KOMPARASI SARIMA DAN LSTM DALAM PREDIKSI PERMINTAAN DAN LABA KOTOR PENJUALAN STATIONERY

• ADE RUMINTARSIH
• 14220007

---

ABSTRAK

Di era globalisasi dan transformasi digital yang dinamis, perusahaan-perusahaan menghadapi tantangan untuk memprediksi dengan akurat permintaan pasar guna mempertahankan keunggulan kompetitif. Penelitian ini bertujuan mengevaluasi efektivitas model Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Long Short-Term Memory (LSTM) dalam meramalkan permintaan dan laba kotor. Hasil penelitian menunjukan LSTM terbukti lebih unggul dengan nilai Root Mean Squared Error (RMSE) yang rendah, yaitu 0,0013 untuk fitur "Demand" dan "Gross Profit" 0,0083 dibandingkan dengan SARIMA. Selain itu, LSTM juga mencatat Mean Absolute Error (MAE) sebesar 0,0023 dan Mean Squared Error (MSE) sebesar 0,0002 untuk fitur "Demand", serta MAE sebesar 0,0026 dan MSE sebesar 6,8633 untuk fitur "Gross Profit". Keunggulan LSTM terletak pada kemampuannya dalam menangkap pola temporal yang kompleks dan menyesuaikan diri dengan perubahan pasar yang cepat. Temuan ini memberikan wawasan tentang pentingnya adopsi teknologi prediksi yang canggih untuk mendukung keputusan strategis dalam mengelola produksi, stok, dan pemasaran secara efisien di lingkungan bisnis yang dinamis dan kompetitif.

KATA KUNCI

LSTM,SARIMA,Prediksi,MSE

---

DAFTAR PUSTAKA

[1] H. Upadhyay, S. Shekhar, A. Vidyarthi, R. Prakash, and R. Gowri, “Sales Prediction in the Retail Industry Using Machine Learning: A Case Study of BigMart,” in 2023 International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM), 2023, pp. 1–6. doi: 10.1109/ELEXCOM58812.2023.10370313.

[2] G. Chaubey, P. R. Gavhane, D. Bisen, and S. K. Arjaria, “Customer purchasing behavior prediction using machine learning classification techniques,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 12, pp. 16133–16157, 2023, doi: 10.1007/s12652-022-03837-6.

[3] T. Toe Teoh, “AI in Sales,” in Artificial Intelligence in Business Management, Singapore: Springer Nature Singapore, 2023, pp. 175–208. doi: 10.1007/978-981-99-4558-0_10.

[4] A. A. Yasir, E. Wihardjo, I. L. Mursyidah, and E. S. WJ, “APPLICATION OF DEEP LEARNING TO IMPROVE SALES PREDICTION ACCURACY IN THE E-COMMERCE INDUSTRY”. S.-Y. Pan, Q. Liao, and Y.-T. Liang, “Multivariable sales prediction for filling stations via GA improved BiLSTM,” Pet. Sci., vol. 19, no. 5, pp. 2483–2496, 2022, doi: https://doi.org/10.1016/j.petsci.2022.05.005.

[5] B. Zhang, M.-L. Tseng, L. Qi, Y. Guo, and C.-H. Wang, “A comparative online sales forecasting analysis: Data mining techniques,” Comput. Ind. Eng., vol. 176, p. 108935, 2023, doi: https://doi.org/10.1016/j.cie.2022.108935.

[6] D. Li and J. Xin, “Deep learning-driven intelligent pricing model in retail: from sales forecasting to dynamic price optimization,” Soft Comput., 2024, doi: 10.1007/s00500-024-09937-z.

[7] H. K. Hameed, “Deep learning Algorithm for predicting various retail store sales Using LSTM and ARIMA,” Dijlah J., vol. 6, no. 4, 2023. U. M. Sirisha, M. C. Belavagi, and G. Attigeri, “Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison,” IEEE Access, vol. 10, pp. 124715 124727, 2022, doi: 10.1109/ACCESS.2022.3224938.

