Penerapan Random Oversampling dan Algoritma Boosting untuk Memprediksi Kualitas Buah Jeruk

Abstract

According to the 2019 data, global orange production has increased significantly, reaching 79 million tons. However, despite the availability of various types of oranges in Indonesian markets, many vendors still sell low-quality oranges. To address this issue, researchers have applied random oversampling and boosting algorithms to predict orange quality, using the public Orange Quality Analysis Dataset. This study uses random oversampling to address data imbalance and combines it with boosting algorithms like Adaboost, Gradient Boosting, Light GBM, and CatBoost. The data features considered include size, weight, sweetness level, acidity level, and others. The accuracy of the boosting algorithms used varied, with CatBoost showing the highest accuracy rate of 91.42%. The hope is that this research can help orange producers create high-quality products and reduce the occurrence of low-quality oranges, ultimately providing consumers with better oranges. Additionally, this can help producers market their oranges both domestically and internationally.