Class Balancing and Parameter Tuning of Machine Learning Models for Enhancing Aphrodisiac Herbal Plant Classification
Main Article Content
Abstract
Herbal plants with aphrodisiac claims are an important part of traditional medicine that continues to evolve within the modern scientific context. However, the classification process for these plant claims is often done manually and subjectively, necessitating a more objective, data-driven approach. Artificial Intelligence (AI) and its various derivatives, such as Machine Learning, present a reliable solution for several related classification studies. The primary challenge in classification lies in data class imbalance and selecting the optimal model parameters. This study proposes an integrated approach that utilizes machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and XGBoost, combined with SMOTE class balancing techniques and hyperparameter tuning through Grid Search, Random Search, and Bayesian Optimization. Experiments were conducted on a dataset of herbal plants with attributes and labels of aphrodisiac claims, and the results were evaluated based on accuracy, precision, recall, and execution time. The findings indicated that the combinatorial approach significantly improved model performance compared to the basic approach. Among the hyperparameter tuning results, the SVM method achieved the best accuracy (0.889) and precision (0.889). This research contributes to the development of an AI-based classification system in the field of ethnopharmacology. It can serve as a reference for creating scientifically validated databases of herbal plants.
Article Details
How to Cite
Jayadi, P., Bhagawan, W., & Aldida, J. (2025). Class Balancing and Parameter Tuning of Machine Learning Models for Enhancing Aphrodisiac Herbal Plant Classification. Journal of Informatics Information System Software Engineering and Applications (INISTA), 7(2), 183-192. https://doi.org/10.20895/inista.v7i2.1832
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References
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[2] N. Nasim, I. S. Sandeep, and S. Mohanty, “Plant-derived natural products for drug discovery: current approaches and prospects,” The Nucleus, vol. 65, no. 3, pp. 399–411, Dec. 2022, doi: 10.1007/s13237-022-00405-3.
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[4] G. Nouioura, B. Lyoussi, and E. Derwich, “Rekindling desire: Unveiling the Aphrodisiac potential of Apiaceae Elixirs,” Phytomedicine Plus, vol. 4, no. 2, p. 100530, May 2024, doi: 10.1016/j.phyplu.2024.100530.
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[6] I. Süntar, “Importance of ethnopharmacological studies in drug discovery: role of medicinal plants,” Phytochemistry Reviews, vol. 19, no. 5, pp. 1199–1209, Oct. 2020, doi: 10.1007/s11101-019-09629-9.
[7] M. Leonti, “The future is written: Impact of scripts on the cognition, selection, knowledge and transmission of medicinal plant use and its implications for ethnobotany and ethnopharmacology,” J Ethnopharmacol, vol. 134, no. 3, pp. 542–555, Apr. 2011, doi: 10.1016/j.jep.2011.01.017.
[8] F. Jamshidi-Kia, Z. Lorigooini, and H. Amini-Khoei, “Medicinal plants: Past history and future perspective,” Journal of HerbMed Pharmacology, vol. 7, no. 1, pp. 1–7, 2018, doi: 10.15171/jhp.2018.01.
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[12] H. Bouakkaz, M. Bouakkaz, C. A. Kerrache, and S. Dhelim, “Enhanced classification of medicinal plants using deep learning and optimized CNN architectures,” Heliyon, vol. 11, no. 3, p. e42385, 2025, doi: https://doi.org/10.1016/j.heliyon.2025.e42385.
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[17] A. K. Shembo, S. S. Ayichew, I. Stiers, A. Geremew, and L. Carson, “Classification and ordination analysis of wild medicinal plants in Ada’a district, Ethiopia: Implication for sustainable conservation and utilization,” Ecological Frontiers, vol. 44, no. 4, pp. 809–819, 2024, doi: https://doi.org/10.1016/j.ecofro.2024.04.002.
[18] T. P. Tran, F. Ud Din, L. Brankovic, C. Sanin, S. M. Hester, and M. Duc Hoang Le, “Incremental and Zero-Shot Machine Learning for Vietnamese Medicinal Plant Image Classification,” Procedia Comput Sci, vol. 246, pp. 606–615, 2024, doi: https://doi.org/10.1016/j.procs.2024.09.469.
