Peramalan Penjualan Produk Menggunakan Extreme Gradient Boosting (XGBoost) dan Kerangka Kerja CRISP-DM untuk Pengoptimalan Manajemen Persediaan (Studi Kasus: UB Mart)
Kata Kunci:
Bisnis ritel, Peramalan, Extreme Gradient Boosting, Cross-Industry Process for Data MiningAbstrak
Naskah ini akan diterbitkan di Konferensi Nasional SENTRIN
Referensi
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. dan Wirth, R., 2000. CRISP-DM 1.0: Step-by-step Data Mining Guide. [daring] Tersedia pada: <http://www.crisp-dm.org/CRISPWP-0800.pdf>.
Fernie, J. dan Sparks, L., 2019. Logistics and Retail Management: Emerging Issues and New Challenges in the Retail Supply Chain. 5th ed. [daring] Kogan Page. Tersedia pada: <https://books.google.co.id/books?id=vU51DwAAQBAJ>.
Guinoubi, S., Hani, Y. dan Elmhamedi, A., 2021. Demand forecast; a case study in the agri-food sector: Cold. IFAC-PapersOnLine, [daring] 54(1), hal.993–998. https://doi.org/10.1016/j.ifacol.2021.08.191.
Hajjah, A. dan Marlim, Y.N., 2021. Analisis Error Terhadap Peramalan Data Penjualan. Techno.Com, 20(1), hal.1–9. https://doi.org/10.33633/tc.v20i1.4054.
Heizer, J., Render, B. dan Munson, C., 2017. Operations Management. 12th ed. London: Pearson Education.
Heriansyah, E. dan Hasibuan, S., 2018. Implementasi Metode Peramalan pada Permintaan Bracket Side Stand K59A. Jurnal PASTI, 12(2), hal.209–223.
Husein, A.M., Lubis, F.R. dan Harahap, M.K., 2021. Analisis Prediktif untuk Keputusan Bisnis : Peramalan Penjualan. Data Sciences Indonesia (DSI), 1(1), hal.32–40. https://doi.org/10.47709/dsi.v1i1.1196.
Hyndman, R.J. dan Athanasopoulos, G., 2021. Forecasting : Principles and Practice. 3rd ed. Otexts.
Martinez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M.J. dan Flach, P., 2021. CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), hal.3048–3061. https://doi.org/10.1109/TKDE.2019.2962680.
Purnamasari, D.I., Permadi, V.A., Saepudin, A. dan Agusdin, R.P., 2023. Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses. JANAPATI: Jurnal Nasional Pendidikan Teknik Informatika, [daring] 12(1), hal.56–66. Tersedia pada: <https://ejournal.undiksha.ac.id/index.php/janapati/article/view/57144>.
Riza, F., 2022. Analisis dan Prediksi Data Penjualan Menggunakan Machine Learning dengan Pendekatan Ilmu Data. Data Sciences Indonesia (DSI), 1(2), hal.62–68. https://doi.org/10.47709/dsi.v1i2.1308.
XGBoost Developers, 2022. XGBoost Documentation. [daring] Tersedia pada: <https://xgboost.readthedocs.io/>.
Yang, T., 2023. Sales Prediction of Walmart Sales Based on OLS, Random Forest, and XGBoost Models. Highlights in Science, Engineering and Technology, 49, hal.244–249. https://doi.org/10.54097/hset.v49i.8513.
Zhang, L., Bian, W., Qu, W., Tuo, L. dan Wang, Y., 2021. Time series forecast of sales volume based on XGBoost. Journal of Physics: Conference Series, 1873(1). https://doi.org/10.1088/1742-6596/1873/1/012067.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. dan Wirth, R., 2000. CRISP-DM 1.0: Step-by-step Data Mining Guide. [daring] Tersedia pada: <http://www.crisp-dm.org/CRISPWP-0800.pdf>.
Fernie, J. dan Sparks, L., 2019. Logistics and Retail Management: Emerging Issues and New Challenges in the Retail Supply Chain. 5th ed. [daring] Kogan Page. Tersedia pada: <https://books.google.co.id/books?id=vU51DwAAQBAJ>.
Guinoubi, S., Hani, Y. dan Elmhamedi, A., 2021. Demand forecast; a case study in the agri-food sector: Cold. IFAC-PapersOnLine, [daring] 54(1), hal.993–998. https://doi.org/10.1016/j.ifacol.2021.08.191.
Hajjah, A. dan Marlim, Y.N., 2021. Analisis Error Terhadap Peramalan Data Penjualan. Techno.Com, 20(1), hal.1–9. https://doi.org/10.33633/tc.v20i1.4054.
Heizer, J., Render, B. dan Munson, C., 2017. Operations Management. 12th ed. London: Pearson Education.
Heriansyah, E. dan Hasibuan, S., 2018. Implementasi Metode Peramalan pada Permintaan Bracket Side Stand K59A. Jurnal PASTI, 12(2), hal.209–223.
Husein, A.M., Lubis, F.R. dan Harahap, M.K., 2021. Analisis Prediktif untuk Keputusan Bisnis : Peramalan Penjualan. Data Sciences Indonesia (DSI), 1(1), hal.32–40. https://doi.org/10.47709/dsi.v1i1.1196.
Hyndman, R.J. dan Athanasopoulos, G., 2021. Forecasting : Principles and Practice. 3rd ed. Otexts.
Martinez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M.J. dan Flach, P., 2021. CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8), hal.3048–3061. https://doi.org/10.1109/TKDE.2019.2962680.
Purnamasari, D.I., Permadi, V.A., Saepudin, A. dan Agusdin, R.P., 2023. Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses. JANAPATI: Jurnal Nasional Pendidikan Teknik Informatika, [daring] 12(1), hal.56–66. Tersedia pada: <https://ejournal.undiksha.ac.id/index.php/janapati/article/view/57144>.
Riza, F., 2022. Analisis dan Prediksi Data Penjualan Menggunakan Machine Learning dengan Pendekatan Ilmu Data. Data Sciences Indonesia (DSI), 1(2), hal.62–68. https://doi.org/10.47709/dsi.v1i2.1308.
XGBoost Developers, 2022. XGBoost Documentation. [daring] Tersedia pada: <https://xgboost.readthedocs.io/>.
Yang, T., 2023. Sales Prediction of Walmart Sales Based on OLS, Random Forest, and XGBoost Models. Highlights in Science, Engineering and Technology, 49, hal.244–249. https://doi.org/10.54097/hset.v49i.8513.
Zhang, L., Bian, W., Qu, W., Tuo, L. dan Wang, Y., 2021. Time series forecast of sales volume based on XGBoost. Journal of Physics: Conference Series, 1873(1). https://doi.org/10.1088/1742-6596/1873/1/012067.
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