Analysis of the Classification of Environmental Conditions of Chili Plants into Healthy and Unhealthy Categories Using the Decision Tree Algorithm

Authors

  • Ermita Sari Universitas Pahlawan Tuanku Tambusai, Indonesia
  • Noviyanti Noviyanti Universitas Pahlawan Tuanku Tambusai, Indonesia
  • R. Joko Musridho Universitas Pahlawan Tuanku Tambusai, Indonesia
  • Muhammad Rezky Ramadhan Universitas Pahlawan Tuanku Tambusai, Indonesia
  • Vivi Damayanti Universitas Pahlawan Tuanku Tambusai, Indonesia
  • Isvihani Ramadhan Universitas Pahlawan Tuanku Tambusai, Indonesia
  • Fajar Delonda Universitas Pahlawan Tuanku Tambusai, Indonesia
  • M. Irsyadul Fikri Universitas Pahlawan Tuanku Tambusai, Indonesia
  • Amal Alfarizal Universitas Pahlawan Tuanku Tambusai, Indonesia

DOI:

https://doi.org/10.59525/gej.1454

Keywords:

Classification, Chili Plants, Data Mining, Decision Tree J48, Environmental Conditions

Abstract

Changes in various environmental parameters during the cultivation process can have an impact on plant development and have the potential to reduce the quality of the results obtained. Therefore, a method is needed that is able to identify plant conditions quickly, objectively, and accurately. This study aims to build a classification model of chili plant conditions using the Decision Tree J48 algorithm by utilizing environmental parameter data and soil nutrients as the basis for determining healthy and unhealthy plant categories. This research method uses a quantitative approach with data mining techniques on a public dataset consisting of 2,200 datasets. The variables analyzed included nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, soil pH (ph), and rainfall. The process of forming and testing the model was carried out using the WEKA application with the 10-fold cross validation technique. The results showed that moisture was the most dominant attribute in the formation of decision trees, followed by rainfall, temperature, and soil pH as supporting attributes. The resulting model obtained an accuracy rate of 99.5909%, a Kappa Statistic value of 0.989, a Mean Absolute Error (MAE) of 0.0042, and a Root Mean Squared Error (RMSE) of 0.064. These values indicate that the model has an excellent degree of conformity with a relatively low rate of prediction error. Based on these results, the Decision Tree J48 algorithm has proven to be effective in classifying the condition of chili plants based on environmental parameters and has the potential to be applied as the basis for the development of a decision support system to help the process of monitoring plant conditions more quickly, objectively, and accurately.

References

Amir, A., Fachruddin, F., Idris, F., Safriani, M., Saefuddin, R., & Sakti, I. (2025). Perbandingan Model Decision Tree dan Random Forest untuk Penentuan Kesesuaian Lahan Budidaya Cabai dan Terong. 12(5), 695–704. https://doi.org/10.30865/jurikom.v12i5.8672

Amir, A., Fachruddin, F., Idris, F., Safriani, M., Saefuddin, R., & Sakti, I. (2025). Perbandingan model Decision Tree dan Random Forest untuk penentuan kesesuaian lahan budidaya cabai dan terong. JURIKOM, 12(5), 695–704. https://doi.org/10.30865/jurikom.v12i5.8672

Chitradurga Manjunath, M., & Palayyan, B. P. (2023). An efficient crop yield prediction framework using hybrid machine learning model. Revue d'Intelligence Artificielle, 37(4), 1057–1067. https://doi.org/10.18280/ria.370428

European Journal of Agronomy. (2023). Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices. European Journal of Agronomy, 146, 126820. https://doi.org/10.1016/j.eja.2023.126820

Iniyan, S., Varma, V. A., & Naidu, C. T. (2023). Crop yield prediction using machine learning techniques. Advances in Engineering Software, 175, 103326. https://doi.org/10.1016/j.advengsoft.2022.103326

Jhajharia, K., Mathur, P., Jain, S., & Nijhawan, S. (2023). Crop yield prediction using machine learning and deep learning techniques. Procedia Computer Science, 218, 406–417. https://doi.org/10.1016/j.procs.2023.01.023

