Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification
This study investigates the application of machine learning models to predict plant growth milestones based on environmental and treatment data. The dataset comprises categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical variables including sunlight ho...
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my-inti-eprints.20552024-11-26T06:58:49Z http://eprints.intimal.edu.my/2055/ Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani QA75 Electronic computers. Computer science QA76 Computer software QK Botany T Technology (General) This study investigates the application of machine learning models to predict plant growth milestones based on environmental and treatment data. The dataset comprises categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical variables including sunlight hours, temperature, and humidity. Preprocessing involved one-hot encoding for categorical variables and standard scaling for numerical features. The models employed were Support Vector Machine (SVM), Naive Bayes, and Extreme Learning Machine (ELM). The baseline SVM model achieved an accuracy of 58.97%, and hyperparameter tuning using GridSearchCV did not improve this performance, maintaining the accuracy at 58.97%. The Naive Bayes model achieved an accuracy of 51.28%, while the ELM model had an accuracy of 43.85%. Among the models, the SVM demonstrated the highest accuracy, though further improvement is required for practical implementation. The findings underscore the importance of selecting appropriate machine learning models and optimizing their parameters to enhance prediction accuracy in agricultural applications. Despite the SVM's superior performance in this context, continued refinement is essential to address the challenges posed by predicting plant growth milestones accurately. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2055/1/jods2024_56.pdf text en cc_by_4 http://eprints.intimal.edu.my/2055/2/596 M., Muflih and Silvia, Ratna and Haldi, Budiman and Usman, Syapotro and Muhammad, Hamdani (2024) Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification. Journal of Data Science, 2024 (56). pp. 1-6. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
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QA75 Electronic computers. Computer science QA76 Computer software QK Botany T Technology (General) M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth Classification |
description |
This study investigates the application of machine learning models to predict plant
growth milestones based on environmental and treatment data. The dataset comprises
categorical variables such as soil type, water frequency, and fertilizer type, alongside numerical
variables including sunlight hours, temperature, and humidity. Preprocessing involved one-hot
encoding for categorical variables and standard scaling for numerical features. The models
employed were Support Vector Machine (SVM), Naive Bayes, and Extreme Learning Machine
(ELM). The baseline SVM model achieved an accuracy of 58.97%, and hyperparameter tuning
using GridSearchCV did not improve this performance, maintaining the accuracy at 58.97%.
The Naive Bayes model achieved an accuracy of 51.28%, while the ELM model had an
accuracy of 43.85%. Among the models, the SVM demonstrated the highest accuracy, though
further improvement is required for practical implementation. The findings underscore the
importance of selecting appropriate machine learning models and optimizing their parameters
to enhance prediction accuracy in agricultural applications. Despite the SVM's superior
performance in this context, continued refinement is essential to address the challenges posed
by predicting plant growth milestones accurately. |
format |
Article |
author |
M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani |
author_facet |
M., Muflih Silvia, Ratna Haldi, Budiman Usman, Syapotro Muhammad, Hamdani |
author_sort |
M., Muflih |
title |
Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth
Classification |
title_short |
Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth
Classification |
title_full |
Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth
Classification |
title_fullStr |
Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth
Classification |
title_full_unstemmed |
Comparison of SVM, Naive Bayes, and ELM Models in Plant Growth
Classification |
title_sort |
comparison of svm, naive bayes, and elm models in plant growth
classification |
publisher |
INTI International University |
publishDate |
2024 |
url |
http://eprints.intimal.edu.my/2055/1/jods2024_56.pdf http://eprints.intimal.edu.my/2055/2/596 http://eprints.intimal.edu.my/2055/ http://ipublishing.intimal.edu.my/jods.html |
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13.222552 |