Implementasi Data Mining Menggunakan Algoritma C4.5 pada Klasifikasi Penjualan Hijab

Faridatul Husna, Hairur Rahman, Juhari Juhari


Indonesia is known as a country with a majority Muslim population, this makes the need for clothing in Indonesia must also pay attention to the criteria for Muslim clothing, one of which is the hijab. Business developments in the fashion world, especially hijab, have become a trend setter at this time so that the large amount of data in the fashion business world creates conditions where there are businesspeople who have a lot of data but lack of information from that data. To deal with these conditions, it is necessary to classify the data. A classification is a process to find the same properties in a data set to be classified into different classes.  One of the classification methods is the Decision tree using the C4.5 Algorithm.  This research aims to determine the model and the accuracy of the C4.5 algorithm in classifying hijab sales from several hijab brands.  The Decision tree model is obtained using the C4.5 algorithm with the first root being the price attribute, where the first root is the attribute that most affected the sale of the hijab.  The result of calculating the accuracy value is 87% so that the Decision tree model and the classification process using the C4.5 Algorithm are classified as good. This research is expected to help businesspeople in the fashion sector, especially hijab, to find out the factors that influence consumer interest in a hijab product.


accuracy; c4.5 algorithm; classification; decision tree

Full Text:



Yasyi, Dini N. “Tahun 2020, Sektor Ekonomi Kreatif Akan Sumbang Rp.1.100 Triliun ke PDB Indonesia”,, diakses pada 2 Juni 2021 pukul 14.02.

Ramageri, Bharati M. 2010.Data mining Techniques and Aplications. Indian Journal of Computer Science and Engineering. 1(4).

Micahel J. A. Berry, G. L. 2004. Data mining Techniques For Marketing, Sales, and Customer Relationship Management. Wiley Publishing..

Santosa, B. 2007. Data mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis (1st ed.). Graha Ilmu

Han, J, Kamber, M, & Pei, J. 2012. Data mining: Concept and Techniques, Third Edition. Waltham: Morgan Kaufmann Publishers.

Azwanti, N. 2018. Analisa Algoritma C4.5 untuk Memprediksi Penjualan Motor pada PT. Capella Dinamik Nusantara Cabang Muka Kuning. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 13(1), 6.

Mienye, I. D., Sun, Y., & Wang, Z. 2019. Prediction performance of improved decision tree-based algorithms: A review. Procedia Manufacturing, 35, 698–703.

Lakshmi, B. N., Indumathi, T. S., & Ravi, N. (2016). A Study on C.5 Decision tree Classification Algorithm for Risk Predictions During Pregnancy. Procedia Technology, 24, 1542–1549.

Kusrini & Luthfi, Emha T. 2009. Algoritma Data mining. ANDI OFFSET.

Rogers, Simon & Girolami, Mark. 2012. A First Course in Machine Learning. CRC Press Taylor & Francis Group.

Rahman, M. F., Alamsah, D., Darmawidjadja, M. I., & Nurma, I. 2017. Klasifikasi Untuk Diagnosa Diabetes Menggunakan Metode Bayesian Regularization Neural Network (RBNN). Jurnal Informatika, 11(1), 36.

Gorunescu, Florin. 2011. Data mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer .

Hartama, A. A. K. 2017. Klasifikasi Penyakit Hipertensi Menggunakan Algoritma C4.5 Studi Kasus RSU Provinsi NTB. 1–154.



  • There are currently no refbacks.