Machine Learning With Decision Tree for Predict Incoice Payment, Case Study Gramedia Jakarta

Machine Learning With Decision Tree for Predict Incoice Payment, Case Study Gramedia Jakarta

• By Firmansyah,;
• Jurnal Nasional Terakreditasi
• 475
• 168-174

Arus keuangan yang lancar merupakan salah satu kunci agar perusahaan tetap bertahan dan memiliki keberlangsungan. Pembayaran atas faktur penjualan adalah salah satu masalah yang dapat mempengaruhi keuangan, jika pembayaran faktur terlambat maka perputaran kas menjadi lambat dan berdampak pada operasional perusahaan. Belum adanya alat yang dapat memprediksi pembayaran faktur di Gramedia menyulitkan bagian keuangan. Dari permasalahan itu, maka diterapkan machine learning untuk memprediksi pembayaran faktur oleh customer, apakah pembayarannya terlambat atau tidak terlambat. Proses dalam data mining menggunakan framework CRISP-DM (Cross Standard Industry for Data Mining). Fitur data yang digunakan sebagai parameter yaitu invoice amount, payment method, paid invoice, average days late dan ratio amount of overdue by amount of balance. Data faktur penjualan diprediksi menggunakan model decision tree algoritma C5.0 dengan hasil akurasi mencapai 71.84%.  Algoritma C5.0 terbukti mampu memprediksi faktur yang pembayarannya terlambat (melewati jatuh tempo) dan pembayarannya tepat waktu (sebelum jatuh tempo)

• Katakunci machine learning, decision tree, C5.0 algorithm, crisp-dm, invoice overdue prediction, rapid miner
• URL https://ojs.uma.ac.id/index.php/jite/article/download/5066/3421
• Penerbit JITE (Journal of Informatics and Telecommunication Engineering), Medan Sumatera Utara

REFERENSI

Aguilar, J. A. H., Montoya, E., Zavala, A. O. J. C., & Mayo, A. R. P. (2018). The Use of Data Mining Applied In the Accounts Receivable from the Employees of a Government Institution in México. Journal of Computer Science and Information Technology, 6(1), 34–43. https://doi.org/10.15640/jcsit.v6n1a4

Appel, A. P., Oliveira, V., Lima, B., Malfatti, G. L., de Santana, V. F., & de Paula, R. (2019). Optimize Cash Collection: Use Machine Learning to Predicting Invoice Payment. ArXiv.

Bahrami, M., Bozkaya, B., & Balcisoy, S. (2020). Using Behavioral Analytics to Predict Customer Invoice Payment. Big Data, 8(1), 25–37. https://doi.org/10.1089/big.2018.0116

Cheong, M. L. F., Cheong, M. L. F., & Shi, W. (2018). Customer Level Predictive Modeling for Accounts Receivable to Reduce Intervention Actions Customer Level Predictive Modeling for Accounts Receivable to Reduce Intervention Actions. Proceedings of the 14th International Conference on Data Science (ICDATA 2018), Las Vegas, Nevada, July 30 - August 2., Icdata, Research Collection School Of Information Systems.

Duru, A. N., Ekwe, M. C., & Okpe, I. I. (2014). Accounts Receivable Management and Corporate Performance of Companies in The Food & Beverage Industry : Evidence from Nigeria. 2(10), 1056–1063.

Firmansyah, & Yulianto, A. (2021). Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta. Journal of Informatics Anf Telecommunication Engineering, 4(January), 383–392.

Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (3rd ed.). Elsevier Inc.

Irawan, F., & Samopa, F. (2019). Accounts Receivable Seamless Prediction for Companies by Using Multiclass Data Mining Model. IPTEK Journal of Proceedings Series, 0(1), 1. https://doi.org/10.12962/j23546026.y2019i1.5096

Jadhav, S. D., & Channe, H. P. (2016). Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques. International Journal of Science and Research (IJSR), 5(1), 1842–1845. https://doi.org/10.21275/v5i1.nov153131

Jindal, D., Jain, S., & Vartika. (2017). Effect of Receivables Management on Profitability : A Study of Commercial Vehicle Industry in India. International Journal of Applied Sciences and Management, 2(2), 246–255.

Krichene, A. (2017). Using a naive Bayesian classifier methodology for loan risk assessment: Evidence from a Tunisian commercial bank. Journal of Economics, Finance and Administrative Science, 22(42), 3–24. https://doi.org/10.1108/JEFAS-02-2017-0039

Kumar, S. V. K., & Kiruthika, P. (2013). An Overview of Classification Algorithm in Data Mining. International Journal of P2P Network Trends and Technology (IJPTT), 4(8), 369–374. https://doi.org/10.17148/IJARCCE.2015.41259

Larose, D. T. (2006). Data Mining Methods and Models. Johns Wiley& Sons.

Menkinoski, G., Nikolovski, P., Karadjova, V., & Dičevska, S. (2016). Accounts Receivable Management - Important Task under Conditions of Globalization. IJSBAR, 27, 88–94. https://translate.google.com/translate?hl=es&sl=en&u=http://gssrr.org/index.php%3Fjournal%3DJournalOfBasicAndApplied%26page%3Darticle%26op%3Ddownload%26path%255B%255D%3D5281%26path%255B%255D%3D2861&prev=search%0A

Moedjiono, S., Fransisca, F., & Kusdaryono, A. (2016). Segmentation and Classification Customer Payment Behavior at Multimedia Service Provider Company with K-Means and C4.5 Algorithm. International Journal of Computer Networks and Communications Security, 4(9), 265–275.

Shah, H. S. (2019). Customer Payment Prediction in Account Receivable. International Journal of Science and Research (IJSR), 8(1), 642–644. https://www.ijsr.net/archive/v8i1/ART20194177.pdf