Tree Algorithm Model on Size Classification Data Mining

  • Agis Abhi Rafdhi Universitas Komputer Indonesia
  • Eddy Soeryanto Soegoto Universitas Komputer Indonesia
  • Senny Luckyardi Universitas Komputer Indonesia
  • Chepi Nur Albar Universitas Komputer Indonesia
Keywords: Classification, Data mining, Decision Tree


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.



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How to Cite
Agis Abhi Rafdhi, Eddy Soeryanto Soegoto, Senny Luckyardi, & Chepi Nur Albar. (2023). Tree Algorithm Model on Size Classification Data Mining. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(4), 895 - 903.
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