Analyzing and Forecasting Admission data using Time Series Model

Authors

  • Nu'man Normas Muhamad Jurusan Informatika - Universitas Muhammadiyah Surakarta, Indonesia
  • Husni Thamrin Jurusan Informatika - UMS, Indonesia

DOI:

https://doi.org/10.15575/join.v5i1.546

Keywords:

Admission, ARIMA, Autoregression, Forecasting, Time Series

Abstract

Problems that will be faced by higher education institutions, especially in the phase of new student admissions. Careful planning and strategies are needed in dealing with the process of admission of new students. The data for planning can be obtained using the forecasting method. The time series forecasting model is used to get forecasting data. Forecasting data is used for the decision making process. The data of new student admissions obtained is 3-period data (2017 - 2019). The data obtained is stationary. Because the data is stationary, the data does not need differentiation. The data obtained also has a sufficient correlation value, and has a loop on the 7th lag. Before making an application, a test is performed to find a time series model that is suitable for admission data. The tested models are the ARIMA model and the Autoregression model. In testing the forecast timespan, the ARIMA model gets a smaller error value in almost all tests. In the Cross-validation method, the ARIMA Model also gets a smaller RMSECV or MAECV value than the AR model. The ARIMA model was chosen to be implemented into the application. The auto_arima algorithm is used so that applications can adapt to different data. The ARIMA model is implemented into a prediction application using the Python programming language. Application development uses Django as a web-based web application framework. Bootstrap is used to create application interfaces. the result from forecasted data is acceptable for short period.

Author Biographies

Nu'man Normas Muhamad, Jurusan Informatika - Universitas Muhammadiyah Surakarta

Saya Mahasiswa Jurusan Informatika di Universtias Muhammadiyah Surakarta

Husni Thamrin, Jurusan Informatika - UMS

Dosen Jurusan Informatika di Universtias Muhammadiyah Surakarta

References

M. Umar and F. Ismail, “Peningkatan Mutu Lembaga Pendidikan Islam (Tinjauan Konsep Mutu Edward Deming dan Joseph Juran),†J. Ilm. Iqra’, vol. 11, no. 2, Feb. 2018.

I. Wahyudi, “KOMERSIALISASI PENDIDIKAN TINGGI DI INDONESIA,†Tawazun J. Pendidik. Islam, vol. 8, no. 1, pp. 49–70, Apr. 2018.

S. Karmita, A. B. W. Putra, A. F. O. Gaffar, and A. S. Wiguna, “Prediksi Jumlah Calon Mahasiswa Baru Menggunakan Fuzzy Time Series-Time Invariant,†Pros. SAKTI (Seminar Ilmu Komput. dan Teknol. Informasi), vol. 3, no. 1, pp. 208–214, Apr. 2019.

U. M. Surakarta, “One Day Service – PMB UMS,†2016. [Online]. Available: http://pmb.ums.ac.id/ods/. [Accessed: 03-Oct-2019].

D. A. Anggoro and W. Supriyanti, “Aplikasi Sistem Pendukung Keputusan dengan Metode AHP untuk Pemilihan Siswa Berprestasi di SMAN Kebakkramat,†J. Penelit. dan Pengabdi. Kpd. Masy. UNSIQ, vol. 6, no. 3, pp. 163–171, 2019.

A. Purba, “PERANCANGAN APLIKASI PERAMALAN JUMLAH CALON MAHASISWA BARU YANG MENDAFTAR MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOTHING (Studi Kasus : Fakultas Agama Islam UISU),†JURIKOM (Jurnal Ris. Komputer), vol. 2, no. 6, Dec. 2015.

E. Sinaga, A. S. Sembiring, and R. Limbong, “PERANCANGAN APLIKASI PREDIKSI JUMLAH KELULUSAN MAHASISWA DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) (STUDI KASUS : PRODI TI STMIK BUDIDARMA MEDAN),†Inf. dan Teknol. Ilm., vol. 13, no. 3, pp. 308–313, Jan. 2019.

S. Pravilovic, A. Appice, and D. Malerba, “Integrating cluster analysis to the ARIMA model for forecasting geosensor data,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8502 LNAI, pp. 234–243.

H. Thamrin and D. J. Murray-Smith, “A Mathematical Model of the Human Respiratory Control System during Exercise,†Modelling, Simulation, and Identification / 658: Power and Energy Systems / 660, 661, 662. ACTA Press.

A. Azid et al., “Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia,†Water, Air, Soil Pollut., vol. 225, no. 8, p. 2063, Aug. 2014.

S. N. Janah, W. Sulandari, and S. B. Wiyono, “PENERAPAN MODEL HYBRID ARIMA BACKPROPAGATION UNTUK PERAMALAN HARGA GABAH INDONESIA,†MEDIA Stat., vol. 7, no. 2, pp. 63–69, Dec. 2014.

G. PRASETYO, “Prediksi Harga Saham Sektor Keuangan dan Sektor Infrastruktur di Indonesia dengan Model ARIMA,†J. Ilm. Mhs. FEB, vol. 6, no. 2, Jul. 2018.

B. Y. Pandji, I. Indwiarti, and A. A. Rohmawati, “Perbandingan Prediksi Harga Saham dengan model ARIMA dan Artificial Neural Network,†Indones. J. Comput., vol. 4, no. 2, pp. 189–198, Sep. 2019.

S. Sismi and M. Y. Darsyah, “Perbandingan Prediksi Harga Saham PT.BRI, Tbk dengan METODE ARIMA dan MOVING AVERAGE,†Pros. Semin. Nas. Mhs. Unimus, vol. 1, no. 0, Nov. 2018.

M. As’ad, S. S. Wibowo, and E. Sophia, “Peramalan Jumlah Mahasiswa Baru dengan Model Autoregressive Integrated Moving Average (Arima),†J. Inform. Merdeka Pasuruan, vol. 2, no. 3, Dec. 2017.

J. B. Alexander and E. C.-R. Rogelio, SCOPE: Selective Cross-Validation over Parameters for Elo. Salt Lake City: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-19), 2019.

G. Jain and B. Mallick, “A Study of Time Series Models ARIMA and ETS,†SSRN Electron. J., Jan. 2017.

S. D. P. Williams, “The effect of coloured noise on the uncertainties of rates estimated from geodetic time series,†J. Geod., vol. 76, no. 9–10, pp. 483–494, Feb. 2003.

T. Chai, T. Chai, and R. R. Draxler, “Ozone health and ecosystem impacts View project Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature,†Geosci. Model Dev, vol. 7, pp. 1247–1250, 2014.

Downloads

Published

2020-07-16

Issue

Section

Article

Citation Check