Comparative Analysis of Accuracy in Identifying Types of Glass
Dublin Core
Title
Comparative Analysis of Accuracy in Identifying Types of Glass
Subject
Type of Glass, K-NN, C4.5, Naïve Bayes,
Accuracy, Error, Confusion Matrix
Accuracy, Error, Confusion Matrix
Description
To see whether the proposed research model is able to improve the performance of the
classification of the Glass Type Identification data using the K-Nearest Neighbor (K-
NN) method then the results will be compared with the C4.5 method and the Naïve
Bayes method, a performance analysis of the methods will be carried out. The results
are based on the results of the Confusion Matrix tabulation (two-class prediction. In this
study, only three preprocessing processes were carried out. The first process is handling
missing value. The missing value for attributes with numeric values is replaced by the
mean (mean) value of the attributes in the same column. Meanwhile, the missing values
for attributes with nominal values are replaced by the most likely values for the
attributes in the same column. Then the second process is the handling of duplicated
data. The data recorded were 214 data, the number of attributes was 9 attributes and the
number of classes was 6 classes.The results of this study show that the highest accuracy
value is in the C4.5 method with an accuracy of 73.45% with a value of K = 2 and an
error rate of 26.55%, while the method with low accuracy is the KNN method. with an
accuracy value of 61.95% and an error rate of 38.05%. Naïve Bayes has an accuracy of
63.33% and an error rate of 36.67. Therefore C4.5 is more effective than the two
methods.
classification of the Glass Type Identification data using the K-Nearest Neighbor (K-
NN) method then the results will be compared with the C4.5 method and the Naïve
Bayes method, a performance analysis of the methods will be carried out. The results
are based on the results of the Confusion Matrix tabulation (two-class prediction. In this
study, only three preprocessing processes were carried out. The first process is handling
missing value. The missing value for attributes with numeric values is replaced by the
mean (mean) value of the attributes in the same column. Meanwhile, the missing values
for attributes with nominal values are replaced by the most likely values for the
attributes in the same column. Then the second process is the handling of duplicated
data. The data recorded were 214 data, the number of attributes was 9 attributes and the
number of classes was 6 classes.The results of this study show that the highest accuracy
value is in the C4.5 method with an accuracy of 73.45% with a value of K = 2 and an
error rate of 26.55%, while the method with low accuracy is the KNN method. with an
accuracy value of 61.95% and an error rate of 38.05%. Naïve Bayes has an accuracy of
63.33% and an error rate of 36.67. Therefore C4.5 is more effective than the two
methods.
Creator
Novriadi Antonius Siagian
Publisher
Perpustakaan Horizon Karawang
Date
2021
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Novriadi Antonius Siagian, “Comparative Analysis of Accuracy in Identifying Types of Glass,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/3347.