Comparison Naïve Bayes and SVM to Classify Drought-Infected Rice Plants Based on Morphological Characteristics in Supporting National Food Security

Authors

  • Damaris Easter Nugrahita Christi Department of Mathematics, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia, Indonesia
  • Angelia Melisa Hutapea Department of Biology, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia, Indonesia
  • Fulkan Kafilah Al Husein Department of Mathematics, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia, Indonesia http://orcid.org/0009-0007-2186-1722
  • Nadiza Lediwara Department of Informatics, Faculty Defense Sciences and Technology, Republic of Indonesia Defense University, Bogor, Indonesia, Indonesia
  • Sembada Denrineksa Bimorogo Department of Informatics, Faculty Defense Sciences and Technology, Republic of Indonesia Defense University, Bogor, Indonesia, Indonesia
  • Rizky Dwi Satrio Department of Biology, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia, Indonesia

Keywords:

Rice Plant, Modelling, SVM, Naïve Bayes

Abstract

Data mining is part of the Knowledge Discovery in Database (KDD) process. The use of data mining serves to classify, predict, and extract other useful information from large data sets. This study aimed to classify rice plants under treatment (drought stress and control) using data mining, focusing on the analysis of the variables of Leaf Area (LA), Root Length (RL), and Shoot Length (SL). Each classification algorithm has different characteristics, resulting in varied performance results. After testing both classification algorithms, the accuracy results were 71.70% for Naïve Bayes and 73.85% for SVM. This shows that the SVM algorithm performs better than Naïve Bayes algorithms to determine best treatment of rice to support national food security further. Furthermore, It also can be concluded that using a machine learning approach can solve problems in the classification of rice plants affected by drought threats is fairly effective with the maximum score obtained is only 73.85%.

Author Biographies

Damaris Easter Nugrahita Christi, Department of Mathematics, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia

             

Angelia Melisa Hutapea, Department of Biology, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia

                  

Fulkan Kafilah Al Husein, Department of Mathematics, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia

                 

Nadiza Lediwara, Department of Informatics, Faculty Defense Sciences and Technology, Republic of Indonesia Defense University, Bogor, Indonesia

                

Sembada Denrineksa Bimorogo, Department of Informatics, Faculty Defense Sciences and Technology, Republic of Indonesia Defense University, Bogor, Indonesia

               

Rizky Dwi Satrio, Department of Biology, Faculty of Military Mathematics and Natural Sciences, Republic of Indonesia Defense University, Bogor, Indonesia

         

References

Iqbal, A., Fahad, S., Iqbal, M., Alamzeb, M., Ahmad, A., Anwar, S., Khan, A. A., Amanullah, Arif, M., Inamullah, Shaheenshah, Saeed, M., & Song, M. (2020). Special Adaptive Features of Plant Species in Response to Drought (pp. 77–118). https://doi.org/10.1007/978-3-030-40277-8_4

Miftahudin, Putri, R. E., & Chikmawati, T. (2020). Vegetative morphophysiological responses of four rice cultivars to drought stress. Biodiversitas, 21(8), 3727–3734. https://doi.org/10.13057/biodiv/d210840

S. Pulwarty, R., & Sivakumar, M. V. K. (2014). Information systems in a changing climate: Early warnings and drought risk management. Weather and Climate Extremes, 3, 14–21. https://doi.org/10.1016/j.wace.2014.03.005

Satrio, R. D., Fendiyanto, M. H., Supena, E. D. J., Suharsono, & Miftahudin. (2019). Identification of drought-responsive regulatory genes by hierarchical selection of expressed sequence tags and their expression under drought stress in rice. International Journal of Agriculture and Biology, 22(6), 1524–1532. https://doi.org/10.17957/IJAB/15.1230

Satrio, R. D., Fendiyanto, M. H., Supena, E. D. J., Suharsono, S., & Miftahudin, M. (2021). Genome-wide SNP discovery, linkage mapping, and analysis of QTL for morpho-physiological traits in rice during vegetative stage under drought stress. Physiology and Molecular Biology of Plants, 27(11), 2635–2650. https://doi.org/10.1007/s12298-021-01095-y

Tarek H. M. Abou-El-Enien et al. (2015). A Comparison among Support Vector Machine and other Machine Learning Classification Algorithms. IPASJ International Journal of Computer Science, , 3(5), 25–35.

Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256

Mohidem, N. A., Hashim, N., Shamsudin, R., & Man, H. C. (2022, June 1). Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process and Nutrient Content. Agriculture (Switzerland). MDPI. https://doi.org/10.3390/agriculture12060741

Paiman, Ardiyanta, Ansar, M., Effendy, I., & Sumbodo, B. T. (2020). Rice cultivation of superior variety in swamps to increase food security in Indonesia. Reviews in Agricultural Science. Gifu University - United Graduate School of Agricultural Science. https://doi.org/10.7831/ras.8.0_300

Wang, X. (2022, April 1). Managing Land Carrying Capacity: Key to Achieving Sustainable Production Systems for Food Security. Land. MDPI. https://doi.org/10.3390/land11040484

Toulotte, J. M., Pantazopoulou, C. K., Sanclemente, M. A., Voesenek, L. A. C. J., & Sasidharan, R. (2022, February 1). Water stress resilient cereal crops: Lessons from wild relatives. Journal of Integrative Plant Biology. John Wiley and Sons Inc. https://doi.org/10.1111/jipb.13222

Rico-Chávez, A. K., Franco, J. A., Fernandez-Jaramillo, A. A., Contreras-Medina, L. M., Guevara-González, R. G., & Hernandez-Escobedo, Q. (2022, April 1). Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management. Plants. MDPI. https://doi.org/10.3390/plants11070970

Alfred, R., Obit, J. H., Chin, C. P. Y., Haviluddin, H., & Lim, Y. (2021). Towards paddy rice smart farming: A review on big data, machine learning, and rice production tasks. IEEE Access. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3069449

Verma, V., Vishal, B., Kohli, A., & Kumar, P. P. (2021, November 1). Systems-based rice improvement approaches for sustainable food and nutritional security. Plant Cell Reports. Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s00299-021-02790-6

Narayan, Y. (2020). Comparative analysis of SVM and Naive Bayes classifier for the SEMG signal classification. In Materials Today: Proceedings (Vol. 37, pp. 3241–3245). Elsevier Ltd. https://doi.org/10.1016/j.matpr.2020.09.093

Abdullah, D. M., & Abdulazeez, A. M. (2021). Machine Learning Applications based on SVM Classification: A Review. Qubahan Academic Journal, 1(2), 81–90. https://doi.org/10.48161/qaj.v1n2a50

Anggrawan, A., Hairani, H., & Satria, C. (2023). Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE. International Journal of Information and Education Technology, 13(2), 289–295. https://doi.org/10.18178/ijiet.2023.13.2.1806

Yasar, A., & Saritas, M. M. (2019). Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification. International Journal of Intelligent Systems and Applications in Engineering, 7(2), 88–91.

https://doi.org/10.18201/ijisae.2019252786

Lubis, C. P., Rosnelly, R., Roslina, R., Situmorang, Z., & Wanayumini, W. (2021). PENERAPAN METODE NAÏVE BAYES DAN C4.5 PADA PENERIMAAN PEGAWAI DI UNIVERSITAS POTENSI UTAMA. CSRID (Computer Science Research and Its Development Journal), 12(1), 51. https://doi.org/10.22303/csrid.12.1.2020.51-62

Riyadi, S., Siregar, M. M., Margolang, K. fadhli F., & Andriani, K. (2022). ANALYSIS OF SVM AND NAIVE BAYES ALGORITHM IN CLASSIFICATION OF NAD LOANS IN SAVE AND LOAN COOPERATIVES. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 8(3), 261–270. https://doi.org/10.33330/jurteksi.v8i3.1483

Saptadi, N. T. S., Suyuti, A., Ilham, A. A., & Nurtanio, I. (2023). Modeling of Organic Waste Classification as Raw Materials for Briquettes using Machine Learning Approach. International Journal of Advanced Computer Science and Applications, 14(3), 577–585. https://doi.org/10.14569/IJACSA.2023.0140367

Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192.https://doi.org/10.1016/j.aci.2018.08.003

Chen, R. C., Dewi, C., Huang, S. W., & Caraka, R. E. (2020). Selecting critical features for data classification based on machine learning methods. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00327-4

Chen, F., Yu, L., Mao, J., Yang, Q., Wang, D., & Yu, C. (2024). A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics. Applied Energy, 373, 123854.

Rácz, A., Bajusz, D., & Héberger, K. (2021). Effect of dataset size and train/test split ratios in qsar/qspr multiclass classification. Molecules, 26(4). https://doi.org/10.3390/molecules26041111

Downloads

Published

2025-07-01

How to Cite

Christi, D. E. N., Hutapea, A. M., Al Husein, F. K., Lediwara, N., Bimorogo, S. D., & Satrio, R. D. (2025). Comparison Naïve Bayes and SVM to Classify Drought-Infected Rice Plants Based on Morphological Characteristics in Supporting National Food Security. MUNISI: Military Mathematics and Natural Sciences, 3(1), 8–19. Retrieved from https://upgradeojs.rifqisyams.id/index.php/munisi/article/view/19748

Issue

Vol. 3 No. 1 (2025)

Section

Article of Research

Citation Check

Similar Articles

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)