Comparison of Neural Network and Random Forest Classifier Performance on Dragon Fruit Disease | IEEE Conference Publication | IEEE Xplore

Comparison of Neural Network and Random Forest Classifier Performance on Dragon Fruit Disease


Abstract:

Measuring the performance of several classifiers in the modeling process, based on datasets with certain criteria, becomes important. It used to determine which classifie...Show More

Abstract:

Measuring the performance of several classifiers in the modeling process, based on datasets with certain criteria, becomes important. It used to determine which classifier is more reliable for a particular task. Neural network will be compared its performance against Random Forest using dragon fruit datasets. Consisting of 41 images of healthy and sick fruit and leaf, it is divided into four classes, both classifiers were used in two comparing experiments. The validation used is 10-fold cross-validation. The results obtained are not much different from the prediction accuracy in the range of 70% to 82.9% for both classifiers.
Date of Conference: 29-30 September 2021
Date Added to IEEE Xplore: 08 November 2021
ISBN Information:
Conference Location: Surabaya, Indonesia

References

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