Leveraging artificial neural networks for optimizing Cinnamomum Sintoc essential oil production in Mount Ciremai

Cinnamomum sintoc is a plant renowned for its production of high-quality essential oils. This study assessed the essential oil content in C. sintoc based on its morphological characteristics, environmental conditions, and soil nutrient composition. A mixed-methods approach was employed, with observational data collected from the Mount Ciremai National Park and analytical data obtained from soil laboratory tests. To analyze the data, Artificial Neural Networks (ANN), specifically ANFIS and MICMAC, were utilized for tasks such as pattern recognition, classification, and prediction. The results indicated that both environmental conditions and C. sintoc morphology have a direct influence on essential oil content, with soil nutrients identified as the most significant factor. The ANFIS analysis, with a prediction accuracy of 96%, identified the optimal nutrient levels for essential oil production: C/N ratio greater than 9, Mg at 0.5, C-organic above 6, N-total at 0.7, K at 0.6, P2O5 between 5 and 9, Ca below 5, and soil pH at 5.5. These findings suggest that the guidelines developed through ANFIS can be further refined into practical rules for optimizing the utilization of C. sintoc plants, potentially improving efficiency in essential oil production.