Penerapan Algoritma C4.5 Untuk Klasifikasi Hasil Produksi Batu Bata

Ari  Syahputra; Eva  Darnila; Cut Agusniar

doi:10.51903/bc8vh970

Authors

Ari Syahputra Universitas Malikussaleh, Kota Lhokseumawe, Aceh, indonesia
Eva Darnila Universitas Malikussaleh, Kota Lhokseumawe, Aceh, indonesia https://orcid.org/0000-0002-4058-8828
Cut Agusniar Universitas Malikussaleh, Kota Lhokseumawe, Aceh, indonesia https://orcid.org/0009-0000-7976-3147

DOI:

https://doi.org/10.51903/bc8vh970

Keywords:

C4.5, Brick Quality Classification, Data Mining, Decision Tree, Quality Control

Abstract

Red clay bricks are one of the most widely used construction materials in the building industry. However, the determination of brick quality is often carried out manually, which may result in inconsistent product quality due to subjective assessments. This study aims to apply the C4.5 algorithm to classify the quality of red clay bricks objectively based on physical testing results. The research data were obtained from measurements of burning duration, water absorption, compressive strength, and brick color. The research methodology consisted of data collection, data preprocessing, entropy and information gain calculations, and decision tree construction using the C4.5 algorithm. A total of 300 brick samples were analyzed and categorized into three quality classes: Good, Moderate, and Poor. The classification model was evaluated using a confusion matrix to measure its performance. The results showed that the C4.5 algorithm successfully classified brick quality with an accuracy rate of 81.11%, indicating a good level of classification performance. Furthermore, the decision tree analysis revealed that water absorption was the most influential attribute in determining brick quality. The developed system provides a practical and objective approach for quality control by reducing subjectivity and improving consistency in the evaluation process. The findings of this study contribute to the implementation of data mining techniques in the construction materials industry and support more effective, measurable, and reliable brick quality assessment processes.

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