ANALISIS PREDIKSI KELULUSAN UJIAN LABOR PROFESIONAL MAHASISWA UNIVERSITAS METAMEDIA

Karfindo Karfindo, Rifa Turaina, Rusli Saputra

Sari


Increasing developments in the field of Artificial Intelligence (AI) have made many other fields begin to apply AI in data analysis in their respective fields, such as health, finance, education, and others. In the field of education, it is currently known as Ecuation Data Mining (EDM) which is a scientific discipline for exploring data originating from educational contexts. At metamedia university there are already many information systems used to process student data. With this data warehouse, the authors try to perform data analysis using the CRIPS-DM method. CRIPS-DM is an industry independent process model for data mining. One of the problems that occurs is failure in the professional labor exam. The author tries to apply the naïve Bayes machine learning algorithm to analyze the causes of student failure. The data that is processed is data on the value of labor exams, data on the value of 6 courses. To evaluate the accuracy of the writer using a confusion matrix with an accuracy rate of 68.75%.

Teks Lengkap:

PDF

Referensi


Alizadeh, S. H., Hediehloo, A., & Harzevili, N. S. (2021). Multi independent latent component extension of naive Bayes classifier. Knowledge-Based Systems, 213. https://doi.org/10.1016/j.knosys.2020.106646

Ang, K. L. M., Ge, F. L., & Seng, K. P. (2020). Big Educational Data Analytics: Survey, Architecture and Challenges. In IEEE Access (Vol. 8, pp. 116392–116414). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2020.2994561

Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm ✩. 192, 105361. https://doi.org/10.1016/j.knosys

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. In IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews (Vol. 40, Issue 6, pp. 601–618). https://doi.org/10.1109/TSMCC.2010.2053532

Wu, C., Zhang, Q., Cheng, Y., Gao, M., & Wang, G. (2021). Novel three-way generative classifier with weighted scoring distribution. Information Sciences, 579, 732–750. https://doi.org/10.1016/j.ins.2021.08.025

Zhang, H., & Jiang, L. (2022). Fine tuning attribute weighted naive Bayes. Neurocomputing, 488, 402–411. https://doi.org/10.1016/j.neucom.2022.03.020




DOI: https://doi.org/10.33559/eoj.v5i2.1492

Refbacks

  • Saat ini tidak ada refbacks.


Jumlah Kunjungan

Negara Pengunjung

Flag Counter

Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi 4.0 Internasional.