Classification of Graduate Profiles Based on Graduate Tracer Study Using Algorithm Naive Bayes Classifier
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Abstract
The graduate profile is the role of the graduate of the study program or field of expertise / field of work planned after completing education from the study program. The determination of the profile of study program graduates is generally carried out based on the results of the assessment of stakeholder needs. Based on data from the Ministry of Research, Technology and Higher Education, the IT study program is one of the most majors or study programs in Indonesian universities and the highest number of enthusiasts choose this study program each year. Each year, graduates of IT study programs have a large number of graduates, both vocational and non- vocational colleges. The number of IT graduates is large but they have low graduate competencies, even they do not have competencies in the IT field so that their work is not in accordance with the graduate profile that has been designed. Therefore, it is necessary to conduct research to classify the profile of graduates who have worked based on tracer study data using the Naïve Bayes Classifier method. This study uses attributes, namely study program, value criteria, gender and field of work and the labels used are status (Linear and Non-Linear). The results of the study on the classification of the profile of graduates using the Naïve Bayes Classfier method show that alumni work not according to the profile of graduates by 73% and according to the profile of graduates by 23%, with a data accuracy rate of 87% and are included in the good classification category.
##plugins.themes.bootstrap3.article.details##
[2.] Peraturan Presiden, No 8 Tahun 2012 tentang penyusunan kurikulum pendidikan tinggi berbasis Kerangka Kualifikasi Nasional Indonesia(KKNI). Jakarta : Presiden Republik Indonesia.
[3.] Jananto, A. (2013). Algoritma Naive Bayes untuk Mencari Perkiraan Waktu Studi Mahasiswa. Dinamik, 18(1).
[4.] Handayani, F., & Pribadi, F. S. (2015). Implementasi Algoritma Naive Bayes Classifier dalam Pengklasifikasian Teks Otomatis Pengaduan dan Pelaporan Masyarakat melalui Layanan Call Center 110. Jurnal Teknik Elektro, 7(1), 19-24.
[5.] Saleh, A. (2015). Implementasi metode klasifikasi naive bayes dalam memprediksi besarnya penggunaan listrik rumah tangga. Creative Information Technology Journal, 2(3), 207-217.
[6.] Nursalim, S., & Himawan, H. (2014). Klasifikasi bidang kerja lulusan menggunakan algoritma k- nearest neighbor. J Teknolog Informasi, 10(1), 31-43.
[7.] Asikin, M. F., Kurniawaty, D., Sari, S. K., & Cholissodin, I. (2016). Implementasi Metode Naïve Bayes Classifier Untuk Seleksi Asisten Praktikum Pada Simulasi Hadoop Multinode Cluster. Jurnal Teknologi Informasi dan Ilmu Komputer, 3(4), 273-278.
[8.] Septiari, D. (2016). Implementasi Metode Naive Bayes Classifier Dalam Klasifikasi Kelayakan Calon Pendonor Darah (Studi Kasus: PMI Kabupaten Demak). Universitas Dian Nuswantoro, Semarang.
[9.] Bisri, M. H. Implementasi Algoritma Naïve Bayes untuk Memprediksi Penjurusan Siswa di SMA Kesatrian 1 Semarang.
[10.] Rifqo, M. H., & Wijaya, A. (2017). Implementasi Algoritma Naive Bayes dalam Penentuan Pemberian Kredit. Pseudocode, 4(2), 120-128.
[11.] Hemali, Daniela, Chris J. Hinde, and Roger G. Stone. Naive Bayes vs. decision trees vs. neural networks in the classification of training web pages. (2009)