Eksistensi Metode ARIMA, SARIMA dan LSTM dalam Memprediksi Penjualan

• NURUL JANNAH SALSABILA
• 14210257

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ABSTRAK

ABSTRAK

Nama             : Nurul Jannah Salsabila

NIM                : 14210257

Progam Studi : Ilmu Komputer

Fakultas         : Teknologi Informasi

Jenjang          : Strata Dua (S2)

Konsentrasi    : Data Mining

Judul Tesis  : “Eksistensi Metode ARIMA, SARIMA dan LSTM dalam Memprediksi Penjualan.”

Peramalan penjualan merupakan elemen penting dari perencanaan bisnis, dengan melakukan peramalan penjualan diawal periode akan menentukan berkembangnya bisnis tersebut. Akurasi yang tepat pada peramalan penjualan akan membantu perusahaan meminimalisir biaya yang dikeluarkan hingga meminimalisir kerugian dimasa yang akan datang yang didasari data dimasalalu atau data timeseries. Untuk merealisasikan akurasi yang maksimal, penelitian ini menerapkan tiga metode data mining yakni metode ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal Autoregressive Integrated Moving Average) dan LSTM (Long ShortTerm Memory Network). Hasil yang diperoleh pada penelitian ini didapati bahwa metode ARIMA terbukti lebih unggul dibandingkan kedua metode lainnya. Hal itu terbukti dari nilai RMSE dan R2 yang diperoleh masing-masing sebesar 0.097 dan 0.743. Meskipun hasil akurasi yang diperoleh belum maksimal, akan tetapi nilai akhir yang didapat sudah cukup baik untuk diimplementasikan. Sehingga hasil penelitian ini menyimpulkan metode LSTM cukup menjanjikan untuk melakukan proses peramalan pada data timeseries.

KATA KUNCI

Peramalan,Timeseries,ARIMA,SARIMA,LSTM

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

[1] 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.

[2] Y. Ali and S. Nakti, “Sales Forecasting: A Comparison of Traditional and Modern Times-Series Forecasting Models on Sales Data with Seasonality,” in 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), 2023, pp. 159–163.

[3] S. Pang, “Retail Sales Forecast Based on Machine Learning Methods,” in 2022 6th Annual International Conference on Data Science and Business Analytics (ICDSBA), 2022, pp. 357–361. doi: 10.1109/ICDSBA57203.2022.00030.

[4] Y. Niu, “Walmart Sales Forecasting using XGBoost algorithm and Feature engineering,” in 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), 2020, pp. 458–461. doi: 10.1109/ICBASE51474.2020.00103.

[5] V. Shankar et al., “How Technology is Changing Retail,” J. Retail., vol. 97, no. 1, pp. 13–27, 2021, doi: https://doi.org/10.1016/j.jretai.2020.10.006.

[6] T. O. Edwardo and Y. Ruldeviyani, “Sales Prediction Analysis in Determining New Minimarket Stores,” in 2020 International Conference on Information Technology Systems and Innovation (ICITSI), 2020, pp. 79–84. doi: 10.1109/ICITSI50517.2020.9264911.

[7] A. Lasek, N. Cercone, and J. Saunders, “Restaurant Sales and Customer Demand Forecasting: Literature Survey and Categorization of Methods,” in Smart City 360{\textdegree}, 2016, pp. 479–491. 50

[8] M. O. Anwer and S. Akyüz, “Sales Forecasting of a Hypermarket: Case Study in Baghdad Using Machine Learning,” in 2022 30th Signal Processing and Communications Applications Conference (SIU), 2022, pp. 1–4. doi: 10.1109/SIU55565.2022.9864955.

[9] W. Liao, G. Ye, Y. Yin, W. Yan, Y. Ma, and D. Zuo, “Auto Parts Sales Prediction based on Machine Learning for Small Data and a Long Replacement Cycle,” in 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020, pp. 1–5. doi: 10.1109/AICCSA50499.2020.9316540.

[10] M. W. Arshad and S. F. Tahir, “Sales Prediction of Cardiac Products by Time Series and Deep Learning,” Int. J. Innov. Sci. Technol., vol. 4, no. 5, pp. 1–11, 2022, doi: 10.33411/ijist/2022040501.

