Knowledge Discovery in Database dengan Multivariate Linear Regression pada Sistem Pertanian Hidroponik Berbasis Internet of Things
DOI:
https://doi.org/10.61769/telematika.v17i2.542Keywords:
hydroponic, multivariate linear regression, knowledge discovery, correlation, Statistical Package for Social Sciences (SPSS)Abstract
Hydroponic system depends on some important variables for growing the plants such as temperature, humidity, light intensity, and water pH. There have been many developments in monitoring systems and variable control but they are limited to manual controlling systems. The existing automatic system still utilizes an open system with a threshold as the response trigger system. However, the system could not make corrections when needed until the threshold is achieved. On the other hand, the adaptive system can make corrections based on the feedback to be more responsive to ongoing changes. This research aims at designing the feedback model by discovering the correlation of nutrient concentration as the dependent variable with pH variable, light intensity, humidity, and temperature as the independent variable or predictor using Knowledge Discovery in Database method and multivariate linear regression analysis. The outcome of this result is the mathematical model of the multivariable linear equation describing the relations between the dependent variable and independent variables using the software IBM Statistical Package for Social Sciences. The final finding indicates that the ratio of F(116, 119)=8.390, p-value 0,000 which is less than 0,05 proved that the independent variables are able to predict significantly linearly dependent variables with a standard error of 5.6%. The Air_Temp contributes the most significant independent variable to the dependent variable TDS with a p-value of 0.015.
References
Susilawati, Dasar-dasar Bertanam Secara Hidroponik, Unsri Press, 2019.
S. A. Pohan dan Oktoyournal, “Pengaruh konsentrasi nutrisi A-B mix terhadap pertumbuhan caisim secara hidroponik (drip system),” Lumbung, vol 18, no. 1, hlm.20-32, Januari 2019.
M. Singgih, K. Prabawati dan D. Abdulloh, “Bercocok tanam mudah dengan sistem hidroponik NFT,” Jurnal Karya Pengabdian Dosen dan Mahasiswa, vol. 3, no. 1, hlm. 21-24, Januari 2019.
R. L. Alam, dan A. Nasuha, “Sistem pengendali pH dan pemantauan lingkungan tanaman hidroponik menggunakan fuzzy logic controller berbasis IoT,” Electronics, Informatic, and Vocational Education (Ellinvo), vol. 5, no. 1, hlm.11-20, Mei 2020.
D. T. Larose dan C. D. Larose, Discovering Knowledge in Data - An Introduction to Data Mining, Wiley, 2014.
C. K. Nwagu, O. C. Omankwu, dan H. Inyiama, “Knowledge discovery in databases (KDD): an overwiew,” International Journal of Computer Science and Information Security, vol. 15, no.12, hlm. 13-16, Desember 2017.
A. C. Wardhana, Y. Nurhadryani, dan S. Wahjuni, “Knowledge management system berbasis web tentang budidaya hidroponik untuk mendukung smart society,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 3, hlm. 619-628, Juni 2020.
B. Haryanto, N. Ismail, dan E. J. Pristianto, “Sistem monitoring suhu dan kelembapan secara nirkabel pada budidaya tanaman hidroponik,” JTERA - Jurnal Teknologi Rekayasa, vol. 3, no. 1, hlm. 47-54, Juni 2018.
S. N. Sholihat, M. R. Kirom, dan I. W. Fathonah, “Pengaruh kontrol nutrisi pada pertumbuhan kangkung dengan metode hidroponik nutrient film technique (NFT),” e-Proceeding of Engineering, vol. 5, no. 1, hlm. 910–915, Maret 2018, Bandung, Indonesia [Daring]. Tersedia: Telkom University Open Library, https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/19120. [20 Des 2022].
M. N. Williams, C. A. G. Grajales, dan D. Kurkiewicz, “Assumptions of multiple regression: correcting two misconceptions”, Practical Assessment, Research and Evaluation, vol. 18, no. 11, Sept 2013.
C. P. Dhakal, “Interpreting the basic outputs (SPSS) of multiple linear regression”, International Journal of Science and Research (IJSR), vol. 8, no. 6, hlm. 1448–1452, Juni 2019.
R: a language and environment statistical computing. R Foundation for Statistical Computing (2021), R Core Team. [Daring]. Tersedia: https://www.r-project.org/
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Yoyok Gamaliel, Tunggul A. Nugroho, Lewinna C. Aguskin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
