Perkiraan Masa Tunggu Alumni Mendapatkan Pekerjaan Menggunakan Metode Prediksi Data Mining Dengan Algoritma Naive Bayes Classifier
DOI:
https://doi.org/10.18196/st.212225Keywords:
Forecasting the grace period getting the job, data mining, Naive Bayes Classifier, RapidMinerAbstract
Student and Alumni data Universitas Muhammadiyah Yogyakarta is very common, and one of these is the alumni data obtained from work after the completion of undergraduate studies. Former students are given jobs caused or influenced by a range of factors. This research aims to have the grace period Classification or old alumni gain positions by triggering a process of data extraction and using the Bayes naïve classification algorithm. The algorithms used later succeeded in predicting sooner or later to get a job, the predictive results alumni can be used to make decisions to improve the quality of a university. Research on the support system using several parameters, i.e., gender, faculty, GPA, year of graduation, and job status. The data used are as much as 435, including seven years of 2011-2014 volume. The results of this study have the accuracy level of former students having the grace period come to 71% and of the calculated results of the predictions of the former students obtaining a job at Universitas Muhammadiyah Yogyakarta of the year 2011-2014 the Ensure that the work is carried out more quickly with the status of the slow to deliver the work
References
Defiyanti, Sofi, and Mohamad Jajuli. (2015). “Integrasi Metode Klasifikasi Dan Clustering Dalam Data Mining.” Konferensi Nasional Informatika (KNIF), 39–44.
Fithri, Diana Laily, and Eko Darmanto. (2014). “Sistem Pendukung Keputusan Untuk Memprediksi Kelulusan Mahasiswa Menggunakan Metode Nave Bayes.” In . Muria Kudus University.
Hastuti, Khafiizh. (2012). “Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Mahasiswa Non Aktif.” Semantik 2 (1).
Jananto, Arief. (2013). “Algoritma Naive Bayes Untuk Mencari Perkiraan Waktu Studi Mahasiswa.” Teknologi Informasi DINAMIK 18 (1): 9–16.
Lumenta, Arie SM, and Agustinus Jacobus. (2017). “Prediksi Masa Studi Mahasiswa Dengan Menggunakan Algoritma Naïve Bayes.” Jurnal Teknik Informatika Universitas Sam Ratulangi 11 (1).
Murtopo, Aang Alim. (2016). “Prediksi Kelulusan Tepat Waktu Mahasiswa STMIK YMI Tegal Menggunakan Algoritma Naïve Bayes.” CSRID (Computer Science Research and Its Development Journal) 7 (3): 145–54.
Ridwan, Mujib, Hadi Suyono, and M Sarosa. (2013). “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier.” Eeccis 7 (1): 59–64. https://doi.org/10.1038/hdy.2009.180.
Sabna, Eka, and Muhardi Muhardi. (2016). “Penerapan Data Mining Untuk Memprediksi Prestasi Akademik Mahasiswa Berdasarkan Dosen, Motivasi, Kedisiplinan, Ekonomi, Dan Hasil Belajar.” Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer Dan Teknologi Informasi 2 (2): 41–44.
Saleh, Alfa. (2015). “Implementasi Metode Klasifikasi Naive Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga.” Creative Information Technology Journal 2 (3): 207–17.
Yuda, Nugroho Septian. (2014). “Data Mining Menggunakan Algoritma Naïve Bayes Untuk Klasifikasi Kelulusan Mahasiswa Universitas Dian Nuswantoro.(Studi Kasus: Fakultas Ilmu Komputer Angkatan 2009).” Skripsi, Fakultas Ilmu Komputer.
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