ANALYZING THE POSSIBILITY OF DELAYS OF SPP PAYMENTS WITH C4.5 ALGORITHM (CASE STUDY OF POLITEKNIK TEDC BANDUNG)
Menganalisis Kemungkinan Keterlambatan Pembayaran SPP Dengan Algoritma C4.5 (Studi Kasus Politeknik TEDC Bandung)
Payment of tuition as one of the sources of funds, plays an important role in the sustainability of the operations of higher education. The problem that arises is that students are not often late to make payments in a timely manner. One of the factors causing the many cases of late payment of tuition fees due to lack of policy and decisive action on the part of the campus when students are late in making payments, besides the factors of parents and students also have an influence on the delay. The purpose of this study is to classify students who are late and timely in making SPP payments using the C4.5 algorithm. From the total sample used then divided into 4 partitions, partition 1 for 90% training data and 10% testing data, partition 2 for 80% training data and 20% testing data, and partition 3 for 70% training data and 30% testing data, and partition 4 for 60% training data and 40% testing data. The classification results of the C4.5 algorithm are evaluated and validated with a cross-validation and confusion matrix to determine the accuracy of the C4.5 algorithm in predicting late SPP payments. Based on the comparison of the results of evaluations and validations conducted, it shows that data partition 2 has a better level of accuracy than the other partitions, which is 75%.
Aguilar-Chinea, R., Rodriguez, I. C., & Exposito, C. (2019). Using a decision tree algorithm to predict the robustness of a transshipment schedule. Procedia Computer Science, 149, 529–536. https://doi.org/https://doi.org/10.1016/j.procs.2019.01.172
Apandi, T. H., Maulana, R. B., Piarna, R., & Vernanda, D. (2019). Laporan Akhir Penelitian: Menganalisis Kemungkinan Keterlambatan Pembayaran Spp Dengan Algoritma C4.5 (Studi Kasus Politeknik TEDC Bandung). Subang.
Apandi, T., Piarna, R., & Vernanda, D. (2018). Optimization of Feature Selection Using Genetic Algorithms to Increase Payment Delay Prediction Results (Subang Polytechnic State Case Study). In The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus (pp. 807–813). Kudus: EAI. Retrieved from https://eudl.eu/doi/10.4108/eai.24-10-2018.2280507
Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (3rd ed.). USA: Morgan Kaufmann.
Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining (1st ed.). Yogyakarta: Andi Publisher. Retrieved from http://andipublisher.com/produk-0907003050-algoritma-data-mining.html
Larose, D. T. (2005). Discovering Knowledge in Data: An Introduction to Data mining. New Jersey: John Willey & Sons, Inc.
Maulana, R. B. (2016). Penerapan Data mining untuk Menganalisa Kemungkinan Keterlambatan Pembayaran SPP (Studi Kasus Politeknik TEDC Bandung).
Swastina, L. (2013). Penerapan Algoritma C4.5 Untuk Penentuan Jurusan Mahasiswa. Gema Aktualita, Vol. 2(No. 1), 2–3.
Trabelsi, A., Elouedi, Z., & Lefevre, E. (2019). Decision tree classifiers for evidential attribute values and class labels. Fuzzy Sets and Systems, 366, 46–62. https://doi.org/https://doi.org/10.1016/j.fss.2018.11.006
Witten, & Frank, E. (2005). Data mining Practical Machine Learning Tools and Techniques. San Francisco: Elsevier (2nd ed., Vol. 2).
Zhang, J., Williams, S., & Wang, H. (2018). Intelligent computing system based on pattern recognition and data mining algorithms. Sustainable Computing: Informatics and Systems, 20, 192–202. https://doi.org/https://doi.org/10.1016/j.suscom.2017.10.010
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