Analisis Association Rule Untuk Memprediksi Kelulusuan Mahasiswa F-Kip Universitas Graha Nusantara Padangsidimpuan

  • Al Wendi Universitas Graha Nusantara
  • Andi Saputra Mandopa Universitas Graha Nusantara
  • Erwina Azizah Hasibuan Universitas Graha Nusantara
Keywords: Prediction of Student Graduation, Association Rule.

Abstract

University administrators need graduation predictions to determine initial steps to avoid dropout. The length of a student's study period is determined by various factors. Therefore, it is important to know which students may not graduate on time. Data mining techniques can be used to explore new insights to predict student graduation. By using the association rule technique we can obtain information from large data such as data from universities. The aim of this research is to determine the pattern of study duration for Graha Nusantara University F-KIP students. by using the association rule data mining method and comparing a priori algorithms and hash-based algorithms. The data used is Graha Nusantara University F-KIP master data which is processed using association rule data mining techniques with a priori algorithms and hash-based algorithms with minimum support of 1% and minimum confidence of 1%. The results of data processing with the a priori algorithm are the same as the results of data processing with the hash-based algorithm, namely 49 2-itemset combinations. The pattern that was formed included 7.5% of graduates from the mathematics department studying for more than 5 years with a confidence value of 38.5%.

References

[1] E. Aribowo dan O. SAD, "Analisis Perbandingan Algoritma Apriori Dan Algoritma Hash Based Pada Market Basket Analysis Di Apotek Universitas Ahmad Dahlan," Jurnal Sarjana Teknik Informatika volume 3 Nomor 1, Februari 2015.
[2] D. Fitriati, "Implementasi Data Mining untuk Menentukan Kombinasi Media Promosi Barang Berdasarkan Perilaku Pembelian Pelanggan Menggunakan Algoritma Apriori," Annual Research Seminar volume 2 Nomor 1, Desember 2016.
[3] W. Weku, " Implementasi Data Mining Untuk Menentukan Studi Mahasiswa Menggunakan Algoritma Apriori," Jurnal Matematika Dan Aplikasi Volume 3 Nomor 1 Tahun 2014.
[4] D. Listriani dan H.Setyaningrum, " Penerapan Metode Asosiasi Menggunakan Algoritma Apriori Pada Aplikasi Analisa Pola Belanja Konsumen," Jurnal Teknik Informatika Volume 9 Nomor. 2, Oktober 2016.
[5] T. Pradana, " Penggalian Kaidah Multilevel Association Rule Dari Data Mart Swalayan ASGAP," Jurnal SPIRIT Volume. 7 Nomor. 2 Nopember 2015.
[6] Tampubolon, K., Saragih, H., Reza, B., Epicentrum, K., & Asosiasi, A. (2013). Implementasi Data Mining Algoritma Apriori pada sistem persediaan alat-alat kesehatan. Informasi dan Teknologi Ilmiah (INTI), 1(1), 93-106.
[7] Zega, M., & Fauzi, R. (2023). Penerapan Data Mining pada Transaksi Penjualan Menggunakan Algoritma Apriori di Alfamart Centre Park. Computer and Science Industrial Engineering (COMASIE), 9(5).

[8] Mawarni, R. (2023). Pengolahan Data Mining Terhadap Penjualan Menngunakan Algoritma Apriori pada Toko Alfamart Mulya Asri Tulang Bawang Barat. Jurnal Informatika Software dan Network (JISN), 4(1), 18-26.
[9] Saputra, A., Sari, H. L., & Sartika, D. (2023). Implementasi Metode Association Rule Mining Pada Penjualan Barang Di Toko Bangunan Ada Mas Menggunakan Algoritma Apriori. Jurnal Multidisiplin Dehasen (MUDE), 2(4), 709-718.
[10] Wicaksono, H. C., Witarsyah, D., & Hamami, F. (2023). Analisis Penempatan Produk Retail dengan Metode Asosiasi Menggunakan Algoritma FP-Growth. eProceedings of Engineering, 10(3).
Published
2023-12-08
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