Model Prediksi Penempatan Magang Siswa SMK menggunakan Teknik Association Rule Mining
Insternship activity is one of the core activities of every Vocational School (SMK) as the purpose of this school is to conduct education at the level of work-oriented readiness. Every SMK graduate is expected to be better prepared to enter the industrial world. However, in fact there were gaps that resulted in the unpreparedness of students after graduating from school. This research identified and analyzed the placement of student internships. The aim was to find an insternship placement pattern in order to get an overview and recommendation of an appropriate internship according to students abilities. The technique used was the association rule mining, a technique of the data mining method that was useful for uncovering the rules that were correlated to each other so that they can better organize and predict the internship placements. The results showed that the association rule mining could be applied to analyze student performance and predict internship placements in the future. This prediction could be a consideration for the teacher to determine the subjects that need to be improved to prepare students for internships.
 F. S. Hadi, A. Mukhadis, and A. Nyoto, “Hambatan Dan Faktor Penyebabnya Prakerin Ditinjau Dari Persiapan, Pelaksanaan, Dan Evaluasi Kompetensi Keahlian Teknik Pemesinan Di Smk,” Teknologi dan Kejuruan: Jurnal Teknologi, Kejuruan, dan Pengajarannya, vol. 40, no. 2, pp. 99–114, Sep. 2017.
 N. L. Cintya Dewi, A. Prasteya Wibawa, and U. Pujianto, “Technology Acceptance Model on Internship Placement Recommendation System Based on Naïve Bayes,” in 2018 International Conference on Sustainable Information Engineering and Technology (SIET), 2018, no. 1, pp. 151–155.
 R. Syahputra and W. Safitri, “Analisa Tingkat Prestasi Atlet Karate Sumatera Barat Menggunakan Algoritma Data Mining,” Indonesian Journal of Computer Science, vol. 7, no. 2, pp. 200–210, Oct. 2018.
 B. Siswanto and P. Thariqa, “Association Rules Mining for Identifying Popular Ingredients on YouTube Cooking Recipes Videos,” in 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), 2018, pp. 95–98.
 T. Devasia, Vinushree T P, and V. Hegde, “Prediction of students performance using Educational Data Mining,” in 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 2016, pp. 91–95.
 K. Parmar, D. Vaghela, and P. Sharma, “Performance prediction of students using distributed data mining,” in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, no. Ddm, pp. 1–5.
 A. Jamil, M. Ahsan, T. Farooq, A. Hussain, and R. Ashraf, “Student Performance Prediction Using Algorithms of Data Mining,” in 2018 International Conference on Computing, Engineering, and Design (ICCED), 2018, pp. 244–249.
 L. Jena and N. K. Kamila, “A Model for Prediction of Human Depression Using Apriori Algorithm,” in 2014 International Conference on Information Technology, 2014, pp. 240–244.
 D. Prangchumpol, “Improving the performance of network traffic prediction for academic organization by using association rule mining,” in Fourth edition of the International Conference on the Innovative Computing Technology (INTECH 2014), 2014, vol. I, pp. 93–96.
 O. Chandrakar and J. R. Saini, “Predicting Examination Results using Association Rule Mining,” International Journal of Computer Applications, vol. 116, no. 1, pp. 7–10, Apr. 2015.
 V. Ciriani, S. D. C. di Vimercati, S. Foresti, and P. Samarati, “k-Anonymous Data Mining: A Survey,” in Privacy-Preserving Data Mining: Models and Algorithms, C. C. Aggarwal and P. S. Yu, Eds. Crema: Springer, 2008, pp. 105–136.
 N. Sharma and C. Kant Verma, “Association Rule Mining: An Overview,” International Journal of Computer Science & Communication, vol. 5, no. 1, pp. 10–15, 2014.
 J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques, Third. United States of America: Morgan Kaufmann Publishers, 2012.
Copyright (c) 2020 Dwi Welly Sukma Nirad
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.