05172371).https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8905soil moisture sensormachine learningregressionknndecision tree |
| spellingShingle | Iman Setiawan Mohammad Dahlan Th. Musa Dini Aprilia Afriza Siti Nur Hafidah Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor Journal of Applied Informatics and Computing soil moisture sensor machine learning regression knn decision tree |
| title | Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor |
| title_full | Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor |
| title_fullStr | Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor |
| title_full_unstemmed | Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor |
| title_short | Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor |
| title_sort | comparing machine learning algorithms to enhance volumetric water content prediction in low cost soil moisture sensor |
| topic | soil moisture sensor machine learning regression knn decision tree |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8905 |
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