Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction

Perbandingan Model Pohon Klasifikasi Algoritma C4.5 dan C5.0 untuk Analisis Faktor yang Mempengaruhi Keberhasilan Lelang

Authors

  • Mohammad Fajri Statistics Studies Program, Tadulako University, Indonesia
  • Iut Tri Utami Statistics Studies Program, Diponegoro University, Indonesia
  • Muh. Maruf Statistics Studies Program, Tadulako University (Untad), Indonesia

DOI:

https://doi.org/10.29244/ijsa.v6i1p13-22

Keywords:

auction, C4.5 Algorithm, C5.0 Algorithm, decision tree

Abstract

Auction in Indonesia is carried out by the Office of State Assets and Auction Services (KPKNL). Goods auctioned at KPKNL are quite diverse including land, wood, inventory, vehicles, and other goods. However, not all of the items auctioned were sold. Because not a few items have been auctioned but no one has made an offer. The Purpose of this study is to compare two classification methods, C4.5 and C5.0 algorithm and to determine which items were successfully auctioned with those that did not and its factors. The methods that used were comparing the classification tree C4.5 algorithm and C5.0 algorithm with cross validation. From the results of the comparison of the two methods, it was found that the C5.0 Algorithm method was rated better than the C4.5 algorithm in classifying the auction results with an accuracy of 96.43% and 92.86% respectively. In this case, C5.0 has a higher precision than C4.5.

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References

Blockeel, H., & Struyf, J. (2002). Efï¬cient Algorithms for Decision Tree Cross-validation. 30.

Brieman, L., Friedman, J., & Olshen, R. A. (1984). Classification and Regression Trees. CRC press.

Bujlow, T., Riaz, T., & Pedersen, J. M. (2012). A method for classification of network traffic based on C5. 0 Machine Learning Algorithm. International Conference on Computing, Networking and Communications, 237–241.

Chen, C. C., & Chung, M.-C. (2015). Predicting the success of group buying auctions via classification. Knowledge-Based Systems, 89, 627–640. https://doi.org/10.1016/j.knosys.2015.09.009

Ente, D. R., Thamrin, S. A., Arifin, S., Kuswanto, H., & Andreza, A. (2020). Klasifikasi Faktor-Faktor Penyebab Penyakit Diabetes Melitus di Rumah Sakit Unhas Menggunakan Algoritma c4.5. Indonesian Journal of Statistics and Its Applications, 4(1), 80–88.

Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques. Elsevier.

Lakshmi, T. M., Martin, A., Begum, R. M., & Venkatesan, V. P. (2013). An Analysis on Performance of Decision Tree Algorithms Using Student’s Qualitative Data. International Journal of Modern Education and Computer Science, 5(5), 18.

Lusk, J., & Shogren, J. F. (2007). Experimental auctions: Methods and applications in economic and marketing research. Cambridge University Press.

Quinlan, J. R. (2014). C4. 5: Programs for Machine Learning. Elsevier.

Salim, H. S. (2014). Perkembangan Hukum Jaminan di Indonesia. Divisi Buku Perguruan. PT RajaGrafindo Persada.

Tan, P. N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Pearson.

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Published

2022-05-31

How to Cite

Fajri, M., Utami, I. T. ., & Maruf, M. . (2022). Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction: Perbandingan Model Pohon Klasifikasi Algoritma C4.5 dan C5.0 untuk Analisis Faktor yang Mempengaruhi Keberhasilan Lelang. Indonesian Journal of Statistics and Its Applications, 6(1), 13–22. https://doi.org/10.29244/ijsa.v6i1p13-22

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