[19] B. R. Pushpa, N. S. Rani, M. Chandrajith, N. Manohar, and S. S. K. Nair, “On the importance of integrating convolution features for Indian medicinal plant species classification using hierarchical machine learning approach,” Ecol Inform, vol. 81, p. 102611, 2024, doi: https://doi.org/10.1016/j.ecoinf.2024.102611.
[20] T. Asafo-Agyei, Y. Appau, K. B. Barimah, and A. Asase, “Medicinal plants used for management of diabetes and hypertension in Ghana,” Heliyon, vol. 9, no. 12, p. e22977, 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e22977.
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[23] J. Bergstra and Y. Bengio, “Random Search for Hyper-Parameter Optimization,” Journal of Machine Learning Research, vol. 13, no. 10, pp. 281–305, 2012, [Online]. Available: http://jmlr.org/papers/v13/bergstra12a.html
[24] W. S. Bhagawan, W. Ekasari, and M. Agil, “Ethnopharmacology of medicinal plants used by the Tenggerese community in Bromo Tengger Semeru National Park, Indonesia,” Biodiversitas, vol. 24, no. 10, Nov. 2023, doi: 10.13057/biodiv/d241028.
[25] W. S. Bhagawan, A. Suproborini, D. L. P. Putri, A. Nurfatma, and R. T. Putra, “Ethnomedicinal study, phytochemical characterization, and pharmacological confirmation of selected medicinal plant on the northern slope of Mount Wilis, East Java, Indonesia,” Biodiversitas, vol. 23, no. 8, Aug. 2022, doi: 10.13057/biodiv/d230855.
[26] W. S. Bhagawan, W. Ekasari, and M. Agil, “Ethnobotanical survey of herbal steam baths among the Tenggerese community in Bromo Tengger Semeru National Park, Indonesia,” IOP Conf Ser Earth Environ Sci, vol. 1352, no. 1, p. 012103, May 2024, doi: 10.1088/1755-1315/1352/1/012103.
[27] Y. Yiblet, “Ethnobotanical study of medicinal plants used to manage human ailments in Lay Gaint District, South Gondar Zone, Amhara Region, Northwestern Ethiopia,” Heliyon, vol. 10, no. 15, p. e35277, 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e35277.
[28] B. Paneru, B. Thapa, and B. Paneru, “Leveraging AI in ayurvedic agriculture: A RAG chatbot for comprehensive medicinal plant insights using hybrid deep learning approaches,” Telematics and Informatics Reports, vol. 16, p. 100181, 2024, doi: https://doi.org/10.1016/j.teler.2024.100181.
[29] L. Breiman, “Random Forests,” Mach Learn, vol. 45, pp. 5–32, 2001, doi: 10.1007/978-3-030-62008-0_35.
[30] D. Shanthini, M. Shanthi, and M. C. Bhuvaneswari, “A Comparative Study of SVM Kernel Functions Based on Polynomial Coefficients and V-Transform Coefficients,” International Journal Of Engineering And Computer Science, vol. 6, no. 6, pp. 2319–7242, 2017, doi: 10.18535/ijecs/v6i3.65.
[31] D.- Andriansyah and Eka Wulansari Fridayanthie, “Optimization of Support Vector Machine and XGBoost Methods Using Feature Selection to Improve Classification Performance,” JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, vol. 6, no. 2, pp. 484–493, Jan. 2023, doi: 10.31289/jite.v6i2.8373.
[32] R. Azadnia, F. Noei-Khodabadi, A. Moloudzadeh, A. Jahanbakhshi, and M. Omid, “Medicinal and poisonous plants classification from visual characteristics of leaves using computer vision and deep neural networks,” Ecol Inform, vol. 82, p. 102683, 2024, doi: https://doi.org/10.1016/j.ecoinf.2024.102683.
[33] M. A. Kiflie, D. P. Sharma, and M. A. Haile, “Deep learning for Ethiopian indigenous medicinal plant species identification and classification,” J Ayurveda Integr Med, vol. 15, no. 6, p. 100987, 2024, doi: https://doi.org/10.1016/j.jaim.2024.100987.