Application to predict the new student’s score using time series algorithm

Authors

  • Sinar Nadhif Ilyasa Universitas Muhammadiyah Surakarta
  • Husni Thamrin Universitas Muhammadiyah Surakarta

Keywords:

Time series, Django framework, Auto Regression, ARMA, Triple Exponential Smoothing

Abstract

With the rapid development of information technology in this era, data accuracy is essential in our daily lives to solve existing problems. The existence of information is beneficial in helping the decision-making process. Therefore, any existing information can be further processed and analyzed to be used as new knowledge so that it is useful to determine the right decision. The purpose of this research is to determine whether an application using the time series algorithm such as Auto Regression, ARMA (Auto Regression Moving Average), and Triple Exponential Smoothing model. They can forecast prediction scores that may help to solve the student's admission problem. In this case of the project, the researcher found that the Universitas Muhammadiyah Surakarta's admission system is not evaluated correctly in accepting students and controlling incoming students' quality due to the lack of insights. This time series application is one solution to help manage incoming students' quality and quantity, especially in the Universitas Muhammadiyah Surakarta. This application is developed using a web framework called Django, a full-stack Python web framework that encourages rapid growth and clean, pragmatic design. The Auto Regression model is chosen as a prediction model in One Day Service (ODS) Universitas Muhammadiyah Surakarta. It has a better performance than ARMA and Triple Exponential Smoothing and a higher chance to avoid overfitting than the other two models that are more complex for the ODS data.

References

B. Handaga and H. Amrullah, “Sistem Informasi Akademik Untuk Layanan Mahasiswa Ums Berbasis Mobile,†Emitor, vol. 16, no. 02, pp. 9–20, 2017.

Nia Komalasari, “Sistem Pendukung Keputusan Kelaikan Terbang (SPK2T),†J. Ind. Elektro dan Penerbangan 4, vol. 4, no. 1, pp. 1–11, 2014.

D. H. Kamagi and S. Hansun, “Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa,†J. Ultim., vol. 6, no. 1, pp. 15–20, 2014, doi: 10.31937/ti.v6i1.327.

A. Rohman, “Model Algoritma K-nearest neighbor (K-NN) untuk prediksi kelulusan mahasiswa,†Neo Tek. J. Ilm. Teknol., vol. 1, no. 1, 2015.

H. Thamrin, “Perancangan Tools Berbasis Python Untuk Memantau Keaktifan Server,†Komuniti, vol. 2, 2017.

A. Khosravi, L. Machado, and R. O. Nunes, "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Appl. Energy, vol. 224, no. September, pp. 550–566, 2018, doi: 10.1016/j.apenergy.2018.05.043.

H. Zhang, S. Zhang, P. Wang, Y. Qin, and H. Wang, "Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China," J. Air Waste Manag. Assoc., vol. 67, no. 7, pp. 776–788, 2017, doi: 10.1080/10962247.2017.1292968.

N. A. Bakar and S. Rosbi, "Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction," Int. J. Adv. Eng. Res. Sci., vol. 4, no. 11, pp. 130–137, 2017, doi: 10.22161/ijaers.4.11.20.

H. Moeeni, H. Bonakdari, and I. Ebtehaj, "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resour. Manag., vol. 31, no. 7, pp. 2141–2156, 2017, doi: 10.1007/s11269-017-1632-7.

N. S. Arunraj, D. Ahrens, and M. Fernandes, "Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry," Int. J. Oper. Res. Inf. Syst., vol. 7, no. 2, pp. 1–21, 2016, doi: 10.4018/ijoris.2016040101.

P. Sutthichaimethee and D. Ariyasajjakorn, "Forecasting energy consumption in short-term and long-term period by using ARIMAX Model in the construction and materials sector in Thailand," J. Ecol. Eng., vol. 18, no. 4, pp. 52–59, 2017, doi: 10.12911/22998993/74396.

M. F. Ani, S. R. Kamat, and M. Fukumi, "Development of Decision Support System via Ergonomics Approach for Driving Fatigue Detection," vol. 1, no. 1, pp. 60–72, 2020.

H. Khan and P. Pohwani, "Testing Phillips Curve in Pakistan," J. Public Value Adm. Insight, vol. 3, no. 3, pp. 145–152, 2020.

X. Ying, "An Overview of Overfitting and its Solutions," J. Phys. Conf. Ser., vol. 1168, no. 2, 2019, doi: 10.1088/1742-6596/1168/2/022022.

F. Rahmi Ras, H. Nelly Astuti, and B. Efori, “Perancangan Sistem Pakar Diagnosa Penyakit Asidosis Tubulus Renalis Menggunakan Metode Certainty Factor Dengan Penelusuran Forward Chaining,†Media Inform. Budidarma, vol. 1, no. 1, pp. 13–16, 2017.

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Published

2020-09-28

Issue

Vol. 14 No. 3 (2020): September 2020

Section

Articles

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