Parents’ Sum of Salaries Analyses towards School Tuition Fee Arrears Potential with Decision Tree Method
DOI:
https://doi.org/10.24002/ijis.v2i1.2168Keywords:
School tuition fees, Business Intelligence, decision treeAbstract
School tuition fee is typically used for funding school operational, i.e. paying honorary teachers in public and private schools, purchasing practical instruments, printing examination worksheets, and other net-operational costs. According to the discovered data in the research environment, the funding is unable to be acquired properly due to students’ school tuition fees arrears for months even years until they graduate. Considering the condition, this research is conducted to identify the potential of students’ school tuition arrears, based on the sum of their parents’ salaries centered on the business intelligence approach, using the decision tree method. The analysis results show that, students whose parents’ income is less than Rp 672.500,00 will be potentially in arrears with school tuition more than Rp 900.000,00 each month, while students whose parents’ income is above Rp 672.500,00 are potentially in arrears of less than Rp 900.000,00 or not in arrears. To evaluate the effectiveness of the decision tree algorithm for data processing, it has an accuracy value of 95.97%, with a precision of 94.96% that means the algorithm has a good correlation based on attributes and the data that have been processed by the algorithm.References
R. K. Amin, Indwiarti, and Y. Sibaroni, “Implementation of decision tree using C4.5 algorithm in decision making of loan application by debtor (Case study: Bank pasar of Yogyakarta Special Region),” 2015 3rd Int. Conf. Inf. Commun. Technol. ICoICT 2015, vol. 0, pp. 75–80, 2015.
K. Arun, G. Ishan, and K. Sanmeet, “Loan Approval Prediction based on Machine Learning Approach,” IOSR J. Comput. Eng., vol. 18, no. 3, pp. 18–21, 2016.
Hendria Tony Fristanto, “Rancang Bangun Sistem Informasi Pembayaran Sumbangan Pembinaan Pendidikan (SPP) Dan Insidental Pada Sekolah Menengah Kejuruan Muhammadiyah Tinatar Punung Hendria Tony Fristanto,” Indones. J. Netw. Secur., vol. 3, no. 4, pp. 24–28, 2014.
H. P. Dewi, “Sistem Pengendalian Interen Atas Piutang Sumbangan Pembinaan Pendidikan ( Spp ) Sebagai Upaya Peningkatan Kinerja Pada Yayasan Islam Al - Hamidiyah Depok Tangerang,” J. Din. Ekon. Bisnis, vol. 12, pp. 76–88, 2015.
R. K. A N Hak, Cepy Slamet, “Implementasi Sistem Informasi Keuangan Siswa Berbasis Sms Gateway Di Smk Muhammadiyah 2 Kadungora,” J. Algoritm., vol. 11, no. 1, pp. 1–8, 2014.
I. S. Ma’rifati, “Sistem Informasi Akuntansi Pendapatan Sumbangan Pembinaan Pendidikan (SPP) Pada SMU XYZ,” J. Evolusi, vol. 3, no. 1, pp. 1–8, 2015.
S. Darudiato, S. W. Santoso, and S. Wiguna, “Business Intelligence : Konsep Dan Metode,” J. CommIT, vol. 4, no. 9, pp. 63–67, 2010.
D. D. Wu, S. H. Chen, and D. L. Olson, “Business intelligence in risk management: Some recent progresses,” J. Inf. Sci., vol. 256, pp. 1–7, 2014.
A. K. Maheshwari, Intelligence and Disease., 1st ed., vol. 219, no. 5655. New York: Business Expert Press, LLC, 2015.
A. Sucipto, “Pada Koperasi Simpan Pinjam Dengan Menggunakan METODE Algoritma Klasifikasi C4.5,” J. DISPROTEK, vol. 6, no. 1, pp. 75–87, 2015.
Ivandari, “Model Pohon Keputusan Untuk Klasifikasi Persetujuan Kredit,” J. Litbang Kota Pekalongan, vol. 9, pp. 124–136, 2015.
R. S. Oetama, “Enhancing Decision Tree Performance in Credit Risk Classification and Prediction,” J. Ultim. J. Tek. Inform., vol. 1, no. 7, pp. 50–55, 2015.
M. H. Rifqo and T. Arzi, “Implementasi Algoritma C4.5 untuk Menentukan Calon Debitur dengan Mengukur Tingkat Risiko Kredit pada Bank Bri Cabang Curup,” J. Pseudocode, vol. III, no. 2, pp. 83–90, 2016.
Abdussomad, “Penerapan Algoritma Klasifikasi C4.5 Untuk Menghasilkan Pola Kelayakan Kredit,” J. Inov. Inform., vol. II, no. 1, pp. 19–27, 2017.
Z. Nur and I. Mukhlash, “Implementasi Business Intelligence Pada Manajemen Report Bank XYZ,” J. Sains Dan Senni Pomits, vol. 3, no. Bisnis Intelijen, pp. 16–21, 2014.
M. Mustafid, “Sistem business intelligence untuk mendukung perguruan tinggi yang kompetitif,” J. Sist. Inf. Bisnis, vol. 3, no. 1, pp. 18–24, 2013.
U. Rahardja, Q. Aini, A. Khoirunisa, and B. Intelligence, “Implementasi Business Intelligence Menggunakan Highchart pada Sistem Penilaian Absensi berbasis YII Framework,” J. Comput. Sci. Res. its Dev., vol. 9, no. 2, pp. 115–124, 2017.
A. K. Hamoud, A. S. Hashim, and W. A. Awadh, “Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis,” Int. J. Interact. Multimed. Artif. Intell., vol. 5, no. 2, p. 26, 2018.
S. S. Bhatkande and R. G. Hubballi, “IJARCCE Weather Prediction Based on Decision Tree Algorithm Using Data Mining Techniques,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 5, no. 5, pp. 483–487, 2016.
H. Jiang and R. Wang, “Fault mode prediction based on decision tree,” IEEE Adv. Inf. Manag. Commun. Electron. Autom. Control Conf., pp. 1729–1733, 2016.
T. Wiyana, Hari;Kurniawan, Sigit; S. Ali, “Memprediksi Transaksi Nasabah Pada Koperasi Simpan Pinjam PT. Asuransi Staco Mandiri Dengan Algoritma C4.5,” in sentika, 2018, pp. 97–106.
G. Lukhayu Pritalia, “Penerapan Algoritma C4.5 untuk Penentuan Ketersediaan Barang E-commerce,” Indones. J. Inf. Syst., vol. 1, no. 1, pp. 47–56, 2018.
W. Said, M. M. Hassan, and A. M. Fawzy, “Data mining techniques for database prediction: Starting point,” J. Theor. Appl. Inf. Technol., vol. 96, no. 23, pp. 7723–7738, 2018.
R. K Rai, “Intricacies of Unstructured Data,” J. ICST Trans. Scalable Inf. Syst., vol. 4, no. 14, pp. 1–5, 2017.
E. S. Rahayu, R. S. Wahono, and C. Supriyanto, “Penerapan Metode Average Gain, Threshold Pruning dan Cost,” J. Intell. Syst., vol. 1, no. 2, pp. 91–97, 2015.
Downloads
Published
How to Cite
Issue
Section
License
Indonesian Journal of Information Systems as journal publisher holds copyright of papers published in this journal. Authors transfer the copyright of their journal by filling Copyright Transfer Form and send it to Indonesian Journal of Information Systems.
Indonesian Journal of Information Systems is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