[11] “Corporación Favorita Grocery Sales Forecasting,” kaggle, 2018. https://www.kaggle.com/c/favorita-grocery-sales-forecasting/data

[12] E. D. Spyrou, I. Tsoulos, and C. Stylios, “Applying and Comparing LSTM and ARIMA to Predict CO Levels for a Time-Series Measurements in a Port Area,” Signals, vol. 3, no. 2, pp. 235–248, 2022, doi: 10.3390/signals3020015.

[13] L. Nashold and R. Krishnan, “Using LSTM and SARIMA Models to Forecast Cluster CPU Usage,” 2020, [Online]. Available: http://arxiv.org/abs/2007.08092

[14] M. Djerioui, Y. Brik, M. Ladjal, and B. Attallah, “Heart Disease prediction using MLP and LSTM models,” 2020 Int. Conf. Electr. Eng. ICEE 2020, 2020, doi: 10.1109/ICEE49691.2020.9249935.

[15] J. Yang et al., “Brief introduction of medical database and data mining technology in big data era,” J. Evid. Based. Med., vol. 13, no. 1, pp. 57–69, 51 2020, doi: https://doi.org/10.1111/jebm.12373.

[16] L. Chen, P. Chen, and Z. Lin, “Artificial Intelligence in Education: A Review,” IEEE Access, vol. 8, pp. 75264–75278, 2020, doi: 10.1109/ACCESS.2020.2988510.

[17] J. Guo, “Research on Artificial Intelligence: Deep Learning to Identify Plant Species,” IEEE Int. Conf. Mach. Learn. Knowl. Eng., 2022. [18] S. Ray, “A Quick Review of Machine Learning Algorithms,” 2019 Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput., pp. 35–39, 2019.

[19] Q. Bi, K. E. Goodman, J. Kaminsky, and J. Lessler, “What is machine learning? A primer for the epidemiologist,” Am. J. Epidemiol., vol. 188, no. 12, pp. 2222– 2239, 2019, doi: 10.1093/aje/kwz189.

[20] B. Ghasemkhani, O. Aktas, and D. Birant, “Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing,” Machines, vol. 11, no. 3, 2023, doi: 10.3390/machines11030322.

[21] P. P. Shinde and S. Shah, “A Review of Machine Learning and Deep Learning Applications,” Proc. - 2018 4th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2018, pp. 1–6, 2018, doi: 10.1109/ICCUBEA.2018.8697857.

[22] S. Mainali, M. E. Darsie, and K. S. Smetana, “Machine Learning in Action: Stroke Diagnosis and Outcome Prediction,” Frontiers in Neurology, vol. 12. Frontiers Media S.A., Dec. 06, 2021. doi: 10.3389/fneur.2021.734345.

[23] D. Jha et al., “Kvasir-SEG: A Segmented Polyp Dataset,” in MultiMedia Modeling, 2020, pp. 451–462.

[24] U. Hanifah, “Pengaruh Ekspor Dan Impor Terhadap Pertumbuhan Ekonomi Di Indonesia,” Transekonomika Akuntansi, Bisnis Dan Keuang., vol. 2, no. 6, pp. 52 107–126, 2022, doi: 10.55047/transekonomika.v2i6.275.

[25] M. Al-Hamadani, “Classification and analysis of the MNIST dataset using PCA and SVM algorithms,” vol. 71, pp. 221–238, 2023, doi: 10.5937/vojtehg71- 42689.

[26] I. Stan?in and A. Jovi?, “An overview and comparison of free Python libraries for data mining and big data analysis,” in 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2019, pp. 977–982. doi: 10.23919/MIPRO.2019.8757088.

[27] J. Wang, T. Kuo, L. Li, and A. Zeller, “Assessing and Restoring Reproducibility of Jupyter Notebooks,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2021, pp. 138–149. doi: 10.1145/3324884.3416585.

[28] S. Helmini, N. Jihan, M. Jayasinghe, and Perera, “Sales forecasting using multivariate long short term memory network models,” PeerJ Prepr., 2019, doi: http://dx.doi.org/10.7287/peerj.preprints.27712v1.

