Implementasi Metode Regresi Linier Berganda Dalam Estimasi Tingkat Pendaftaran Mahasiswa Baru

Abdul Zikri Siregar(1*),

(1) Program Studi Sistem Informasi, STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
(*) Corresponding Author

Abstract


Every agency or an organization that wants to survive is needed to improve a proper service. Increasing the amount of capacity is an important thing to note, one way that can be done to estimate the amount of capacity is by data mining. The data mining method used in this case is to use the Multiple Linear Regression algorithm. This algorithm is an analysis that has more than one independent variable. This research was conducted at AMIK Tunas Bangsa Pematangsiantar, by analyzing the data for new students to estimate the number of new student admissions. From the results of this study, the estimated number of new students got 93 people, which previously was 107 people, which means that in the following year there was a decrease in the number of new students from the year previous. This study aims as a recommendation in improving future evaluation.

Full Text:

PDF

References


P. S. Ramadhan and N. Safitri, “Penerapan Data Mining Untuk Mengestimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Deli Serdang,” vol. 18, no. 1, pp. 55–61, 2019.

Y. R. Eggy Inaidi Andana Warih, “Penerapan Data Mining Untuk Menentukan Estimasi,” pp. 1–5.

A. Rivandi, E. Bu, and N. Silalahi, “Penerapan Metode Regresi Linier Berganda Dalam Estimasi Biaya Pencetakan Spanduk (Studi Kasus : PT. Hansindo Setiapratama),” vol. 7, pp. 263–268, 2019.

W. Handoko, “Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Single Exponential Smoothing (Studi Kasus : AMIK Royal Kisaran),” vol. V, no. 2, 2019

D. S. Purnia dan A. I. Warnilah, “Implementasi Data Mining Pada Penjualan Kacamata Menggunakan Algoritma Apriori,” IJCIT (Indonesian Journal on Computer and Information Technology) , vol. 2, pp. 31-39, 2017.




DOI: https://doi.org/10.30645/kesatria.v2i3.73

DOI (PDF): https://doi.org/10.30645/kesatria.v2i3.73.g73

Refbacks

  • There are currently no refbacks.


Published Papers Indexed/Abstracted By: