Analisis Perbandingan ARIMA dan Long-Short Term Memory dalam Prediksi Penjualan (Studi Kasus: PT XYZ)
Kata Kunci:
prediksi, arima, lstmAbstrak
Jurnal ini akan dipublikasikan pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Referensi
Arhami, M. & Nasir, M. (2020). Data Mining: Algoritma dan Implementasi. Yogyakarta: Andi.
Brockwell, P.J., & Davis, R.A. (2016). Introduction to Time Series and Forecasting. Springer International Publishing Switzerland.
Eunike, A., Setyanto, N. W., Yuniarti, R., Hamdala, I., Lukodono, R. P., & Fanani, A. A. (2021). Perencanaan Produksi dan Pengendalian Persediaan: Edisi Revisi. Universitas Brawijaya Press.
Hanke, J. E. & Wichers, D. W. (2005). Business Forecasting Eighth Edition. New Jersey: Pearson Prentice hall.
Heizer, Jay dan Render, Barry. (2015). Manajemen Operasi. Jakarta: Salemba Empat.
Li, D., & Liu, S. (2018). Water Quality Monitoring and Management: Basis, Technology and Case Studies. Academic Press.
Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173.
Rasyidi, M. A. (2017). Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan ARIMA. Journal of Information Systems Engineering and Business Intelligence, 3(2), 107.
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A Comparison of Arima and LSTM in Forecasting Time Series. 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE.
Stellwagen, E., & Tashman, L. (2013). ARIMA: The models of Box and Jenkins. Foresight: The International Journal of Applied Forecasting, (30), 28-33.
Tang, Z., De Almeida, C., & Fishwick, P. A. (1991). Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation, 57(5), 303-310.
Wong, K. D. F. (1997). The Relevance Of Business Cycles In Forecasting International Tourist Arrivals. Tourism Management, 18(8), 581-586.
Arhami, M. & Nasir, M. (2020). Data Mining: Algoritma dan Implementasi. Yogyakarta: Andi.
Brockwell, P.J., & Davis, R.A. (2016). Introduction to Time Series and Forecasting. Springer International Publishing Switzerland.
Eunike, A., Setyanto, N. W., Yuniarti, R., Hamdala, I., Lukodono, R. P., & Fanani, A. A. (2021). Perencanaan Produksi dan Pengendalian Persediaan: Edisi Revisi. Universitas Brawijaya Press.
Hanke, J. E. & Wichers, D. W. (2005). Business Forecasting Eighth Edition. New Jersey: Pearson Prentice hall.
Heizer, Jay dan Render, Barry. (2015). Manajemen Operasi. Jakarta: Salemba Empat.
Li, D., & Liu, S. (2018). Water Quality Monitoring and Management: Basis, Technology and Case Studies. Academic Press.
Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173.
Rasyidi, M. A. (2017). Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan ARIMA. Journal of Information Systems Engineering and Business Intelligence, 3(2), 107.
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A Comparison of Arima and LSTM in Forecasting Time Series. 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE.
Stellwagen, E., & Tashman, L. (2013). ARIMA: The models of Box and Jenkins. Foresight: The International Journal of Applied Forecasting, (30), 28-33.
Tang, Z., De Almeida, C., & Fishwick, P. A. (1991). Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation, 57(5), 303-310.
Wong, K. D. F. (1997). The Relevance Of Business Cycles In Forecasting International Tourist Arrivals. Tourism Management, 18(8), 581-586.
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