[29] A. Soy Temür, M. Akgün, and G. Temür, “Predicting housing sales in turkey using arima, lstm and hybrid models,” J. Bus. Econ. Manag., vol. 20, no. 5, pp. 920–938, 2019, doi: 10.3846/jbem.2019.10190.

[30] C. C. Wang, C. H. Chien, and A. J. C. Trappey, “On the application of ARIMA and LSTM to predict order demand based on short lead time and on-time delivery requirements,” Processes, vol. 9, no. 7, 2021, doi: 10.3390/pr9071157.

[31] 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, no. December, pp. 124715–124727, 2022, 53 doi: 10.1109/ACCESS.2022.3224938.

[32] J. Chen, W. Koju, S. Xu, and Z. Liu, “Sales Forecasting Using Deep Neural Network and SHAP techniques,” 2021 IEEE 2nd Int. Conf. Big Data, Artif. Intell. Internet Things Eng. ICBAIE 2021, no. Icbaie, pp. 135–138, 2021, doi: 10.1109/ICBAIE52039.2021.9389930.

[33] A. L. D. Loureiro, V. L. Miguéis, and L. F. M. da Silva, “Exploring the use of deep neural networks for sales forecasting in fashion retail,” Decis. Support Syst., vol. 114, pp. 81–93, 2018, doi: 10.1016/j.dss.2018.08.010.

[34] B. Kavya, R. Deetchika, V. Radhamani, and D. Manju, “Food Inventory Demand Forecasting Tool,” 2022.

[35] K. Radhakrishnan and T. Velmurugan, “Analyzing House Sales Prices byhyperparameters tuning Method Using Deep Learning (DL) Techniques,” NeuroQuantology, vol. 20, no. 8, pp. 1452–1460, 2022.

[36] A. Arora, V. Hassija, S. Bansal, S. Yadav, V. Chamola, and A. Hussain, “A novel multimodal online news popularity prediction model based on ensemble learning,” Expert Syst., vol. n/a, no. n/a, p. e13336, doi: https://doi.org/10.1111/exsy.13336.

[37] J. Wang, “A hybrid machine learning model for sales prediction,” in 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 2020, pp. 363–366. doi: 10.1109/ICHCI51889.2020.00083.

[38] S. Pereira, “Price Suggestion and Recommendation of Resale Products on Ecommerce Websites MSc Research Project Data Analytics”.

[39] T. Deng, Y. Zhao, S. Wang, and H. Yu, “Sales Forecasting Based on LightGBM,” 2021 IEEE Int. Conf. Consum. Electron. Comput. Eng. ICCECE 54 2021, no. Iccece, pp. 383–386, 2021, doi: 10.1109/ICCECE51280.2021.9342445.

[40] A. Massaro, A. Panarese, D. Giannone, and A. Galiano, “Augmented data and Xgboost improvement for sales forecasting in the large-scale retail sector,” Appl. Sci., vol. 11, no. 17, 2021, doi: 10.3390/app11177793.

[41] E. Alogogianni and M. Virvou, “Data Mining for Targeted Inspections Against Undeclared Work by Applying the CRISP-DM Methodology,” in 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA), 2021, pp. 1–8. doi: 10.1109/IISA52424.2021.9555543.

[42] M. Sikalubya, S. Xu, W. Yu, and P. Moonga, “Study on forecasting soybean production: An application of arima model,” Proc. - 2019 Int. Conf. Intell. Comput. Autom. Syst. ICICAS 2019, pp. 447–452, 2019, doi: 10.1109/ICICAS48597.2019.00100.

[43] H. Alabdulrazzaq, M. N. Alenezi, Y. Rawajfih, B. A. Alghannam, A. A. AlHassan, and F. S. Al-Anzi, “On the accuracy of ARIMA based prediction of COVID-19 spread,” Results Phys., vol. 27, p. 104509, 2021, doi: 10.1016/j.rinp.2021.104509.

[44] M. R. Abonazel and A. I. Abd-Elftah, “Forecasting Egyptian GDP using ARIMA models,” Reports Econ. Financ., vol. 5, no. 1, pp. 35–47, 2019, doi: 10.12988/ref.2019.81023.

[45] S. Ma, Q. Liu, and Y. Zhang, “A prediction method of fire frequency: Based on the optimization of SARIMA model,” PLoS One, vol. 16, no. 8 August, pp. 1– 13, 2021, doi: 10.1371/journal.pone.0255857.

