PREDIKSI KLASTER KONSUMEN BERDASARKAN PERILAKU BELANJA MUSIMAN MENGGUNAKAN METODE SARIMA
Keywords:
Artificial Intelligence, SARIMA, Perilaku Belanja, pola musiman, klaster konsumenAbstract
Perilaku belanja konsumen cenderung mengalami fluktuasi yang dipengaruhi oleh faktor musiman seperti periode waktu tertentu, kebiasaan pembelian, dan tren konsumsi. Penelitian ini bertujuan untuk memprediksi pola belanja konsumen musiman sebagai dasar pembentukan klaster konsumen menggunakan pendekatan Artificial Intelligence. Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) digunakan untuk memodelkan data deret waktu penjualan yang bersumber dari dataset shopping_trends.csv. Tahapan penelitian meliputi pemahaman data, pra-pemrosesan data, uji stasioneritas menggunakan Augmented Dickey-Fuller (ADF), pemodelan SARIMA, serta evaluasi model menggunakan Root Mean Squared Error (RMSE) dan Mean Absolute Error (MAE). Hasil penelitian menunjukkan bahwa model SARIMA dengan parameter (0,0,0)(2,1,0,7) mampu menangkap pola musiman mingguan dengan nilai RMSE sebesar 28,34 dan MAE sebesar 23,20. Hasil prediksi ini dapat dimanfaatkan sebagai dasar pengelompokan (klasterisasi) konsumen berdasarkan pola dan intensitas belanja musiman.
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References
A data-driven clustering method for time course gene expression data. nucleic acids research, 34(4), 1261-1269. https://doi.org/10.1093/nar/gkl013
Cong, J., Ren, M., Xie, S., & Wang, P. (2019). Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018. International Journal of Environmental Research and Public Health, 16(23), 4760. https://doi.org/10.3390/ijerph16234760
Development of a scale to measure the perceived benefits and risks of online shopping. journal of interactivemarketing20(2),5575.
https://doi.org/10.1002/dir.20061
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24(5), 1709-1734. https://doi.org/10.1007/s10796-02110186-w
Fildes, R., & Ma, S. (2015). Peramalan dan riset operasional: Sebuah tinjauan.
Jurnal Masyarakat Riset Operasional , 66(12), 2042– 2053.https://doi.org/10.1057/jors.2015.28
Going shopping: key determinants of shopping behaviors and motivations. international journal of retail & distribution management, 27(4), 154-
165. https://doi.org/10.1108/09590559910268499
Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. bmc medical research methodology, 21(1). https://doi.org/10.1186/s12874- 02101235-8
Kumar, S. V. and Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review,
7(3). https://doi.org/10.1007/s12544-015-0170-8
Madrid, E. A. and António, N. (2021). Short-Term Electricity Load Forecasting with Machine Learning. Information, 12(2), 50.
https://doi.org/10.3390/info12020050
Measuring festival quality and value affecting visitors’ satisfaction and loyalty using a structural approach. international journal of hospitality management, 29(2), 335-342.
https://doi.org/10.1016/j.ijhm.2009.10.002
Multichannel customer management: Understanding the research-shopper phenomenon. international journal of research in marketing, 24(2), 129-
148. https://doi.org/10.1016/j.ijresmar.2006.11.002
Predicting Potential Evapotranspiration for Kalaburagi District using a Seasonal Arima Model. international journal of environment and climate change, 13(11), 2073- 2082. https://doi.org/10.9734/ijecc/2023/v13i113367
Purchasing, Pricing, and Quick Response in the Presence of Strategic
Consumers. management science, 55(3), 497-511.
https://doi.org/10.1287/mnsc.1080.0948
Rotella, P., Salomon, F. L. R., & Pamplona, E. d. O. (2014). ARIMA: An Applied Time Series Forecasting Model for the Bovespa Stock Index. Applied
Mathematics, 05(21), 3383-3391. https://doi.org/10.4236/am.2014.521315 Scatter in fatigue life due to effects of porosity in cast A356-T6 aluminum-silicon alloys.
metallurgical and materials transactions a, 34(9), 1879-1890. https://doi.org/10.1007/s11661-003-0153-6
Tang, C., Wang, L., Wei, Y., Wu, P., & Wei, H. (2023). Time-Frequency Domain Variation Analysis and LSTM Forecasting of Regional Visibility in the China Region
Based on GSOD Station Data. Atmosphere, 14(7), 1072. https://doi.org/10.3390/atmos14071072
The impact of website quality on customer satisfaction and purchase intentions: Evidence from Chinese online visitors. international journal of hospitality management, 27(3), 391-402.
https://doi.org/10.1016/j.ijhm.2007.10.008
Tsai, CF, & Chiu, CC (2004). Metodologi segmentasi pasar berbasis pembelian. Expert Systems with Applications , 27(2), 265–276. https://doi.org/10.1016/j.eswa.2004.03.006
Wang, K., Deng, C., Li, J. P., Zhang, Y. Y., Li, X., & Wu, M. (2017). Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiology and Infection, 145(6), 1118- 1129. https://doi.org/10.1017/s0950268816003216
Wang, H., Tian, C., Wang, W. M., & Luo, X. (2018). Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiology and Infection,
146(8), 935-939. https://doi.org/10.1017/s095026881800111
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Copyright (c) 2026 Elisabeth Monalisa Sawe, I Wayan Sudiarsa, Maria Yulita Runu Ke’e, Anastasia Deveni Putri, Maria Dewi Sartika
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