Kumar, S., et al. (2024). Crop yield prediction using multi-attribute weighted tree-based support vector machine. Measurement: Sensors, 31, 101002. https://doi.org/10.1016/j.measen.2023.101002

Mohan, R. N. V. J., Rayanoothala, P. S., & Sree, R. P. (2025). Next-gen agriculture: Integrating AI and XAI for precision crop yield predictions. Frontiers in Plant Science, 15. https://doi.org/10.3389/fpls.2024.1451607

Morales, A., & Villalobos, F. J. (2023). Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science, 14, 1128388. https://doi.org/10.3389/fpls.2023.1128388

Moussaid, A., El Fkihi, S., Zennayi, Y., et al. (2022). Machine learning applied to tree crop yield prediction using field data and satellite imagery. Informatics, 9(4), 80. https://doi.org/10.3390/informatics9040080

Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H., & Islam, N. (2022). A systematic literature review on crop yield prediction with deep learning and remote sensing. Remote Sensing, 14(9), 1990. https://doi.org/10.3390/rs14091990

Nurfalah, K., Firmansyah, E., Sutara, B., & Sofiyan, Y. (2025). Prediksi Risiko Gagal Panen Cabai Rawit Merah Menggunakan Algoritma Decision Tree. 5(2), 111–120.

Nurfalah, K., Firmansyah, E., Sutara, B., & Sofiyan, Y. (2025). Prediksi risiko gagal panen cabai rawit merah menggunakan algoritma Decision Tree. Jurnal Agritech, 5(2), 111–120.

Pankaj, Bharti, P. K., & Kumar, B. (2023). Crop yield prediction using machine learning: A review of recent approaches. International Journal of Computer Applications, 185(24), 27–32. https://doi.org/10.5120/ijca2023922994

Pavani, S., & Beulet, A. S. (2024). Improved precision crop yield prediction using weighted-feature hybrid SVM. IETE Journal of Research, 70(3). https://doi.org/10.1080/03772063.2023.2192000

Ramadhan, M. R., & Noviyanti. (2025). Penerapan algoritma Decision Tree pada klasifikasi data pertanian berbasis sensor lingkungan. Jurnal Informatika Pertanian, 9(1), 45–56.

Shams, M. Y., Gamel, S. A., & Talaat, F. M. (2024). Enhancing crop recommendation systems with explainable artificial intelligence: A study on agricultural decision-making. Neural Computing and Applications, 36, 5695–5714. https://doi.org/10.1007/s00521-023-09391-2

Sharma, V., Honkavaara, E., Hayden, M., & Kant, S. (2024). UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning. Plant Stress, 12, 100464. https://doi.org/10.1016/j.stress.2024.100464

Talaat, F. M. (2023). Crop yield prediction algorithm ( CYPA ) in precision agriculture based on IoT techniques and climate changes. Neural Computing and Applications, 35(23), 17281–17292. https://doi.org/10.1007/s00521-023-08619-5

Talaat, F. M. (2023). Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes. Neural Computing and Applications, 35(23), 17281–17292. https://doi.org/10.1007/s00521-023-08619-5

Tariq, A., Yan, J., Gagnon, A. S., & Khan, M. R. (2023). Geo-spatial Information Science Mapping of cropland , cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science, 26(3), 302–320. https://doi.org/10.1080/10095020.2022.2100287

Tariq, A., Yan, J., Gagnon, A. S., & Khan, M. R. (2023). Mapping cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-spatial Information Science, 26(3), 302–320. https://doi.org/10.1080/10095020.2022.2100287

Tripathi, A., Rathore, B. S., & Singh, D. (2023). Survey paper on agricultural dataset for improving crop yield prediction using machine learning algorithms. International Journal of Computer Applications, 184(46), 28–34. https://doi.org/10.5120/ijca2023922571

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Published

2026-06-27

How to Cite

Sari, E., Noviyanti, N., Musridho, R. J., Ramadhan, M. R., Damayanti, V., Ramadhan, I., … Alfarizal, A. (2026). Analysis of the Classification of Environmental Conditions of Chili Plants into Healthy and Unhealthy Categories Using the Decision Tree Algorithm. Global Education Journal, 4(2), 303–309. https://doi.org/10.59525/gej.1454

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