MODEL KLASIFIKASI BERBASIS MACHINE LEARNING UNTUK PERPANJANGAN MASA JABATAN KEPALA SEKOLAH MENGGUNAKAN ALGORITMA C4.5
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Jan 10, 2023
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The extension assessment process of the tenure’s school principal at Dinas Pendidikan Kabupaten Musi Rawas runs manually, so it takes more than 1 (one) month to get the results. This study aims to model a classification based on Machine Learning technique using the C4.5 Decission Tree algorithm to make it easier for supervisors to provide recommendations whether the principal’s position has Extended or Not Extended status. The research design uses the CRISP-DM concept which is adopted to the needs of the research objectives. The resulting model has 15 rules which are used as the basis for forming a Decision Tree. Model validation measurement was tested using the Confusion Matrix method and it provides an accuracy of 83,3%.
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Siswipraptini, P. C., Fadiarora, A. S., & Sikumbang, H. (2023). MODEL KLASIFIKASI BERBASIS MACHINE LEARNING UNTUK PERPANJANGAN MASA JABATAN KEPALA SEKOLAH MENGGUNAKAN ALGORITMA C4.5. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(1), 255-264. https://doi.org/10.35870/jimik.v4i1.167
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M. Amrane, S. Oukid, I. Gagaoua, and T. Ensaari, 201. Breast Cancer Classification Using Machine Learning. Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), pp. 168–171.
M. Batta, 2018. Machine Learning Algorithms - A Review. Int. J. Sci. Res. 18(8), pp. 381–386.
D. Kurniadi, E. Abdurachman, H. L. H. S. Warnars, and W. Suparta, 2021. Predicting student performance with multi-level representation in an intelligent academic recommender system using backpropagation neural network. ICIC Express Lett. Part B Appl., 12(10), pp. 883–890.
P. B. N. Setio, D. R. S. Saputro, and Bowo Winarno, 2020. Klasifikasi Dengan Pohon Keputusan Berbasis Algoritme C4.5. Prism. Pros. Semin. Nas. Mat., vol. 3, pp. 64–71.
S. Sunarti, 2019. Prediksi Promosi Jabatan Karyawan Dengan Algoritma C4.5 (Studi Kasus: Apartemen Senayan Jakarta). Techno.Com, 18(4) , pp. 288–298.
C. K. Lo, H. C. Chen, P. Y. Lee, M. C. Ku, L. Ogiela, and C. H. Chuang, 2019. Smart dynamic resource allocation model for patient-driven mobile medical information system using C4.5 algorithm. J. Electron. Sci. Technol., 17(3) , pp. 231–241.
A. Cherfi, K. Nouira, and A. Ferchichi. 2018. Very Fast C4.5 Decision Tree Algorithm. Appl. Artif. Intell., 32(2), pp. 119–137.
J. S. Lee, 2019. AUC4.5: AUC-Based C4.5 Decision Tree Algorithm for Imbalanced Data Classification. IEEE Access, vol. 7, pp. 106034–106042.
A. Luque, A. Carrasco, A. Martín, and A. de las Heras, 2019. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognit., vol. 91, pp. 216–231.
Xu, Y. Zhang, and D. Miao, 2000. Three-way confusion matrix for classification: A measure driven view. Inf. Sci. (Ny)., vol. 507, pp. 772–794.
P. C. NCR et al., 2000. CRISP-DM 1.0, vol. 78.
A. Azevedo and M. F. Santos, 2014. KDD , SEMMA AND CRISP-DM : A PARALLEL OVERVIEW. IADIS European Conference Data Mining.
S. Huber, H. Wiemer, D. Schneider, and S. Ihlenfeldt, 2019. DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model. Procedia CIRP, vol. 79, pp. 403–408.
H. Wiemer, L. Drowatzky, and S. Ihlenfeldt, 2019. Applied Aciences Data Mining Methodology for Engineering Applications ( DMME )— A Holistic Extension. Appl. Sci.