Violation Types Determination of The Whistleblowing System Using the C4.5 Algorithm
DOI:
https://doi.org/10.24014/coreit.v9i1.22897Abstract
Whistleblowing is a complaint system and follow-up management of each violation report. The problem that arises is when determining the follow-up, namely determining the severity or severity of the violation and the sanctions given are only based on the superior's assessment without adhering to standard guidelines or rules. This results in the sanctions given not in accordance with the violations committed. The purpose of this study is to classify the types of violations so as to facilitate the determination of sanctions on the whistleblowing system using the C4.5 Algorithm. The partition was performed three times with the highest additional value of 0.8516 and a decision tree was obtained. Based on the decision tree, the final node that has been generated is then extracted into 27 rules. The classification results from the C4.5 Algorithm can be used to classify the types of violations with an accuracy rate of more than 80%. The first validation with 15 tests obtained an accuracy rate of 86.66%. The second validation is the combination of data on 125 cases of combination data and obtained an accuracy rate of 84.8%. The decision tree generated from three partitions consists of 27 rules that can be used as a pattern to classify the types of violations.
References
R. Benkercha and S. Moulahoum, “Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system,” Sol. Energy, vol. 173, no. April, pp. 610–634, 2018, doi: 10.1016/j.solener.2018.07.089.
N. Asiah and D. S. Rini, “Pengaruh Bystander Effect Dan Whistleblowing Terhadap Terjadinya Kecurangan Laporan Keuangan,” Nominal, Barom. Ris. Akunt. dan Manaj., vol. 6, no. 1, 2017, doi: 10.21831/nominal.v6i1.14336.
S. Moral-García, C. J. Mantas, J. G. Castellano, and J. Abellán, “Using Credal C4.5 for Calibrated Label Ranking in Multi-Label Classification,” Int. J. Approx. Reason., vol. 147, pp. 60–77, 2022, doi: https://doi.org/10.1016/j.ijar.2022.05.005.
T. H. Apandi, R. B. Maulana, R. Piarna, and D. Vernanda, “Menganalisis Kemungkinan Keterlambatan Pembayaran Spp Dengan Algoritma C4.5 (Studi Kasus Politeknik Tedc Bandung),” J. Techno Nusa Mandiri, vol. 16, no. 2, pp. 93–98, 2019, doi: 10.33480/techno.v16i2.659.
E. Elisa, “Analisa dan Penerapan Algoritma C4 . 5 Dalam Data Mining Untuk Mengidentifikasi Faktor-Faktor Penyebab Kecelakaan Kerja Kontruksi PT . Arupadhatu Adisesanti,” JOIN, vol. 2, no. 1, pp. 36–41, 2017.
H. Yusti and K. Ameliza, “PENERAPAN ALGORITMA C 4.5 UNTUK PENETUAN KRITERIA ANGGOTA LAYAK PINJAM BERDASARKAN AD/ART KOPERASI,” J. Ilmu Komput. dan Bisnis, vol. 9, no. Nov 2018, pp. 2044–2050, 2018, [Online]. Available: https://www.cambridge.org/core/product/identifier/CBO9781139058452A007/type/book_part.
A. R. Panhalkar and D. D. Doye, “Optimization of Decision Trees using Modified African Buffalo Algorithm,” J. King Saud Univ. - Comput. Inf. Sci., 2021, doi: 10.1016/j.jksuci.2021.01.011.
L. N. Rani, “Klasifikasi Nasabah Menggunakan Algoritma C4.5 Sebagai Dasar Pemberian Kredit,” INOVTEK Polbeng - Seri Inform., vol. 1, no. 2, p. 126, 2016, doi: 10.35314/isi.v1i2.131.
Y. Chen and Y. Zhou, “Machine learning based decision making for time varying systems: Parameter estimation and performance optimization,” Knowledge-Based Syst., vol. 190, pp. 1–10, 2020, doi: 10.1016/j.knosys.2020.105479.
H.-B. Wang and Y.-J. Gao, “Research on C4.5 algorithm improvement strategy based on MapReduce,” Procedia Comput. Sci., vol. 183, pp. 160–165, 2021, doi: https://doi.org/10.1016/j.procs.2021.02.045.
J. Yan, Z. Zhang, K. Lin, F. Yang, and X. Luo, “A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks,” Knowledge-Based Syst., vol. 198, p. 105922, 2020, doi: 10.1016/j.knosys.2020.105922.
M. Calis et al., “Algorithms for the management of frontal sinus fractures: A retrospective study,” J. Cranio-Maxillofacial Surg., vol. 50, no. 10, pp. 749–755, 2022, doi: https://doi.org/10.1016/j.jcms.2022.09.007.
H. Huang, H. Wang, and M. Sun, “Incomplete data classification with view-based decision tree,” Appl. Soft Comput. J., vol. 94, p. 106437, 2020, doi: 10.1016/j.asoc.2020.106437.
X. Meng, P. Zhang, Y. Xu, and H. Xie, “Construction of decision tree based on C4.5 algorithm for online voltage stability assessment,” Int. J. Electr. Power Energy Syst., vol. 118, no. July 2019, p. 105793, 2020, doi: 10.1016/j.ijepes.2019.105793.
J. Shanthi, D. G. N. Rani, and S. Rajaram, “A C4.5 decision tree classifier based floorplanning algorithm for System-on-Chip design,” Microelectronics J., vol. 121, p. 105361, 2022, doi: https://doi.org/10.1016/j.mejo.2022.105361.
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