Application of the C 4.5 Algorithm to Classify Customer Characteristics at PT. Bayer Indonesia
DOI:
https://doi.org/10.47709/brilliance.v4i1.4174Keywords:
Data Mining, Transaction Characteristics, Rapidminer, C4.5 AlgorithmAbstract
PT. Bayer Indonesia is a company engaged in drug production. In running its business, companies need to know customer characteristics in determining what actions to take next. This research aims to apply the C 4.5 algorithm in classifying customer characteristics at PT. Bayer Indonesia. The C 4.5 algorithm is a decision tree algorithm that is often used in data mining for classification purposes. This research was conducted to make it easier to find out customer characteristics. Starting with collecting data, then selecting the attributes that will be used. Then the data is separated using split data, the initial comparison used is 60% train data and 40% test data. Then training data is carried out using the C4.5 algorithm. Next, the classification results were evaluated using the confusion matrix method. The data used was 200 data with 9 attributes, obtained an accuracy of 86.25%, precision of 86.25% and recall of 54.55%. Then change the data split parameters to 70% : 30%, 80% : 20% and 90% : 10%. The best accuracy is 100%. The research results show that the C 4.5 algorithm has good performance in classifying the characteristics of PT customers. Bayer Indonesia. The resulting model can be used by companies for more effective marketing strategies and personalized customer service.
References
Analisis Kebutuhan dan Keinginan Konsumen Untuk meningkatkan Pelayanan di Hotel Santika Depok. (2016). Jurnal Ilmiah Pariwisata, 21(2), 1–16.
Audilla, C. M. P., Riyadi, S., & Asroni, A. (2022). Prediction of Student Study Period Based on Admission Pathways Using Support Vector Machine Algorithm. Emerging Information Science and Technology, 1(4), 155–160. https://doi.org/10.18196/eist.v1i4.16598
Ega Saputra, & Rahmat Fauzi. (2022). IMPLEMENTASI DATA MINING K-NEAREST NEIGHBOR PADA PENERIMAAN KARYAWAN DI PT DWI SUMBER ARCA WAJA. Computer and Science Industrial Engineering (COMASIE), 6(4), 41–49.
Faisal, S. (2019). KLASIFIKASI DATA MINNING MENGGUNAKAN ALGORITMA C4.5 TERHADAP KEPUASAN PELANGGAN SEWA KAMERA CIKARANG. Techno Xplore?: Jurnal Ilmu Komputer Dan Teknologi Informasi, 4(1), 1–8. https://doi.org/10.36805/technoxplore.v4i1.541
Gustrianda, R., & Mulyana, D. I. (2022). Penerapan Data Mining Dalam Pemilihan Produk Unggulan dengan Metode Algoritma K-Means Dan K-Medoids. JURNAL MEDIA INFORMATIKA BUDIDARMA, 6(1), 27. https://doi.org/10.30865/mib.v6i1.3294
Irene Sinta Silalahi, Johny R. E. Tampi, & Aneke Y. Punuindoong. (2019). Analisis Pelayanan Obat-Obatan Dalam Kepuasan Konsumen Pada Apotek Syalom Amurang. Jurnal Administrasi Bisnis, 8(2), 67–73.
Jupri, M., & Sarno, R. (2018). Taxpayer compliance classification using C4.5, SVM, KNN, Naive Bayes and MLP. 2018 International Conference on Information and Communications Technology (ICOIACT), 297–303. https://doi.org/10.1109/ICOIACT.2018.8350710
Kurnia, Y., & Kusuma, K. (2021). Comparison of C4.5 Algorithm, Naive Bayes and Support Vector Machine (SVM) in Predicting Customers that Potentially Open Deposits. Bit-Tech, 1(2), 84–91. https://doi.org/10.32877/bt.v1i2.46
Maryamah, Asikin, Moh. F., Kurniawaty, D., Sari, S. K., & Cholissodin, I. (2016). Implementasi Metode Naïve Bayes Classifier Untuk Seleksi Asisten Praktikum Pada Simulasi Hadoop Multinode Cluster. Jurnal Teknologi Informasi Dan Ilmu Komputer, 3(4), 273. https://doi.org/10.25126/jtiik.201634227
Muttaqien, R., Pradana, M. G., & Pramuntadi, A. (2021). Implementation of Data Mining Using C4.5 Algorithm for Predicting Customer Loyalty of PT. Pegadaian (Persero) Pati Area Office. International Journal of Computer and Information System (IJCIS), 2(3), 64–68. https://doi.org/10.29040/ijcis.v2i3.36
Putri Mai Sarah Tarigan, Jaya Tata Hardinata, Hendry Qurniawan, & Muhammad Safii, R. W. (2022). IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA APRIORI DALAM MENENTUKAN PERSEDIAAN BARANG (STUDI KASUS?: TOKO SINAR HARAHAP). Jurnal Sistem Informasi, Teknologi Informasi Dan Komputer, 12(2), 51–61.
Rosid, A., Ardin, G., & Sanjaya, T. B. (2022). Prediksi Keikutsertaan Pelaku Usaha dalam Pemanfaatan Insentif Pajak dengan Artificial Neural Network. Jurnal Ekonomi Indonesia, 11(2), 109–142. https://doi.org/10.52813/jei.v11i2.178
Sandova, F., Kurniawan, R., & Supratati, T. (2024). PENERAPAN DATA MINING MENGGUNAKAN METODE K-MEANS CLUSTERING PADA PENJUALAN TAS DI ASIA TOSERBA CIREBON. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 245–251. https://doi.org/10.36040/jati.v8i1.8330
Siahaan, S. W., Sianipar, K. D. R., R.H Zer, P. P. P. A. N. W. F. I., & Hartama, D. (2020). Penerapan Algoritma C4.5 Dalam Meningkatkan Kemampuan Bahasa Inggris Pada Mahasiswa. PETIR, 13(2), 229–239. https://doi.org/10.33322/petir.v13i2.1029
Sirait, G. (2019). PENGENDALIAN PERSEDIAAN OBAT DENGAN PENDEKATAN ECONOMIC ORDER QUANTITY. JURNAL REKAYASA SISTEM INDUSTRI, 4(2), 98–103. https://doi.org/10.33884/jrsi.v4i2.1276
Syaiful Bahri, & Akhyar Lubis. (2020). METODE KLASIFIKASI DECISION TREE UNTUK MEMPREDIKSI JUARA ENGLISH PREMIER LEAGUE. SINTAKSIS, 2(1), 63–70.
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