Gradient Boosting Machine, Random Forest dan Light GBM untuk
Klasifikasi Kacang Kering

Gradient Boosting Machine, Random Forest dan Light GBM untuk
Klasifikasi Kacang Kering

GBM, RF, LightGBM, Bean Classification, BoxCox

Bean seed classification is critical in determining the quality of beans. Previously, the same dataset was tested using the MLP,
SVM, KNN, and DT algorithms, with SVM producing the best results. The purpose of this study is to determine the most effective
model through the use of the BoxCox transformation selection feature and the random forest (RF) algorithm, as well as the
gradient boosting machine (GBM), light GBM, and repeated k-folds evaluation model. The bean dataset is available on the
UCI Repository website. The BoxCox transformation and repeated k-folds improved the classification prediction's accuracy.
The model is used in the optimal training phase for a random forest with decision tree parameters 50 and depth 10, a gradient
boosting machine model with a learning rate of 1, and a light gradient boosting machine model with a learning rate of 0.5 and
estimator of 500. The best training accuracy results are obtained with light GBM. which is 99 percent accurate, but only 91
percent accurate in terms of validation. According research, the Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira
beans classes provided accuracy values of 91 percent, 100 percent, 92 percent, 92 percent, 95 percent, 94 percent, and 84
percent, respectively

Indrawata Wardhana1
, Musi Ariawijaya2
, Vandri Ahmad Isnaini3
, Rahmi Putri Wirman4

UIN Sulthan Thaha Saifuddin Jambi

27 februari 2022

Fajar bagus W

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Indonesia

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