Claims regarding the effectiveness of aphrodisiac herbal plants circulating widely in society are often unaccompanied by adequate scientific evidence. Responding to this issue, a research team from Universitas PGRI Madiun (UNIPMA) presents an innovation based on cutting-edge technology to improve the accuracy of information received by the public. Through the 2025 Novice Lecturer Research (PDP) scheme, they designed a Machine Learning (ML) classification model to filter and validate claims of aphrodisiac herbal plants more objectively.

The team is chaired by Puguh Jayadi, S.Kom., M.Kom., a Lecturer in Informatics Engineering, in collaboration with Apt. Weka Sidha Bhagawan, M.Farm., a lecturer from the Pharmacy Study Program, and Jofanza Denis Aldida, an Informatics Engineering student.

According to Puguh Jayadi, S.Kom., M.Kom., the urgency of this research arose due to the widespread use of aphrodisiac herbs in traditional medicine without strong scientific data support. "A systematic data-driven approach is needed to evaluate the validity of these claims," he said yesterday, Monday (November 24).

The developed ML model aims to classify claims based on their level of validity. The results are expected to serve as an objective reference for health practitioners, researchers, the herbal industry, and the public who rely on ethnobotanical references. The research process involved collecting a comprehensive dataset ranging from scientific journals to herbal user discussion forums, which were then processed through a cross-disciplinary approach of Informatics Engineering and Pharmacy.

In the modeling stage, the team will apply three classification algorithms known to be effective in handling complex data: Random Forest, Support Vector Machine (SVM), and XGBoost. These three were chosen to see the best performance in reading ethnobotanical data patterns. Model evaluation is carried out using accuracy, precision, recall, and F1-score metrics, with a minimum target of 85 percent accuracy and an F1-score above 0.8.

Puguh Jayadi, S.Kom., M.Kom., emphasized that this research is part of a long-term multidisciplinary research roadmap for 2024–2029. "In the future, it will be developed with cutting-edge applied AI technology," he explained.

The main targeted outputs include an ML classification model specifically adapted for aphrodisiac herbal plant data, SINTA 4 accredited national publications, national seminar proceedings, and scientific posters. This research is expected to make a real contribution to increasing the transparency and credibility of herbal plant claims, while strengthening the foundation of data-driven research in the field of herbal science.

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