Prediction of Potential Natural Antibiotics Plants Based on Jamu Formula Using Random Forest Classifier

Abstract

Jamu is the traditional Indonesian herbal medicine system that is considered to have many benefits such as serving as a cure for diseases or maintaining sound health. A Jamu medicine is generally made from a mixture of several herbs. Natural antibiotics can provide a way to handle the problem of antibiotic resistance. This research aims to discover the potential of herbal plants as natural antibiotic candidates based on a machine learning approach. Our input data consists of a list of herbal formulas with plants as their constituents. The target class corresponds to bacterial diseases that can be cured by herbal formulas. The best model has been observed by implementing the Random Forest (RF) algorithm. For 10-fold cross-validations, the maximum accuracy, recall, and precision are 91.10%, 91.10%, and 90.54% with standard deviations 1.05, 1.05, and 1.48, respectively, which imply that the model obtained is good and robust. This study has shown that 14 plants can be potentially used as natural antibiotic candidates. Furthermore, according to scientific journals, 10 of the 14 selected plants have direct or indirect antibacterial activity.

Analysis of Electrochemically Active Substances in Malvaceae Leaves via Electroanalytical Sensing Technology for Species Identification

“…The performance of the RF model is mainly evaluated by n. estimators. It represents the number of decision trees, and the number and recognition accuracy generally show a positive correlation, but the stability of the model will decline [ 44 ]. The original electrochemical fingerprints of all species and the second derivative electrochemical fingerprints ( Figure 3 and Figure 4 ) were used to learn the three models.…”

Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds

“…The KNApSAcK database (DB) contains information on the species–metabolite relationship (101.500), encompassing 20,741 species and 50,048 metabolites. This database also contains information on accurate mass, molecular formula, metabolite name, and mass spectra in several ionization modes [ 10 ]. IJAH Analytics is an open-access database specifically for Jamu data.…”

“…We also compared the prediction performance of the DNN with other machine learning methods. Moreover, we determined significant metabolites based on target diseases/therapeutic usage of the Unani formula according to the best prediction model, and validated the result based on journal references and counting the structural similarity with relevant metabolites [ 10 ]. Hence, these results can be used as a reference to other research and basic knowledge of drug discovery.…”

The Business Development Strategy of Jamu at PT. Firdaus Kurnia Indah (FKI) in Bangkalan Regency

“…Perkembangan industri jamu semakin pesat karena kebutuhan masyarakat akan hidup sehat semakin tinggi yang menjadi pangsa pasar positif (Batubara et al, 2020). Pengembangan jamu dinilai sangat menguntungkan karena masih banyaknya tanaman herbal yang tersebar di Indonesia (Nasution et al, 2022dan Elfahmi et al, 2014. Pemanfaatan jamu banyak digunakan sebagai pilihan untuk memperkuat imun tubuh seseorang (Kusumo et al, 2020).…”