[46] F. Jafari, K. Ponnambalam, J. Mousavi, and F. Karray, “Yield forecast of California strawberry: Time-series Models vs. ML Tools,” Conf. Proc. - IEEE 55 Int. Conf. Syst. Man Cybern., vol. 2020-Octob, pp. 3594–3598, 2020, doi: 10.1109/SMC42975.2020.9283138.

[47] J. Hu, X. Wang, Y. Zhang, D. Zhang, M. Zhang, and J. Xue, “Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network,” Neural Process. Lett., vol. 52, no. 2, pp. 1485–1500, 2020, doi: 10.1007/s11063-020-10319-3.

[48] X. Wang, L. Yang, Y. Wang, L. Ren, and M. J. Deen, “ADTT: A HighlyEfficient Distributed Tensor-Train Decomposition Method for IIoT Big Data,” IEEE Trans. Ind. Informatics, vol. PP, p. 1, 2020, doi: 10.1109/TII.2020.2967768.

[49] L. Ren, Z. Meng, X. Wang, L. Zhang, and L. Yang, “A Data-Driven Approach of Product Quality Prediction for Complex Production Systems,” IEEE Trans. Ind. Informatics, vol. PP, p. 1, 2020, doi: 10.1109/TII.2020.3001054.

[50] S. Mohan, C. Thirumalai, and G. Srivastava, “Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019, doi: 10.1109/ACCESS.2019.2923707.

[51] T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geosci. Model Dev., vol. 15, no. 14, pp. 5481–5487, 2022, doi: 10.5194/gmd-15-5481-2022.

[52] H. H. Nuha, “Mean Squared Error ( MSE ) dan Penggunaannya Ringkasan Penjelasan Referensi,” vol. 52, pp. 2021–2022, 2021.

[53] N. P. Aryani, A. D. Anggara, I. Akhlis, and K. A. Nisa, “Unnes Physics Education Journal Terakreditasi SINTA 3 Analisis Pengaruh Penggunaan Faktor Eksposi Terhadap Kualitas Citra Radiografi Phantom Air Berdasarkan Nilai Mean Square Error (MSE) N P Aryani1, A D Anggara2, Isa Akhlis2 and 56 K A Nisa1*,” UPEJ Unnes Phys. Educ. J., vol. 11, no. 1, pp. 115–119, 2021, [Online]. Available: http://journal.unnes.ac.id/sju/index.php/upej

[54] M. Calasan, S. Abdel Aleem, and A. Zobaa, “On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function,” Energy Convers. Manag., vol. 210, p. 112716, 2020, doi: 10.1016/j.enconman.2020.112716.

[55] T. A. Setiawan, N. Fadhilah, and E. Jumiati, “Penerapan Metode Linier Regresi Untuk Prediksi Nilai Aset Pemerinatah Kota Pekalongan,” IC Tech, vol. XIV, no. 2, pp. 50–53, 2019.

[56] L. B. Ferreira and F. F. da Cunha, “Multi-step ahead forecasting of daily reference evapotranspiration using deep learning,” Comput. Electron. Agric., vol. 178, no. August, p. 105728, 2020, doi: 10.1016/j.compag.2020.105728.

[57] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination Rsquared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation.,” PeerJ. Comput. Sci., vol. 7, p. e623, 2021, doi: 10.7717/peerj-cs.623.

[58] H. El-Din Hassanien, “Web scraping scientific repositories for augmented relevant literature search using crisp-dm,” Appl. Syst. Innov., vol. 2, no. 4, pp. 1–22, 2019, doi: 10.3390/asi2040037.

[59] D. S. Lindawaty, “WACANA KEBIJAKAN LOCKDOWN DALAM MENGHADAPI COVID-19 DI INDONESIA,” Info Singkat, 2020. https://sdip.dpr.go.id/search/detail/category/Info Singkat/id/1050

[60] L. A. Panzone, S. Larcom, and P. W. She, “Estimating the impact of the first COVID-19 lockdown on UK food retailers and the restaurant sector,” Glob. Food Sec., vol. 28, no. January, p. 100495, 2021, doi: 57 10.1016/j.gfs.2021.100495.