Forecasting of The Number of Schizophrenia Disorder by using The Box-Jenkins of Time Series Analysis

Syifa Putri Humaira, Indah Nursuprianah, Darwan Darwan

Abstract


Schizophrenia is a mental disorder with a complex brain disorder that causes sufferers not to be able to distinguish between reality and imagination. This study aims to determine the parameters for the best model Box-Jenkins time series analysis in predicting the number of schizophrenic in Cirebon City in 2018 as seen from the smallest MSE (Mean Square Error) value. This study uses Box Jenkins method (often referred to as the ARIMA Method) with documentation collection techniques and literature studies. The documentation aims to collect data on the number of schizophrenic patients in January 2014 to December 2018. Data were analyzed in several stages, namely the stage of data stationary identification, determining model parameter estimates, model verification and forecasting. The results of this study show that the best model for predicting the number of schizophrenic patients in the future is ARIMA (0,1,1). The forecasting results of the number of schizophrenic patients in Cirebon City from January to December 2018 respectively are 69 people, 68 people, 68 people, 68 people, 68 people, 68 people, 67 people, 67 people, 67 people, 67 people, 67 people, 67 people.

Keywords


Forecasting, Schizophrenia, Box-Jenkins Method

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References


E. Suherman, “Strategi Pembelajaran Matematika Kontemporer, Bandung. Universitas Pendidikan Indonesia, 2001.

I. Nursuprianah, M. Mahsusin, “Pengaruh Penggunaan Alat Peraga Model Pythagoras Terhadap Kemampuan Matematika Siswa (Studi Eksperimen di SMP Negeri Pamarican Kabupaten Ciamis)”, Eduma : Mathematics Education Learning And Teaching, 1(1), 2012, doi:http://dx.doi.org/10.24235/eduma.v1i1.278

L. A. King, “Psikologi umum: Sebuah pandangan apresiatif”, Jakarta: Salemba Humanika, 2010.

Riskesdas, “Jumlah Penderita Skizofrenia di Indonesia”, access date: 15 Oktober 2017, 2013, doi: www.depkes.go.id/resources/download/general/Hasil%20Riskesdas%202013.pdf

M. Khashei, M. A. Montazeri, M. Bijari,"Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.1, No.1, pp.21-34, 2015.DOI: 10.5815/ijmsc.2015.01.03

A. Asad, M. Ali., “Forecasting of Daily Gold Price by Using Box Jenkins Methodology”, International Journal of Asian Social Science, 2016, 6 (11). 614-624. doi: 0.18488/journal.1/2016.6.11/1.11.614.624

D. S. Dwitanto, “Analisis Runtun Waktu untuk Meramalkan Jumlah Pasien yang Berobat di Puskesmas Blora dengan Menggunakan Software Minitab 14”, Semarang. Universitas Negeri Semarang, 2011.

S. Green, “Time Series Analysis of Stock Prices Using the Box Jenkins Approach”, Electronic Theses and Dissertations, 2011, 668. https://digitalcommons.georgiasouthern.edu/etd/668

Sutarti, “Penggunaan Metode Runtun Waktu dengan Bantuan Minitab 11 for window untuk Forecasting Produksi Textil Pada PT. Primatexco Indonesia Kabupaten Batang Tahun 2009”, Universitas Negeri Semarang, 2009.

A. Ismail, N. I. Hassan, and S. Endot, “Box-Jenkins Method of Analysing Rate of Cancer Deaths at a Public University Hospital in East Coast of Malaysia”, IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), 7-9 April, Langkawi, Malaysia, 2013.

W. Jacobs, A. M. Souza, and R. R. Zanini, “Combination of Box-Jenkins and MLP/RNA Models for Forecasting”, IEEE LATIN AMERICA TRANSACTIONS, VOL. 14, NO. 4, APRIL, 2016.

S. M. T. Ghomi, and K. Forghani, “Airline passenger forecasting using neural networks and Box–Jenkins”, 12th International Conference on Industrial Engineering (ICIE 2016), January 25-26, Kharazmi University - Tehran, Iran, 2016.

F. Chen, H. Garnier, M. Gilson, J. Aguaero, and T. Liu, “Refined instrumental variable parameter estimation of continuous-time Box-Jenkins models from irregularly sampled data”, IET Control Theory & Applications, Vol. 11, Issue:2, pp. 291-300, ISSN 1751-8644, 2016.

W. Allafi, C. Zhang, D. Quang, J. Marco, and K. Uddin, “Parameter Estimation of Hybrid Fractional-Order Hammerstein-Weiner Box-Jenkins Models Using RIVCF Method”, 26th International Conference on Systems Engineering (ICSEng), IEEE, 18-20 Dec, Sydney, Australia, 2018.

