Random Forest Analysis In Classifying Orange Quality Data - Suci Ramadhani; Muslimin B; Ida Maratul Khamidah (Jurnal Info Sains : Informatika dan Sains, 2024)
The quality of oranges is important to determine selling value. However, citrus quality assessments are often subjective and inconsistent, which can impact consumer satisfaction and market efficiency. In the agricultural industry, especially in citrus commodities, there are difficulties in classifying fruit quality accurately and efficiently, which has an impact on the assessment and determination of market prices. Given the importance of citrus quality in the agricultural and food industries, there is an urgent need for objective and efficient methods for classifying citrus quality. Inappropriate classification can cause economic losses for farmers and distributors, as well as reduce consumer satisfaction with product quality. As a solution, this research proposes the use of the Random Forest method to classify orange quality data. The method used in this research involved collecting orange quality data, including characteristics such as color, texture, and size. This data is then analyzed using the Random Forest algorithm. The Random Forest method is used to process orange quality data, by utilizing features such as color, size and skin texture. This model is trained using historical data to predict fruit quality. The research results show that the Random Forest method successfully classifies citrus quality data with high accuracy, demonstrating its potential as an effective tool for future citrus quality assessment by proving its effectiveness in supporting decisions in the agricultural sector.