Tree Algorithm Model on Size Classification Data Mining

Agis Abhi Rafdhi; Eddy Soeryanto Soegoto; Senny Luckyardi; Chepi Nur Albar

doi:10.29207/resti.v7i4.4572

Agis Abhi Rafdhi Universitas Komputer Indonesia
Eddy Soeryanto Soegoto Universitas Komputer Indonesia
Senny Luckyardi Universitas Komputer Indonesia
Chepi Nur Albar Universitas Komputer Indonesia

DOI: https://doi.org/10.29207/resti.v7i4.4572

Keywords: Classification, Data mining, Decision Tree

Abstract

The goal of this research is to use a tree algorithm to categorize student clothing in order to acquire an accurate size. This research is qualitative through descriptive analysis, while the analysis used C.45 tree algorithm classification. Manual calculations utilizing the tree algorithm formula revealed that most students require XL-sized clothing. On the characteristic of X5 (length of the shoulder), the maximum entropy and information gain values were obtained at 0.212642462. According to the forecast, the shoulder length attribute is the first calculation in developing a decision tree scheme since it has the largest entropy and the value of information gain. Lastly, the findings of this study analysis can be used as a mapping prediction to make decisions on the size of the student group's clothing.

Downloads

Download data is not yet available.

References

V. Plotnikova, M. Dumas, and F. Milani, “Adaptations of data mining methodologies: A systematic literature review,” PeerJ Comput. Sci., vol. 6, pp. 1–43, 2020, doi: 10.7717/PEERJ-CS.267.

S. Asha Kiranmai and A. Jaya Laxmi, “Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy,” Prot. Control Mod. Power Syst., vol. 3, no. 1, 2018, doi: 10.1186/s41601-018-0103-3.

S. Sriramoju, G. Ramesh, and B. Srinivas, “An Overview of Classification Rule and Association Rule Mining,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 3, no. April, pp. 1692–1697, 2018, [Online]. Available: https://www.researchgate.net/profile/Gadde-Ramesh/publication/323550246_An_Overview_of_Classification_Rule_and_Association_Rule_Mining/links/5ad98d64a6fdcc293586aa12/An-Overview-of-Classification-Rule-and-Association-Rule-Mining.pdf.

I. Tougui, A. Jilbab, and J. El Mhamdi, “Heart disease classification using data mining tools and machine learning techniques,” Health Technol. (Berl)., vol. 10, no. 5, pp. 1137–1144, 2020, doi: 10.1007/s12553-020-00438-1.

C. W. Song, H. Jung, and K. Chung, “Development of a medical big-data mining process using topic modeling,” Cluster Comput., vol. 22, pp. 1949–1958, 2019, doi: 10.1007/s10586-017-0942-0.

M. Elhoseny, K. Shankar, and J. Uthayakumar, “Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease,” Sci. Rep., vol. 9, no. 1, pp. 1–14, 2019, doi: 10.1038/s41598-019-46074-2.

M. M. Arcinas, G. S. Sajja, S. Asif, S. Gour, E. Okoronkwo, and M. Naved, “Role of Data Mining in Education for Improving Students Performance for Social Change,” Turkish J. Physiother. Rehabil., vol. 32, no. 3, pp. 6519–6526, 2021.

R. Rahim et al., “C4.5 classification data mining for inventory control,” Int. J. Eng. Technol., vol. 7, no. July 2019, pp. 68–72, 2018, doi: 10.14419/ijet.v7i2.3.12618.

M. Sharma, S. Sharma, and G. Singh, “Performance analysis of statistical and supervised learning techniques in stock data mining,” Data, vol. 3, no. 4, pp. 1–16, 2018, doi: 10.3390/data3040054.

M. Z. Arif, R. Ahmed, U. H. Sadia, M. S. I. Tultul, and R. Chakma, “Decision Tree Method Using for Fetal State Classification from Cardiotography Data,” J. Adv. Eng. Comput., vol. 4, no. 1, p. 64, 2020, doi: 10.25073/jaec.202041.273.

M. Li, H. Xu, and Y. Deng, “Evidential decision tree based on belief entropy,” Entropy, vol. 21, no. 9, 2019, doi: 10.3390/e21090897.

T. Ç. AKINCI and H. S. Noğay, “Application of Decision Tree Methods for Wind Speed Estimation,” Eur. J. Tech., vol. 9, no. 1, pp. 74–83, 2019, doi: 10.36222/ejt.558914.

K. N. Dey, S. Saha, A. Ghosh, and S. Bandopadhyay, “Missing value imputation in DNA microarray gene expression data: a comparative study of an improved collaborative filtering method with decision tree based approach,” Int. J. Comput. Sci. Eng., vol. 18, no. 2, p. 130, 2019, doi: 10.1504/ijcse.2019.10019160.

T. Behdadnia, Y. Yaslan, and I. Genc, “A new method of decision tree based transient stability assessment using hybrid simulation for real-time PMU measurements,” IET Gener. Transm. Distrib., vol. 15, no. 4, pp. 678–693, 2021, doi: 10.1049/gtd2.12051.

H. H. Patel and P. Prajapati, “Study and Analysis of Decision Tree Based Classification Algorithms,” Int. J. Comput. Sci. Eng., vol. 6, no. 10, pp. 74–78, 2018, doi: 10.26438/ijcse/v6i10.7478.

S. Mauluddin, I. Ikbal, and A. Nursikuwagus, “Complexity and performance comparison of genetic algorithm and ant colony for best solution timetable class,” J. Eng. Sci. Technol., vol. 15, no. 1, pp. 276–290, 2020.

E. Odhiambo Omuya, G. Onyango Okeyo, and M. Waema Kimwele, “Feature Selection for Classification using Principal Component Analysis and Information Gain,” Expert Syst. Appl., vol. 174, no. February, p. 114765, 2021, doi: 10.1016/j.eswa.2021.114765.

K. Mathan, P. M. Kumar, P. Panchatcharam, G. Manogaran, and R. Varadharajan, “A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease,” Des. Autom. Embed. Syst., vol. 22, no. 3, pp. 225–242, 2018, doi: 10.1007/s10617-018-9205-4.

I. Budiarti, R. Andrian, and A. W N Falah, “Application of Web Communication Relationship Management in Small and Medium Enterprises,” Int. J. Res. Appl. Technol., vol. 1, no. 1, pp. 49–54, 2021, doi: 10.34010/injuratech.v1i1.5611.

W. Novianti and E. Erdiana, “Information Technology to Support E-Advertisement,” Int. J. Res. Appl. Technol., vol. 1, no. 1, pp. 134–139, 2021, doi: 10.34010/injuratech.v1i1.5656.

E. Yakut and M. Yüksel Avcilar, “Association Rules in Data Mining: An Application on a Clothing and Accessory Specialty Store,” Can. Soc. Sci., vol. 10, no. 3, p. 75, 2014, doi: 10.3968/4541.

K. Liu, J. Wang, and Y. Hong, “Wearing comfort analysis from aspect of numerical garment pressure using 3D virtual-reality and data mining technology,” Int. J. Cloth. Sci. Technol., vol. 29, no. 2, pp. 166–179, 2017, doi: 10.1108/IJCST-03-2016-0017.