COMPARISON OF LEAST SQUARE AND QUADRATIC METHODS ON PREDICTION THE NUMBER OF NEW STUDENT APPLICANTS

Atin Hasanah; Kusrini Kusrini; Kusnawi Kusnawi

doi:10.52436/1.jutif.2023.4.6.1124

Atin Hasanah PJJ Master's Study Program in Informatics Engineering, Universitas AMIKOM Yogyakarta, Indonesia
Kusrini PJJ Master's Study Program in Informatics Engineering, Universitas AMIKOM Yogyakarta, Indonesia
Kusnawi PJJ Master's Study Program in Informatics Engineering, Universitas AMIKOM Yogyakarta, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.6.1124

Keywords: least square, MAE, MAPE, MSE, prediction, quadratic

Abstract

New student registration is held every year with several mechanisms. However, in recent years the number of applicants has decreased even though it had experienced a surge in the previous year. So that, it is necessary to have a prediction to predict the number of applicants in the coming year. In addition, the results of these predictions can be used as material for consideration in determining the quota/ceiling for the number of new student admissions in the following academic year. This research used the Least Square and Quadratic methods to predict the number of new student applicants based on data on the number of applicants from the 2014/2015 to 2022/2023 academic years. Performance testing of the two methods was tested with three (3) testing methods : MAE, MAPE, and MSE. The performance test found that the Quadratic method is more suitable with the MAPE value in the "Good" forecasting accuracy category, which is 11%. For the MAE value, it gets 452,17 and an MSE of 302069,04. While Least Square produces a MAPE value in the "Enough" forecasting accuracy category of 30%, for the MAE value, it gets 996,97 and an MSE of 1494205,36.

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References

I. S. Machfiroh, W. A. Alam Sur, and R. T. Pangestu, “Trend Semi Average and Least Square in Forecasting Yamaha Motorcycle Sales,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 1, pp. 343–354, 2022, doi: 10.30598/barekengvol16iss1pp341-352.

A. P. Silalahi and H. G. Simanullang, “Prediksi Jumlah Pasien Covid-19 Di Indonesia Menggunakan Least Square Method Berbasis Android,” J. Inform. Manaj. dan Komput., vol. 14, no. 1, pp. 86–93, 2022, doi: 10.36723/juri.v14i1.328.

A. Ridwan, A. Faisol, and F. Santi Wahyuni, “Penerapan Metode Least Square Untuk Prediksi Penjualan Berbasis Web Pada Doni Sport Malang,” JATI (Jurnal Mhs. Tek. Inform., vol. 4, no. 1, pp. 129–136, 2020, doi: 10.36040/jati.v4i1.2745.

S. Putri and C. Astiti, “Penerapan Metode Least Square Dalam Perhitungan Proyeksi Jumlah Penduduk,” SEPREN (Journal Math. Educ. Applied), vol. 04, no. 02, pp. 147–154, 2023, doi: 10.36655/sepren.v4i1.

I. Rahmawati and R. Wijanarko, “Implementasi Prediksi Penjualan Obat Menggunakan Metode Least Square Pada Apotek Demak Farma Jaya,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 1, no. 1, pp. 19–23, 2019, doi: 10.36499/jinrpl.v1i1.2762.

M. Rahmawita and I. Fazri, “Aplikasi Peramalan Penjualan Obat Menggunakan Metode Least Squaredi Rumah Sakit Bhayangkara,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 4, no. 2, p. 201, 2018, doi: 10.24014/rmsi.v4i2.5685.

I. Dewi, Nindian Puspa; Listiowarni, “Peramalan Harga Bahan Proyek Menggunakan Metode Least Square (Studi Kasus : CV Rizky Mulya),” J-TFA (Jurnal Teknol. Inform., vol. 2, no. 1, pp. 1–33, 2019, [Online]. Available: https://jurnal.ummu.ac.id/index.php/J-TIFA/article/view/240/150

J. S. Widjaya, D. Agushinta R, and S. R. Puspita Sari, “Sistem Prediksi Jumlah Pasien Covid-19 Menggunakan Metode Trend Least Square Berbasis Web,” Sistemasi, vol. 10, no. 1, pp. 39–51, 2021, doi: 10.32520/stmsi.v10i1.1036.

