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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Eduproxima : Jurnal Ilmiah Pendidikan IPA Jurnal Luminous: Riset Ilmiah Pendidikan Fisika Jurnal Pendidikan Abad Ke-21
Hafidah, Siti Nur
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Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Education Physics Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor Setiawan, Iman; Musa, Mohammad Dahlan Th.; Afriza, Dini Aprilia; Hafidah, Siti Nur
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8905

Abstract

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).
Co-Authors Afriza, Dini Aprilia Asep Saefullah Aulia, Winda Biru, Lulu Tunjung Dede Firmansyah Saefudin Iman Setiawan Koimah, Koimah Musa, Mohammad Dahlan Th. Nestiadi, Adi Pratiwi, Nurma Yuni R. Ahmad Zaky El Islami Ramadhan, Amara Dhiva Shifani Rindi, Rindi Rohmawati, Wiwin Sari, Delia Nurpuspita Toebing, Retty Lia Yulianah Yulianah Yuniati, Rania