Y. Zou, X. Tang, L. Li, and L. Yu, “A Hybrid Initialization Method for the Maximum Likelihood Estimation of Box-Jenkins Models”, 37th Chinese Control Conference, 25-27 July, IEEE, Wuhan, China, 2018.

V. Breschi, D. Piga, and A. Bemporad, “Maximum-a-posteriori estimation of jump Box-Jenkins models”, IEEE 58th Conference on Decision and Control (CDC) Palais des Congrès et des Expositions Nice Acropolis Nice, France, December 11-13, 2019.

T. Zong, J. Li, X. Li, L. Shang, and X. Zhang, “Parameter identification of Box-Jenkins systems based on the particle swarm optimization”, 31th Chinese Control and Decision Conference (CCDC), 3-5 June, IEEE, Nanchang, China, 2019.

B. Siregar, E. B. Nababan, A. Yap, U. Andayani, and Fahmi, “Forecasting of Raw Material Needed for Plastic Products Based in Income Data Using ARIMA Method”, 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), IEEE, 6-8 Oct, Malang, Indonesia, 2017.

A. P. Slavia, E. Sutoyo, and D. Witarsyah, “Hotspots Forecasting Using Autoregressive Integrated Moving Average (ARIMA) for Detecting Forest Fires”, IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), 5-7 Nov, Bali, Indonesia, 2019.

M. Sikalubya, X. Shiwei, Y. Wen, and P. Moonga, “Study on Forecasting Soybean Production: An Application of ARIMA Model, International Conference on Intelligent Computing”, Automation and Systems (ICICAS), IEEE, 6-8 Dec, Chongqing, China, 2019.

O. Awe, A. Okeyinka, and O. J. Fatokun, “An Alternative Algorithm for ARIMA Model Selection, International Conference in Mathematics”, Computer Engineering and Computer Science (ICMCECS) 978-1-7281-3126-9, 2020.

A. I. Sulimov, M. A. Sadovnikov, A. A. Galiev, A. V. Karpov, and O. N. Sherstyukov, “Predictability Assess of Multipath Phase Using ARIMA Model”, Systems of Signals Generating and Processing in the Field of on Board Communications, IEEE, 19-20 March, Moscow, Rusia, 2020.

Y. Xie, M. Jin, Z. Zou, G. Xu, D. Feng, W. Liu, and D. Long, “Real-Time Prediction of Docker Container Resource Load Based on A Hybrid Model of ARIMA and Triple Exponential Smoothing”, IEEE Transactions on Cloud Computing, April 22, 2020.

H. Yu, L. J. Ming, R. Sumei, Z. Shuping, “A Hybrid Model for Financial Time Series Forecasting-Integration of EWT ARIMA With The Improved ABC Optimized ELM”, IEEE Access, pp.84501-84518, April 13, 2020.

Y. Wang and Y. Guo, “Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost”, China Communications, IEEE, pp.205-221, April 7, 2020.

D. Lee, D. Lee, M. Choi and J. Lee, “Prediction of Network Throughput using ARIMA”, International Conference on Artificial Intelligence in Information and Communication (ICAIIC), IEEE, Feb 19-21 February, Fukuoka, Japan, 2020.

O. Haffner, E. Kucera, and M. Moravcik, “Sales Prediction of Svijany Slovakia, Ltd. Using Microsoft Azure Machine Learning and ARIMA”, Cybernetics & Informatics (K&I), IEEE, Jan 29 – Feb 1, Velke Karlovice, Czech Republic, 2020.

S. Arikunto, “Prosedur Penelitian Suatu Pendekatan Praktik”, Jakarta. Rineka Cipta, 2014.

S. Makridakis, S. C. Wheelwright, and V. E. McGee, “Metode dan Peramalan Jilid 1 (Ir. Untung Sus Ardiyanto, M.Sc & Ir. Abdul Basith, M.Sc. terjemah) (2 ed.)”, Jakarta. Erlangga, 1999.

W. S. Wei, “Time Series Analysis, Univariate and Multivariate Method Second Edition”, New York. Pearson Education, 2006.

N. Iriawan, “Mengolah Data Statistik dengan Mudah Menggunakan Minitab 14”, Yogyakarta. Andi, 2006.

S. Martha, “Peramalan Data Time Series Menggunakan Metode Box-Jenkins”, Pontianak. Universitas Tanjung Pura, 2010.




DOI: https://doi.org/10.18196/jrc.1640

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