R. W. N. Octavia and U. Chotijah, “Implementasi Metode Least Square Untuk Prediksi Penjualan Kue Donat dan Bomboloni,” Jutisi (Jurnal Ilm. Tek. Inform. dan Sist. Informasi), vol. 11, no. 1, pp. 251–262, 2022, doi: 10.35889/jutisi.v11i1.802.

D. Swanjaya and D. Putra Pamungkas, “Analisa Hasil Prediksi Metode Least Square menggunakan Korelasi dan MAPE pada Toko PS,” Gener. J., vol. 5, no. 1, pp. 11–18, 2021, doi: 10.29407/gj.v5i1.15440.

D. Nasyika, S. Slamin, and P. Pandunata, “Sistem Prediksi Jumlah Permintaan Produk Darah Menggunakan Metode Least Square Regression Line (Studi Kasus : Utd Pmi Kabupaten Jombang),” INFORMAL Informatics J., vol. 3, no. 2, pp. 48–55, 2018, doi: 10.19184/isj.v3i2.9989.

S. R. Garuda and Y. Baliadi, “Aplikasi Metode Tren Waktu Satu Ragam Dalam Peramalan Toleransi Komoditas Pangan Terhadap Perubahan Iklim Di Papua,” J. Inform. Pertanian., vol. 27, no. 1, pp. 35–46, 2018.

A. Rifa’i, “The Statistical Parabolic Projection Method Untuk Forecasting,” J. Manaj. dan Bisni, vol. 8, no. 2, pp. 354–365, 2019, doi: 10.34006/jmbi.v8i2.137.

D. Pratama and M. Ardhiansyah, “Analisis Perbandingan Metode Least Square dan Parabolik untuk Perhitungan Forecasting Penjualan Minuman Pada Kedai Rumah Celoteh,” INFORMATIKA, vol. 3, no. 1, pp. 78–91, 2022.

I. Simamora, “Metode Trend Non Linear Untuk Forecasting Komposisi Penduduk Kabupaten Tapanuli Tengah Menurut Jenis Kelamin Tahun 2006-2019,” J. Curere, vol. 02, no. 02, pp. 175–183, 2018, doi: 10.36764/jc.v2i2.

R. R. Pratama, “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 2, pp. 302–311, 2020, doi: 10.30812/matrik.v19i2.688.

W. Kurniadi, “Pendukung Keputusan Dalam Peramalan Penjualan Ayam Broiler Dengan Metode Trend Moment Dan Simple Moving Average Pada CV. Merdeka Adi Perkasa,” J. Media Inform. Budidarma, vol. 2, no. 3, pp. 76–90, 2018, doi: 10.30865/mib.v2i3.652.

M. Hatta and A. Fauziah Fitri, “Sistem Prediksi Persediaan Stok Darah Dengan Metode Least Square Pada Unit Transfusi Darah Studi Kasus PMI Kota Cirebon,” J. Ilm. Ilmu Komput., vol. 6, no. 1, pp. 41–45, 2020, doi: 10.35329/jiik.v6i1.130.

R. Maulana Fauzi and D. Iskandar Mulyana, “Implementasi Data Mining Menggunakan Metode Least Square untuk Memprediksi Penjualan Lampu LED pada PT. Sumber Dinamika Solusitama,” J. Sos. Teknol., vol. 1, no. 8, pp. 907–919, 2021, doi: 10.36418/jurnalsostech.v1i8.182.

I. Zikri, S. Safrida, E. Susanti, and R. A. Putri, “Analysis of trend and determinant factors of imported soybean in the period of 2003-2022,” Adv. Food Sci. Sustain. Agric. Agroindustrial Eng., vol. 3, no. 1, pp. 17–24, 2020, doi: 10.21776/ub.afssaae.2020.003.01.3.

A. Navlani, A. Fandango, and I. Idris, Python Data Analysis : Perform Data Collection, Data Processing, Wrangling, Visualization, and Model Building Using Python, Third edit. Birmingham, Mumbai: Pact Publishing Ltd, 2021.

W. M. Baihaqi, M. Dianingrum, and K. A. N. Ramadhan, “Regresi Linier Sederhana Untuk Memprediksi Kunjungan Pasien Di Rumah Sakit Berdasarkan Jenis Layanan Dan Umur Pasien,” J. Simetris, vol. 10, no. 2, pp. 671–680, 2019, [Online]. Available: https://www.jurnal.umk.ac.id/index.php/simet/article/view/3484.