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Aug 9, 2020
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Aug 26, 2020
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Abstract

Internet quota is the number of limits or limits of usage in internet use.To overcome this, it is necessary to forecast the amount of internet quota usage. The purpose of this study is to predict the amount of internet quota usage using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method commonly called the Box-Jenkins method is a method used for short-term forecasting with the assumption that the time series data used must be stationary, meaning that the average variation of the data in question is constant. The bedt model obtained to predict the amount of internet quota usage is the AR (1) or ARIMA (1,0,0) models. From the forecasting results it can be seen that the amount of internet quota usage is increasing every day


 

Keywords

time series analysis autoregressive integrated moving average (ARIMA) internet quota

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References

  1. [1] Putri, Maria Cendanasari Kurniawan dan Wiwik Anggraeni, “Penerapan Metode Campuran Autoregressive Integrated Moving Average dan Quantile Regression (ARIMA-QR) untuk Peramalan Harga Cabai Sebagai Komoditas Strategis Pertanian Indonesia”, Jurnal Teknik ITS, vol. 7, no. 1, 2337-3520, 2018.
  2. [2] Cryes, D. Jonathan and Kung-Sik Chan, Time Series Analysis With Application in R, Second Edition. Lowa City USA: Springer Science + Bussines Media LLC, 2008.
  3. [3] Iqbalullah, Juniar dan Wiwiek Setya Winahju, “Peramalan Jumlah Penumpang Pesawat Terbang di Pintu Kedatangan Bandar Udara Internasional Lombok dengan Metode ARIMA Box-Jenkins, ARIMAX, dan Regresi Time Series”, JURNAL SAINS DAN SENI POMITS, vol. 3, no. 2, ISSN: 2337-3539, 2301-9271 Print, 2014.
  4. [4] Panjaitan, Helmi, Alan Prahutama, dan Sudarno, “Peramalan Jumlah Penumpang Kereta Api Menggunakan Metode Arima, Intervensi Dan Afrima”, Jurnal Gaussian, vol. 7, no. 1, halaman 96-109, 2018.
  5. [5] Salwa, Nany, Nidya Tatsara, Ridha Amalia, dan Aja Fatimah Zohra, “Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average), Journal of Data Analysis, vol. 1, no. 1, p. 21-31, 2018.
  6. [6] Perdana K, Echo, Olah Data Skripsi dengan SPSS, LABKOM MANAJEMEN UBB: Pangkalpinang, 2016.
  7. [7] Ispriyanti, Dwi, “Pemodelan Statistika Dengan Transformasi Box-Cox”, Jurnal Matematika Dan Komputer, vol. 7, no. 3, ISSN: 1410-8518, 2004.
  8. [8] Veronica, Roshynta, “Peramalan Jumlah Pengunjung Jawa Timur Park I Menggunakan ARIMA BOX-JENKINS”, Institut Teknologi Sepuluh Nopember, Surabaya, 2015.
  9. [9] Wei, William W.S, Time Series Analysis Univariate and Multivariate Methods, Second Edition. New York: Pearson Addison Wesley, 2006.
  10. [10] Azriati, Kiki Febri, Abdul Hoyyi, dan Moch. Abdul Mukid, “Verifikasi Model Arima Musiman Menggunakan Peta Kendali Moving Average”, Jurnal Gaussian, vol. 3, no. 4, halaman 701-710, 2014.
  11. [11] Yuniarti, Desi, “Peramalan Jumlah Penumpang yang Berangkat Melalui Bandar Udara Temindung Samarinda Tahun 2012 dengan Metode ARIMA Box-Jenkins”, Jurnal Eksponensial, vol. 3, no. 1, ISSN: 2085-7829, 2012.
  12. [12] Al’afi, Anis Mahmud, Widiarti, Dian Kurniasari, dan Mustofa Usman, “Peramalan Data Time Series Seasonal Menggunakan Metode Analisis Spektral”, Jurnal Siger Matematika, vol. 1, no.1, 2020.
  13. [13] Fitriyani, Ellya Helmud, “Pemilihan Paket Internet Android Pada Operator Telepon GSM Menggunakan Metode Analytical Hierarchy Process (AHP)”, Jurnal Sistem Infromasi, vol. 8, no. 1, ISSN Print: 2085-1588, 2016.
  14. [14] Tama, Agi, “Regulasi Perbedaan Harga Terhadap Kualitas Jaringan dan Jumlah Kuota Paket Data/Internet Prabayar dan Pascabayar”, Institut Saind dan Teknologi Nasional, Jakarta, 2018.
  15. [15] Hanim, Yulia Masnunah Hanim, “Penerapan Regresi Time Series dan ARIMAX untuk Peramalan Inflow dan Outflow Uang Kartal di Jawa Timur, DKI Jakarta dan Nasional”, Institut Teknologi Sepuluh Nopember, Surabaya, 2015.