D Decision Tree-Based Classification of Guimaras Mango Varieties Using Fruit and Stone Attributes with the J48 Algorithm
DOI:
https://doi.org/10.30865/jurikom.v15i5.10156Keywords:
Guimaras Mango, J48 Algorithm, Decision Tree, Variety Classification, Machine LearningAbstract
Guimaras mango varieties have overlapping physical characteristics that complicate manual identification. This exploratory study compares three J48 decision-tree configurations using 11 recorded observations (one per variety) and 17 candidate predictors describing fruit and stone attributes in WEKA. Model A uses pruning with a confidence factor of 0.25 and a minimum leaf size of two; Model B uses the same confidence factor with a minimum leaf size of one; Model C is unpruned with a minimum leaf size of one. The trees contain 7, 19, and 22 nodes, with 5, 13, and 15 leaves, respectively. Peel color is the root attribute in all three configurations; subsequent splits also use fruit weight, flesh color, peel thickness, shape, and, in Model C, peel texture. Evaluation on the training observations produces 45.45% accuracy (5/11) for Model A and 100% (11/11) for Models B and C. These are resubstitution results, not validated estimates of performance on unseen specimens. Stone predictors were available but were not selected in the fitted trees. This study demonstrates interpretable, parameter-sensitive descriptions of the recorded classes; independently replicated fruit samples are needed before generalization or practical varietal authentication can be established.
References
[1] N. J. J. E. Sacramento and G. C. D. Cañete, “Is It Worth Enduring? Labor in Philippine Mango Fruit Farming,” Agrarian South: Journal of Political Economy, vol. 12, no. 3, pp. 295–318, 2023, doi: 10.1177/22779760231188196.
[2] M. Rizzo, M. Marcuzzo, A. Zangari, A. Gasparetto, and A. Albarelli, “Fruit ripeness classification: A survey,” Artificial Intelligence in Agriculture, vol. 7, pp. 44–57, 2023, doi: 10.1016/j.aiia.2023.02.004.
[3] J. J. Bird, C. M. Barnes, L. J. Manso, A. Ekárt, and D. R. Faria, “Fruit quality and defect image classification with conditional GAN data augmentation,” Scientia Horticulturae, vol. 293, Art. no. 110684, 2022, doi: 10.1016/j.scienta.2021.110684.
[4] I. Ahmad et al., “Automated Mango Variety Classification Using Deep Feature Extraction and Machine Learning Classifier Integration,” Foods, vol. 15, no. 3, Art. no. 414, 2026, doi: 10.3390/foods15030414.
[5] X. Wang, X. Chen, R. Gong, T. Wang, and Y. Huang, “Improving fruit variety classification using near-infrared spectroscopy and deep learning techniques,” Journal of Food Composition and Analysis, vol. 140, Art. no. 107243, 2025, doi: 10.1016/j.jfca.2025.107243.
[6] D. Worasawate, P. Sakunasinha, and S. Chiangga, “Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach,” AgriEngineering, vol. 4, no. 1, pp. 32–47, 2022, doi: 10.3390/agriengineering4010003.
[7] N. D. Tai, W. C. Lin, N. M. Trieu, and N. T. Thinh, “Development of a Mango-Grading and -Sorting System Based on External Features, Using Machine Learning Algorithms,” Agronomy, vol. 14, no. 4, Art. no. 831, 2024, doi: 10.3390/agronomy14040831.
[8] M. R. Mustaffa, A. A. Idris, L. N. Abdullah, and N. A. Nasharuddin, “Deep learning mango fruits recognition based on TensorFlow Lite,” International Journal of Advances in Intelligent Informatics, vol. 9, no. 3, pp. 565–576, 2023, doi: 10.26555/ijain.v9i3.1368.
[9] Z. E. Fitri, R. Aprilia, A. Madjid, and A. M. N. Imron, “Ensiklopedia Digital Berdasarkan Klasifikasi Varietas Buah Mangga (Mangifera spp.) Menggunakan Algoritma Backpropagation,” Komputika: Jurnal Sistem Komputer, vol. 11, no. 2, pp. 113–120, 2022, doi: 10.34010/komputika.v11i2.5513.
[10] R. Aulia and W. Husna, “Klasifikasi Jenis Buah Mangga Menggunakan Convolutional Neural Network (CNN) Berbasis Citra Digital,” JITSI: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 6, no. 4, pp. 417–421, 2025, doi: 10.62527/jitsi.6.4.473.
[11] E. Tapia-Mendez, I. A. Cruz-Albarran, S. Tovar-Arriaga, and L. A. Morales-Hernandez, “Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables,” Applied Sciences, vol. 13, no. 22, Art. no. 12504, 2023, doi: 10.3390/app132212504.
[12] J. Duan, L. Lai, Z. Yang, Z. Luo, and H. Yuan, “Multi-feature language-image model for fruit quality image classification,” Computers and Electronics in Agriculture, vol. 227, Art. no. 109462, 2024, doi: 10.1016/j.compag.2024.109462.
