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Implementasi Metode Runtun Waktu dalam Pemodelan Total Harga Alat Kedokteran dan Kesehatan
Abstract
Hospital is an institution or health service that provides total individual health care by providing outpatient, inpatient, and emergency services. The health services that will be provided are promotive, preventive, and rehabilitative services. One of the efforts to improve the quality of hospital services is to provide good health services. In terms of supporting the health services provided, a health management is needed. The high price of medical supplies and equipment is due to several other factors causing hospitals to be able to make plans in the procurement of medical equipment and hospital medicine. Therefore, the author uses the Autoregressive Moving Average (ARMA) method in this study to predict the Total Price of Medical and Health Equipment Needed at the Sleman Regional General Hospital in the coming period. Based on the analysis that has been found, one significant and best ARMA model is obtained with the AIC value of 223.92 with equation and the MAPE value of 18.78%, which means the accuracy of the forecasting is 81.22%.
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Anderson, D.R., Burnham, K.P. and White, G.C. (1998) ‘Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies’, Journal of Applied Statistics, 25(2), pp. 263–282.
Andrianto, E., Wahyuni, F.S. and Prasetya, R.P. (2022) ‘Aplikasi sistem peramalan ketersediaan stok barang di toko Mebel Abadi Jaya menggunakan metode Single Moving dan Exponential Smoothing’, JATI (Jurnal Mahasiswa Teknik Informatika), 6(1), pp. 336–341.
Arista, L. (2010) Aplikasi metode ARIMA untuk perkiraan jumlah wisatawan asing di pulau Samosir Sumatera Utara tahun 2011-2013 berdasarkan data tahun 2005-2009, Universitas Sumatera Utara. Universitas Sumatera Utara.
Bonakdari, H., Pelletier, J.P. and Martel-Pelletier, J. (2020) ‘A reliable time-series method for predicting arthritic disease outcomes: New step from regression toward a nonlinear artificial intelligence method’, Computer Methods and Programs in Biomedicine, 189, p. 105315. Available at: https://doi.org/10.1016/j.cmpb.2020.105315.
Box, G.E.P. et al. (2015) Time series analysis: forecasting and control. 5th edn, Syria Studies. 5th edn. New Jersey: Wiley Series in Probability and Statistics.
Farosanti, L., Mubarok Husni and Indrianto (2022) ‘Analisa Peramalan Penjualan Alat Kesehatan dan Laboratorium di PT. Tristania Global Indonesia Menggunakan Metode ARIMA’, Jurnal Informatika Merdeka Pasuruan, 7(1), pp. 14–18.
Fildes, R. and Goodwin, P. (2007) ‘Against your better judgment? How organizations can improve their use of management judgment in forecasting’, Interfaces, 36(6).
Hartati, H. (2017) ‘Penggunaan metode ARIMA dalam meramal pergerakan Inflasi’, Jurnal Matematika Sains dan Teknologi, 18(1), pp. 1–10. Available at: https://doi.org/10.33830/jmst.v18i1.163.2017.
Hasibuan, A.R., Ramdhan, W. and Latiffani, C. (2022) ‘Penerapan metode Single Moving Average (SMA) persediaan jarum suntik pada Puskesmas air joman’, J-Com (Journal of Computer), 2(2), pp. 121–128.
Hussein, S. (2021) Visualisasi data: pengertian, jenis, metode, dan contohnya. Available at: https://geospasialis.com/visualisasi-data/ (Accessed: 21 August 2022).
Kim, S. and Kim, H. (2016) ‘A new metric of absolute percentage error for intermittent demand forecasts’, International Journal of Forecasting, 32(3), pp. 669–679. Available at: https://doi.org/10.1016/j.ijforecast.2015.12.003.
Kusuma, B.S. (2015) ‘Analisa peramalan permintaan air minum dalam kemasan pada PT. XYZ dengan metode least square dan standard error of estimate’, Malikussaleh Industrial Engineering Journal, 4(1), pp. 42–47.
Menteri Kesehatan Republik Indonesia (1976) PERMENKES RI Nomor : 220/Men.Kes/Per/IX/76 tentang produksi dan peredaran kosmetika dan alat kesehatan. Indonesia.
Primandari, A.H. and Kartikasari, M.D. (2020) Analisis Runtun Waktu dengan R. 1st edn. Yogyakarta: Universitas Islam Indonesia.
Rosadi, D. (2016) Statistical analysis with R. 1st edn. Yogyakarta: Gadjah Mada University Press.
Rusdi (2011) ‘Deteksi stasioneritas data runtun waktu melalui uji akar-akar unit’, Jurnal Sainstek, 3(1), pp. 78–89.
Salwa, N. et al. (2018) ‘Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average)’, Journal of Data Analysis, 1(1), pp. 21–31. Available at: https://doi.org/10.24815/jda.v1i1.11874.
Sulistyowati, C., Farihah, E. and Hartadinata, O.S. (2020) Anggaran perusahaan: teori dan praktik. Edited by N. Sasikirono. Scopindo Media Pustaka.
Vivas, E., Allende-Cid, H. and Salas, R. (2020) ‘A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score’, Entropy. MDPI AG, pp. 1–24. Available at: https://doi.org/10.3390/e22121412.
Wulandari, R.A. and Gernowo, R. (2019) ‘Metode Autoregressive Integrated Moving Average (ARIMA) dan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam analisis curah hujan’, Berkala Fisika, 22(1), pp. 41–48.
DOI: https://doi.org/10.34312/jjps.v4i1.17873
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