TY - GEN
T1 - Comparison of Neural Network and Random Forest Classifier Performance on Dragon Fruit Disease
AU - Lado, Anita Jaquiline
AU - Sooai, Adri Gabriel
AU - Mamulak, Natalia Magdalena Rafu
AU - Nani, Paskalis Andrianus
AU - Bria, Yulianti Paula
AU - Batarius, Patrisius
AU - Aliandu, Paulina
AU - Ngaga, Emerensiana
AU - Sinlae, Alfry Aristo Jansen
AU - Mau, Sisilia Daeng Bakka
AU - Tedy, Frengky
AU - Meolbatak, Emiliana Metan
AU - Siki, Yovinia Carmeneja Hoar
AU - Gumelar, Agustinus Bimo
AU - Fanani, Nurul Zainal
AU - Yuhana, Umi Laili
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/29
Y1 - 2021/9/29
N2 - 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.
AB - 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.
KW - classifier performance
KW - dragon fruit datasets
KW - modeling
KW - neural network
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85119958395&partnerID=8YFLogxK
U2 - 10.1109/IES53407.2021.9593992
DO - 10.1109/IES53407.2021.9593992
M3 - Conference contribution
AN - SCOPUS:85119958395
T3 - International Electronics Symposium 2021: Wireless Technologies and Intelligent Systems for Better Human Lives, IES 2021 - Proceedings
SP - 287
EP - 291
BT - International Electronics Symposium 2021
A2 - Yunanto, Andhik Ampuh
A2 - Kusuma N, Artiarini
A2 - Hermawan, Hendhi
A2 - Putra, Putu Agus Mahadi
A2 - Gamar, Farida
A2 - Ridwan, Mohamad
A2 - Prayogi, Yanuar Risah
A2 - Ruswiansari, Maretha
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Electronics Symposium, IES 2021
Y2 - 29 September 2021 through 30 September 2021
ER -