Prediction of Customer Data Classification by Company Category Using Decision Tree Algorithm (Case Study: PT. Teknik Kreasi Solusindo)

Authors

  • Ryo Nicholas Reinaldo Universitas Mercu Buana, Jakarta, Indonesia
  • Saruni Dwiasnati Universitas Mercu Buana, Jakarta, Indonesia

DOI:

https://doi.org/10.38035/ijam.v2i2.285

Keywords:

Classification, Company Data, Data Mining, Decision tree

Abstract

Classification using a decision tree for grouping customers with a case study of PT Teknik Kreasi Solusindo is a problem that exists in the company. Where the classification of customer data grouping in the company PT Teknik Kreasi Solusindo previously had no basis which resulted in the classification results not being entirely good. To overcome this problem, this study uses a classification method that exists in the data mining process, namely the decision tree algorithm. This study uses a Decision tree because the data used has a discrete type and the classification process is simple and fast. The data in the research used are product offering attributes of PT Teknik Kreasi Solusindo from 2018-2023. The source of the data obtained is the results of interviews with representatives from PT Teknik Kreasi Solusindo and also several bidding files in the company. Based on this data, a classification will be carried out with the Google Colabolatory service. And with this method, the accuracy of the decision tree method will be seen as a reference for the desired classification.

 

 

Author Biographies

Ryo Nicholas Reinaldo, Universitas Mercu Buana, Jakarta, Indonesia

 

 

Saruni Dwiasnati, Universitas Mercu Buana, Jakarta, Indonesia

 

 

References

Adhinata, K. B. (2019). Implementasi Algoritma Decision tree Classifier Untuk Klasifikasi Pelanggan Provider X Pada E-commerce Sepulsa. Surabaya, Indonesia: Universitas Ciputra.

Aguilar-Chinea, R. M., Castilla Rodriguez, I., Exposito, C., Melian-Batista, B., & Moreno-Vega, J. M. (2018). Using a decision tree algorithm to predict the robustness of a transshipment schedule. La Laguna, Spain: Universidad de La Laguna.

Alleyda, I. S., Ballya, V. H., Endah, P., Yohanne, M. S., Andhika, W. W., & Desta, S. P. (2021). Klasifikasi Data Penjualan pada Supermarket dengan Metode Decision tree. Jakarta, Indonesia: Universitas Pembangunan Nasional "Veteran" Jakarta.

Arfandi, A. P. W., Ilham, S. S., & Saragih, I. S. (2021). Penerapan Data Mining Klasifikasi Pada Calon Pelanggan Baru Indihome dengan C.45. Pematang Siantar, Indonesia: STIKOM Tunas Bangsa.

Basri, W. G., Risnandar, & Nusa Mandiri, U. (2020). Analisis Loyalitas Pelanggan Berbasis Model Recency, Frequency, Dan Monetary (RFM) Dan Decision tree Pada PT. SOLO. Jakarta, Indonesia: Universitas Nusa Mandiri Kampus Kramat Raya.

Febriyani, A., Prayoga, G. K., & Nurdiawan, O. (2021). Index Kepuasan Pelanggan Informa dengan Menggunakan Algoritma C.45. Cirebon, Indonesia: STMIK IKMI.

Hikmatulloh, R., Mahaerani P., H., & Aini, Q. (2020). Penerapan Decision tree untuk Prediksi Kepuasan Pengguna Bus Transjakarta. Banten, Indonesia: Universitas Islam Negeri Syarif Hidayatullah.

Latifah, R., Wulandari, E. S., & Kreshna, P. E. (2019). Model Decision tree untuk Prediksi Jadwal Kerja menggunakan Scikit-Learn. Jakarta, Indonesia: Universitas Muhammadiyah Jakarta.

Mendez, M. (2012). Sales promotions effects on brand loyalty. Florida, United States of America: Nova Southeastern University.

Musthofa, G. P., & Saputro, P. H. (2020). Komparasi Metode Naïve Bayes dan C4.5 Dalam Klasifikasi Loyalitas Pelanggan Terhadap Layanan Perusahaan. Yogyakarta, Indonesia: Universitas Alma Ata Yogyakarta.

Nasrullah, A. H. (2021). Implementasi Algoritma Decision tree untuk Klasifikasi Produk Laris. Gorontalo, Indonesia: Universitas Ichsan Gorontalo.

Oktaviani, A., Cornelia, R., & Wibisono, D. (2022). Peningkatan Loyalitas Pelanggan pada PT Home Center Indonesia Menggunakan Metode Algoritma C4.5 dan Metode CSI (Customer Satisfaction Index). Jakarta, Indonesia: Universitas Indraprasta PGRI.

Satyanarayana, N., Ramalingaswamy, CH., & Ramadevi, Y. (2014). Survey of Classification Techniques in Data Mining. Hyderabad, India: CVR College of Engineering.

Solehuddin, M., Syafei, W. A., & Gernowo, R. (2022). Metode Decision tree untuk Meningkatkan Kualitas Rencana Pembelajaran dengan Algoritma C4.5. Semarang, Indonesia: Universitas Diponegoro.

Susanto, H., & Sudiyatno. (2014). Data Mining untuk Memprediksi Prestasi Siswa Berdasarkan Sosial Ekonomi, Motivasi, Kedisiplinan, dan Prestasi Masa Lalu. Surakarta, Indonesia: SMK Negeri 4 Surakarta.

Wardani, N. W., & Ariasih, N. K. (2019). Analisa Komparasi Algoritma Decision tree C4.5 dan Naïve Bayes untuk Prediksi Churn Berdasarkan Kelas Pelanggan Retail. Singaraja, Indonesia: Universitas Pendidikan Ganesha.

Wardani, N. W., Dantes, G. R., & Indrawan, G. (2018). Prediksi Customer Churn Dengan Algoritma Decision tree C4.5 Berdasarkan Segmentasi Pelanggan Pada Perusahaan Retail. Jurnal Resistor, 1(1), 1-10.

Yusuf, F. A., Alfaridzi, M., & Herdi, T. (2022). Penerapan Algoritma Decision Tree Untuk Klasifikasi KIPI Vaksin Covid-19. Jurnal Ilmiah FIFO, 14(2), 155. https://doi.org/10.22441/fifo.2022.v14i2.005

Published

2023-07-21

How to Cite

Nicholas Reinaldo, R., & Dwiasnati, S. . (2023). Prediction of Customer Data Classification by Company Category Using Decision Tree Algorithm (Case Study: PT. Teknik Kreasi Solusindo). International Journal of Advanced Multidisciplinary, 2(2), 229–238. https://doi.org/10.38035/ijam.v2i2.285