[10] E. Dave, A. Leonardo, M. Jeanice, and N. Hanafiah, “Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM,” Procedia Comput. Sci., vol. 179, pp. 480–487, 2021, doi: https://doi.org/10.1016/j.procs.2021.01.031.

[11] K. Chen, “An Online Retail Prediction Model Based on AGA-LSTM Neural Network,” in 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 2020, pp. 145–149. doi: 10.1109/MLBDBI51377.2020.00032.

[12] T. Luo, D. Chang, and Z. Xu, “Research on Apparel Retail Sales Forecasting Based on xDeepFM-LSTM Combined Forecasting Model,” Information, vol. 13, no. 10, 2022, doi: 10.3390/info13100497.

[13] B. Lakshmanan, P. S. N. Vivek Raja, and V. Kalathiappan, “Sales Demand Forecasting Using LSTM Network BT - Artificial Intelligence and Evolutionary Computations in Engineering Systems,” S. S. Dash, C. Lakshmi, S. Das, and B. K. Panigrahi, Eds., Singapore: Springer Singapore, 2020, pp. 125–132.

[14] K. Kumar and D. P. Gandhmal, “An intelligent indian stock market forecasting system using LSTM deep learning,” Indones. J. Electr. Eng. Comput. Sci., vol. 21, no. 2, p. 1082, 2021.

[15] D. Riana et al., “RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images,” in Neural Information Processing, T. Mantoro, M. Lee, M. A. Ayu, K. W. Wong, and A. N. Hidayanto, Eds., Cham: Springer International Publishing, 2021, pp. 317–325.

[16] H. Hafizhahullah et al., “A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data,” in Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications, in IC3INA ’22. New York, NY, USA: Association for Computing Machinery, 2023, pp. 106–110. doi: 10.1145/3575882.3575903.

[17] A. R. Yuliani et al., “Remaining Useful Life Prediction of Lithium-Ion Battery Based on LSTM and GRU,” in Proceedings of the 2021 International Conference on Computer, Control, Informatics and Its Applications, in IC3INA ’21. New York, NY, USA: Association for Computing Machinery, 2022, pp. 21–25. doi: 10.1145/3489088.3489092.

[18] M. Aljbour and \.Isa Avc\i, “Sales Prediction in E-Commerce Platforms Using Machine Learning,” in International Conference on Forthcoming Networks and Sustainability in the AIoT Era, 2024, pp. 207–216.

[19] D. Munandar, B. N. Ruchjana, A. S. Abdullah, and H. F. Pardede, “Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting,” Mathematics, vol. 11, no. 13, 2023, doi: 10.3390/math11132975.

[20] D. Indrawan, S. Cakrawijaya, B. Wicaksono, E. Erni, and W. Gata, “PREDIKSI JUMLAH PENONTON VIDEO YOUTUBE MENGGUNAKAN MODEL DEEP NEURAL NETWORK (DNN),” J. Inf. Syst. Informatics Comput., vol. 5, no. 1, pp. 94–98, 2021, doi: 10.52362/jisicom.v5i1.463.

[21] V. F. Hakim and D. Riana, “Analysis of User Complaints for Telecommunication Brands on X (Twitter) using IndoBERT and Deep Learning,” J. Nas. Pendidik. Tek. Inform. JANAPATI, vol. 13, no. 2, pp. 270–279, 2024, doi: 10.23887/janapati.v13i2.76497.

[22] A. Zevana and D. Riana, “TEXT CLASSIFICATION USING INDOBERT FINE-TUNING MODELING WITH CONVOLUTIONAL NEURAL NETWORK AND BI-LSTM,” J. Tek. Inform., vol. 4, no. 6, pp. 1605–1610, 2024, doi: 10.52436/1.jutif.2023.4.6.1650.

[23] K. N. S. Babu and M. M. Kodabagi, “An Efficient Framework for Predicting Future Retail Sales Using Ensemble DNN-BiLSTM Technique,” SN Comput. Sci., vol. 5, no. 1, p. 150, 2024, doi: 10.1007/s42979-023-02427-3.

