Implementation Internet of Things for Feeding Catfish Water Quality Analysis Using Linear Regression and K-Nearest Neighbor

Yaddarabullah Yaddarabullah, Egie Hermawan

Abstract


Cultivation of catfish (Clarias Gariepinus) is a promising business field and also a very productive activity because public interest in catfish is high. This factor is observed by market demand for catfish which is increasing from year to year. In catfish farming, you must pay attention to the acidity of the water (pH), temperature, and oxygen levels, which can change if too much feed is given. This can cause catfish seedlings to die and affect the catfish harvest. Catfish farmers often provide excessive food which causes many catfish seeds to die. This research will conduct a study on an Internet Of Things technology that can be used to monitor the acidity level in water pH, temperature, and oxygen levels as well as feed fish. The Internet of Things is very influential for monitoring the quality of catfish ponds by distributing information data resulting from sensor monitoring. The data obtained will be predicted for water quality in the pond by implementing a Linear Regression method. Furthermore, the acquisition of data from the predictions that have been carried out will be processed again to go to the next phase, namely classifying with the K-Nearest Neighbor algorithm method to carry out the identification phase of water types based on the nearest neighbors. This prediction is used to anticipate and notify catfish farmers through applications if there is a water acidity level (pH), temperature, and oxygen and feed levels that have run out

Keywords


Water Quality Analysis, Internet of things, K-Nearest Neighbor

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References


Al-Dosary, N. M. N., Al-Hamed, S. A., & Aboukarima, A. M. (2019). K-NEAREST NEIGHBORS METHOD FOR PREDICTION OF FUEL CONSUMPTION IN TRACTOR-CHISEL PLOW SYSTEMS. Engenharia Agrícola, 39(6), 729–736.

Priyanka, G., Prakash, N., Kishore, A., & S, N. K. (2020). Smart IoT Based Fish Pond Monitoring System to Enhance Fish Cultivation. 29(5), 6408–6417.

Guo, Y., Han, S., Li, Y., Zhang, C., & Bai, Y. (2018). K-Nearest Neighbor combined with guided filter for hyperspectral image classification. Procedia Computer Science, 129, 159–165. https://doi.org/10.1016/j.procs.2018.03.066

Hu, G., Yang, Z., Zhu, M., Huang, L., & Xiong, N. (2018). Automatic classification of insulator by combining k-nearest neighbor algorithm with multi-type feature for the Internet of Things. 1–10.

Fitriani, R., & Vitriani, Y. (2018). The Comparison of Linear Regression Method and K-Nearest Neighbors in Scholarship Recipient. 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 194–199.

Arafat, A. I., Akter, T., Ahammed, F., Ali, Y., & Nahid, A. (2020). A dataset for internet of things based fish farm monitoring and notification system. Data in Brief, 33, 106457. https://doi.org/10.1016/j.dib.2020.106457

Wahla, A. H., Chen, L., Wang, Y., Chen, R., & Wu, F. (2019). Automatic wireless signal classification in multimedia Internet of Things: An adaptive boosting enabled approach. IEEE Access, 7, 160334–160344.

Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324(1). https://doi.org/10.1088/1757-899X/324/1/012049

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250.

Kang, S. (2021). K-nearest neighbor learning with graph neural networks. Mathematics, 9(8). https://doi.org/10.3390/math9080830

Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2888(August), 986–996. https://doi.org/10.1007/978-3-540-39964-3_62

Kurniadi, D., Abdurachman, E., Warnars, H. L. H. S., & Suparta, W. (2018). The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm. IOP Conference Series: Materials Science and Engineering, 434(1). https://doi.org/10.1088/1757-899X/434/1/012039

Nababan, A. A., Sitompul, O. S., & Tulus. (2018). Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio. Journal of Physics: Conference Series, 1007(1). https://doi.org/10.1088/1742-6596/1007/1/012007

Prasath, V. B. S., Alfeilat, H. A. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. E. (2017). Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review. 1–39. https://doi.org/10.1089/big.2018.0175

Taunk, K., De, S., Verma, S., & Swetapadma, A. (2019). A brief review of nearest neighbor algorithm for learning and classification. 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 1255–1260.




DOI: https://doi.org/10.31326/jisa.v5i2.1431

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Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Published by Universitas Trilogi; managed by Prodi Teknik Informatika
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In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE)

 

JISA(Jurnal Informatika dan Sains)Published by Universitas Trilogi; managed by Prodi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.