Comparative Analysis of Accuracy in Identifying Types of Glass
Abstract
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.
Keywords
Full Text:
PDFReferences
Arifin, Toni. 2015. Implementasi Metode K-Nearest Neighbor untukKlasifikasi Citra Sel PAP Smear menggunakan Analisis Tekstur Nukleus. Jurnal Informatika. Volume. II. Pp. 1-4. ISSN : 2355-6579.
Dai Qin-yun,. Zang Chun-Ping., Wu Hao. 2016. Research of Decision tree Classification Algorithm in Data Mining. Dept. of Electric and Electronic Engineering, Shijiazhuang Vocational and Technology Institute. China
Danades, A., Pratama, D., Anggraini, D., Anggriani, D. 2016. Comparison of Accuracy Level K-Nearest Neighbor Algorithm and Support Vector Machine Algorithm in Classification Water Quality Status. International Conference on System Engineering and Technology, pp. 137-141.
Fadillah, Annisa Pulungan. 2018. AnalisisKinerja Bray Curtis Distance dan Canberra Distance Pada Algoritma K-Nearest Neighbor. Tesis. UniversitasSumatera Utara.
Han, J., Kamber, M. & Pei, J. 2012.Data Mining: Concepts and Techniques. 3rd Edition. Morgan Kaufmann Publishers: San Francisco.
NH Niloy, MAI Navid. Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients. Department of Science, Ruhea College, Rangpur, Bangladesh
Pattekari, S. A., Parveen, A., 2012. Prediction System for Heart Disease Using Naive Bayes, International Journal of Advanced Computer and Mathematical Sciences, ISSN 2230-9624, Vol. 3, No 3, Hal 290-294
Raviya. Kaushik H & Gajjar, Biren. 2013. Performance Evaluation of Different Data Mining Classification Algoritma Using WEKA. Indian Journal of Research. Volume. 2. Issue.1. ISSN: 2250-1991.
DOI: https://doi.org/10.30743/infotekjar.v5i2.3655
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Novriadi Antonius Siagian
This work is licensed under a Creative Commons Attribution 4.0 International License.
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan
Fakultas Teknik - Universitas Islam Sumatera Utara
Jl. Sisingamangaraja, Teladan, Medan 20217
Website: https://jurnal.uisu.ac.id/index.php/infotekjar
Email: infotekjar@ft.uisu.ac.id
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License