[24] S. Kulshreshtha and others, “An ARIMA-LSTM Hybrid Model for Stock Market Prediction Using Live Data.,” J. Eng. Sci. \& Technol. Rev., vol. 13, no. 4, 2020.

[25] H. Bousqaoui, I. Slimani, and S. Achchab, “Comparative analysis of short-term demand predicting models using ARIMA and deep learning,” Int. J. Electr. Comput. Eng., vol. 11, pp. 3319–3328, 2021, [Online]. Available: https://api.semanticscholar.org/CorpusID:235079977

[26] U. G. Ç\.IÇEKL\.I and M. YURTSEVER, “A NOVEL HYBRID MODEL USING ARIMA AND LSTM FOR SALES FORECASTING”.

[27] M. S. Ahnaf, A. Kurniawati, and H. D. Anggana, “Forecasting Pet Food Item Stock using ARIMA and LSTM,” in 2021 4th International Conference of Computer and Informatics Engineering (IC2IE), 2021, pp. 141–146. doi: 10.1109/IC2IE53219.2021.9649271.

[28] A. Kolková and M. Navrátil, “Demand forecasting in Python: deep learning model based on LSTM architecture versus statistical models,” Acta Polytech. Hungarica, vol. 18, no. 8, pp. 123 141, 2021.

[29] P. Saha, N. Gudheniya, R. Mitra, D. Das, S. Narayana, and M. K. Tiwari, “Demand Forecasting of a Multinational Retail Company using Deep Learning Frameworks,” IFAC-PapersOnLine, vol. 55, no. 10, pp. 395–399, 2022, doi: https://doi.org/10.1016/j.ifacol.2022.09.425.

[30] A. Schmidt, M. W. U. Kabir, and M. T. Hoque, “Machine Learning Based Restaurant Sales Forecasting,” Mach. Learn. Knowl. Extr., vol. 4, no. 1, pp. 105–130, 2022, doi: 10.3390/make4010006.

[31] Y. Ensafi, S. H. Amin, G. Zhang, and B. Shah, “Time-series forecasting of seasonal items sales using machine learning – A comparative analysis,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 1, p. 100058, 2022, doi: https://doi.org/10.1016/j.jjimei.2022.100058.

[32] J.-K. Hong, “LSTM-based Sales Forecasting Model.,” KSII Trans. Internet \& Inf. Syst., vol. 15, no. 4, 2021.

[33] P. Saxena, P. Bahad, and R. Kamal, “Long short-term memory-RNN based model for multivariate car sales forecasting,” Int. J. Adv. Sci. Technol, vol. 29, pp. 4645–4656, 2020.

[34] D. Fan, H. Sun, J. Yao, K. Zhang, X. Yan, and Z. Sun, “sWell production forecasting based on ARIMA-LSTM model considering manual operation,” Energy, vol. 220, p. 119708, 2021, doi: https://doi.org/10.1016/j.energy.2020.119708.

[35] X. Li, J. Du, Y. Wang, and Y. Cao, “Automatic Sales Forecasting System Based On LSTM Network,” in 2020 International Conference on Computer Science and Management Technology (ICCSMT), 2020, pp. 393–396. doi: 10.1109/ICCSMT51754.2020.00088.

[36] L. Grogan, V. Reus, B. O’Carroll, and S. Fallon, “Forecasting Necessary Levels Of Bread Stock For ‘Direct-To-Shelf’ Delivery Using CNN And LSTM Artificial Neural Networks,” in 2023 International Conference on Electrical and Information Technology (IEIT), 2023, pp. 318–323. doi: 10.1109/IEIT59852.2023.10335581.

[37] D. Swami, A. Shah, and S. K. B. Ray, “Predicting Future Sales of Retail Products using Machine Learning,” ArXiv, vol. abs/2008.0, https://api.semanticscholar.org/CorpusID:221150393 2020, [Online]. Available:

[38] K. N. Vavliakis, A. Siailis, and A. L. Symeonidis, “Optimizing Sales Forecasting in e Commerce with ARIMA and LSTM Models.,” in WEBIST, 2021, pp. 299–306.