Performance Evaluation of Naive Bayes Algorithm for Classification of Fertilizer Types

Rastri Prathivi, April Firman Daru

Abstract


Determining the right fertilizer is very important to get optimal plant growth results. Each plant requires different nutrient requirements. Different soil types cause the soil's nutrient content and PH value to differ from one type to another. Regional conditions in a place will also cause the need for plant absorption of nutrient content to be more varied. By using the classification of the problems that have been mentioned, it can be solved by studying patterns from existing fertilizer use data into knowledge that can be used to determine decisions. In this study, modeling with the Naïve Bayes algorithm has been applied to the existing fertilizer use data where the probability value of each class has been calculated to get the highest probability value of a class. The measurement of the accuracy value of the modeling used is measured using the Split Validation method, where the training data will be divided into training data and testing data so that the accuracy value of the model is obtained. From the applied modeling, an accuracy value of 60% is obtained, which shows the level of accuracy of the model obtained from the classification results in the form of the name of the fertilizer, which is expected to help in determining the name of the fertilizer that needs to be used.

Keywords


Classification; Naïve Bayes; Fertilizer

Full Text:

Link Download

References


Bhatia, P. (2019). Data Mining and Data Warehousing: Principles and Practical Techniques. Cambridge, United Kingdom: Cambridge University Press. Retrieved from: www.cambridge.org/9781108727747

Han, J., Kamber, M., & Pei, J. (2012). Data Mining : Concepts and Techniques (3rd ed.). Waltham, USA: Morgan Kaufmann Pub. doi: 10.1016/C2009-0-61819-5

Jha, G.K., Ranjan, P. and Gaur, M. (2020). A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture. In Recommender System with Machine Learning and Artificial Intelligence (eds S.N. Mohanty, J.M. Chatterjee, S. Jain, A.A. Elngar and P. Gupta). doi: 10.1002/9781119711582.ch5

R. Priya, D. Ramesh and E. Khosla, (2018) "Crop Prediction on the Region Belts of India: A Naïve Bayes MapReduce Precision Agricultural Model," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 99-104, doi: 10.1109/ICACCI.2018.8554948

Roy, A. H. (2017). Fertilizers and Food Production. In J. A. Kent, T. V. Bommaraju, & S. D. Barnicki, Handbook of Industrial Chemistry and Biotechnology (13th ed.) (pp. 757-804). Cham, Switzerland: Springer International Publishing.

Suntoro, J. (2019). Data Mining : Algoritma dan Implementasi dengan Pemrograman PHP. Jakarta, Indonesia: PT Elex Media Komputindo.

Walida, H., Harahap, F. S., Dalimunthe, B. A., Hasibuan, R., Nasution, A. P., & Sidabukke, S. H. (2020). Pengaruh Pemberian Pupuk Urea Dan Pupuk Kandang Kambing Terhadap Beberapa Sifat Kimia Tanah Dan Hasil Tanaman Sawi Hijau. Jurnal Tanah Dan Sumberdaya Lahan, 7(2), 283–289. doi: https://doi.org/10.21776/ub.jtsl.2020.007.2.12

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data Mining : Practical Machine Learning Tools and Techniques (4th ed.). United States: Morgan Kaufmann.

Zaki, M. J., & Meira, W. (2014). Data Mining and Analysis : Fundamentals Concepts and Algorithms. New York, USA: Cambridge University Press .

Zelle, J. M. (2017). Python Programming: an Introduction to Computer Science (3rd ed.). USA: Franklin, Beedle & Associates.

Wang, J. L., Liu, K. L., Zhao, X. Q., Zhang, H. Q., Li, D., Li, J. J., & Shen, R. F. (2021). Balanced fertilization over four decades has sustained soil microbial communities and improved soil fertility and rice productivity in red paddy soil. Science of The Total Environment, 793, 148664. doi: 10.1016/j.scitotenv.2021.148664

Pahalvi, H. N., Rafiya, L., Rashid, S., Nisar, B., & Kamili, A. N. (2021). Chemical fertilizers and their impact on soil health. In Microbiota and Biofertilizers, Vol 2 (pp. 1-20). Springer, Cham. doi: 10.1007/978-3-030-61010-4_1

Wickramasinghe, I., & Kalutarage, H. (2021). Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Computing, 25(3), 2277-2293. doi: 10.1007/s00500-020-05297-6

Bhatt, M. K., Labanya, R., & Joshi, H. C. (2019). Influence of long-term chemical fertilizers and organic manures on soil fertility-A review. Universal Journal of Agricultural Research, 7(5), 177-188. doi: 10.13189/ujar.2019.070502

Jha, G. K., Ranjan, P., & Gaur, M. (2020). A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture. Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries, 89-99. doi: 10.1002/9781119711582.ch5

De Souza, G. F. M., Melani, A. H. D. A., Michalski, M. A. D. C., & Da Silva, R. F. (Eds.). (2021). Reliability Analysis and Asset Management of Engineering Systems. Elsevier.

Lopes, F., Agnelo, J., Teixeira, C. A., Laranjeiro, N., & Bernardino, J. (2020). Automating orthogonal defect classification using machine learning algorithms. Future Generation Computer Systems, 102, 932-947. doi: 10.1016/j.future.2019.09.009

Akshatha, G. C., & Shastry, K. A. (2022). Crop and Fertilizer Recommendation System Based on Soil Classification. In Recent Advances in Artificial Intelligence and Data Engineering (pp. 29-40). Springer, Singapore. doi: 10.1007/978-981-16-3342-3_3




DOI: http://dx.doi.org/10.35671/telematika.v15i1.1410

Refbacks

  • There are currently no refbacks.


 


Indexed by:

 

Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License .