MODEL SEASONAL ARIMA BOX-JENKINS DALAM PERAMALAN PENJUALAN SURAT KABAR HARIAN SERAMBI INDONESIA
DOI:
https://doi.org/10.31100/histogram.v6i2.2407Keywords:
Deret Waktu, SARIMA, Serambi IndonesiaAbstract
Peramalan penjualan merupakan hal yang lumrah dilakukan dalam era persaingan global yang tinggi saat ini. Di dunia industri informasi, industri penyebaran berita marak didistribusikan dengan berita digital. PT. Aceh Grafika masih memproduksi surat kabar cetak yaitu harian Serambi Indonesia. Perusahaan ini ingin mendapatkan model peramalan penjualan surat kabar Serambi Indonesia untuk dasar produksidi periode-periode selanjutnya. Metode time series yang dapat digunakan untuk meramalkan penjualan salah satunya adalah model Seasonal ARIMA Box-Jenkins atau yang lebih dikenal dengan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Data yang digunakan dalam penelitian ini berupa data penjualan surat kabar harian Serambi Indonesia dari tanggal 1 Januari 2021 hingga 31 Desember 2021. Tujuan penelitian ini yaitu memperoleh model peramalan terbaik dari data penjualan surat kabar harian dan memperoleh hasil peramalan penjualan surat kabar harian untuk bulan Januari hingga Maret tahun 2022. Hasil yang diperoleh dalam penelitian ini adalah model SARIMA terbaik untuk meramalkan penjualan surat kabar harian yaitu ARIMA(0,1,1)(0,1,1)7. Secara keseluruhan penjualan surat kabar harian mengalami peningkatan pada Minggu pertama bulan Januari dan kembali mengalami penurunan pada Minggu akhir bulan Maret. Nilai rata-rata peramalan penjualan surat kabar harian untuk bulan Januari hingga Maret tahun 2022 berkisaran 8.762 oplah per hari.
References
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Wei, W. W. S. (2006). Time series analysis: Univariate and multivariate methods (2nd ed). Pearson Addison Wesley.
Woodward, W. A., Gray, H. L., & Elliott, A. C. (2017). Applied time series analysis, with R (Second edition). CRC Press, Taylor & Francis Group.
Anwar, Samsul. (2017). Peramalan Suhu Udara Jangka Pendek di Kota Banda Aceh dengan Metode Autoregressive Integrated Moving Average (ARIMA). Malikussaleh Journal of Mechanical Science and Technology, Vol. 5. No. 1 (2017), 6-12.
Chatfield, C. (2004). The Analysis Of Time Series: An introduction (6th ed). Chapman & Hall/CRC.
Chatfield, C., & Xing, H. (2019). The Analysis Of Time Series: An introduction with R (Seventh edition). CRC Press, Taylor and Francis Group.
Chang, P. C., Wang Y. W., & Liu C. H. (2007). The Development of Weighted Evolving Fuzzy
Neural Network for PCB Sales Forecasting. Expert System with Application. 32, 86-89.
Hyndman, Rob J., & Anne B. Koehler. (2006). “Another Look at Measures of Forecast Accuracy.†International Journal of Forecasting, 22(4), 679-688.
Wei, W. W. S. (2006). Time series analysis: Univariate and multivariate methods (2nd ed). Pearson Addison Wesley.
Woodward, W. A., Gray, H. L., & Elliott, A. C. (2017). Applied time series analysis, with R (Second edition). CRC Press, Taylor & Francis Group.
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