Extreme Learning Machine Method Application to Forecasting Coffee Beverage Sales
DOI:
https://doi.org/10.30871/jaic.v9i5.10465Keywords:
Coffee Baverage, Extreme Learning Machine, Forecasting, SalesAbstract
Sales estimates can be used to set product prices and increase expected profits. Flyover coffee shop Karanganyar does not have a methodical forecasting method to estimate and predict their need/demand for coffee beverage products. Two previous research that used Extreme Learning Machine (ELM) method in other predictions stated that ELM method has high accuracy and fast compilation time. Another research predicted jeans sales using the ARIMA model and produced an accuracy of 17.05% based on the MAPE (Mean Absolute Percentage Error) method. Menstrual cycle prediction using the Long Short-Term Memory (LSTM) method produces a MAPE value of 7.5%. Two advantages of ELM method from two previous research were used as the basis for selecting ELM method used in our study. To help predict sales of coffee beverage menus, this research utilized an artificial neural network method using ELM algorithm. ELM method consists of an input layer and an output layer connected through a hidden layer. Data used for the test was daily sales data for a month. Data used for this study consisted of 215 data samples. Daily sales data at the Flyover coffee shop were collected from June to December 2024. Based on the results and analysis of error values using MAPE method, an average error value was 8.274%. From comparison of original data results and prediction data, an average MAPE error value the best number of features and hidden neurons is 5.65%.
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[1] A. S. R. M. Sinaga, R. E. Putra, and A. S. Girsang, “Prediction Measuring Local Coffee Production And Marketing Relationships Coffee With Big Data Analysis Support,” Bull. Electr. Eng. Informatics, vol. 11, no. 5, pp. 2764–2772, 2022, doi: 10.11591/eei.v11i5.4082.
[2] F. Orduna-Cabrera et al., “Short-Term Forecasting Arabica Coffee Cherry Yields by Seq2Seq over LSTM for Smallholder Farmers,” Sustain., vol. 17, no. 9, pp. 1–14, 2025, doi: 10.3390/su17093888.
[3] L. Setiyani and W. H. Utomo, “Arabica Coffee Price Prediction Using the Long Short Term Memory Network (LSTM) Algorithm,” Sci. J. Informatics, vol. 10, no. 3, pp. 287–296, 2023, doi: 10.15294/sji.v10i3.44162.
[4] J. Zhao, B. Zong, and L. Wu, “Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques,” ISPRS Int. J. Geo-Information, vol. 12, no. 8, pp. 1–24, 2023, doi: 10.3390/ijgi12080329.
[5] S. Meeprom and A. Kokkhangplu, “Customer experience and satisfaction in coffee consumption: an experiential marketing perspective,” Cogent Bus. Manag., vol. 12, no. 1, p., 2025, doi: 10.1080/23311975.2025.2450296.
[6] W. Wahyuningsih and P. T. Prasetyaningrum, “Enhancing Sales Determination for Coffee Shop Packages through Associated Data Mining: Leveraging the FP-Growth Algorithm,” J. Inf. Syst. Informatics, vol. 5, no. 2, pp. 758–770, 2023, doi: 10.51519/journalisi.v5i2.500.
[7] Y. L. Chen and Y. C. Chen, “Impact of Brand and Testing Method on Coffee Taste Perception between Genders: A Comparative Study of Two Leading Coffee Brands in Taiwan,” SAGE Open, vol. 15, no. 1, pp. 1–12, 2025, doi: 10.1177/21582440251316087.
[8] R. S. Hendiarto, R. F. Laksana, and F. Alhafizh, “Influence of Store Atmosphere and Price on Costumer Loyality (Study at Diantara Kopi Coffee Shop in Bandung),” Owner, vol. 8, no. 3, pp. 2834–2842, 2024, doi: 10.33395/owner.v8i3.2088.
[9] A. W. Gunawan, A. W. Muhaimin, and R. I. Sitawati, “High Quality Product, Good Services, and Competitive Pricing of Local Coffee Shop to Increase Consumer Satisfaction and Loyalty,” J. Tek. Pertan. Lampung (Journal Agric. Eng., vol. 13, no. 2, p. 592, 2024, doi: 10.23960/jtep-l.v13i2.592-605.
[10] S. M. M. MangusStephanie M, “Personal selling and sales management abstracts,” J. Pers. Sell. Sales Manag., vol. 44, no. 4, pp. 343–354, 2024, doi: 10.1080/08853134.2024.2415453.
[11] M. A. Majid, P. D. Saputri, and S. Soehardjoepri, “Stock Market Index Prediction using Bi-directional Long Short-Term Memory,” J. Appl. Informatics Comput., vol. 8, no. 1, pp. 55–61, 2024, doi: 10.30871/jaic.v8i1.7195.
[12] Y. Chen, “Research on Machine Learning-based Prediction of Coffee Futures Prices,” Highlights Sci. Eng. Technol., vol. 92, pp. 199–209, 2024, doi: 10.54097/8c9t9n30.
[13] M. Miranda and S. Sriani, “Implementation of K-Means Clustering in Grouping Sales Data at Zura Mart,” J. Appl. Informatics Comput., vol. 9, no. 2, pp. 547–555, 2025, doi: 10.30871/jaic.v9i2.9160.
[14] R. Shams, S. Chatterjee, and R. Chaudhuri, “Developing brand identity and sales strategy in the digital era: Moderating role of consumer belief in brand,” J. Bus. Res., vol. 179, no. April, p. 114689, 2024, doi: 10.1016/j.jbusres.2024.114689.
[15] V. R. Danestiara, M. Marwondo, and N. N. Azkiya, “Prediction of Inhibitor Binding Affinity and Molecular Interactions in Mpro Dengue Using Machine Learning,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 10, no. 3, pp. 461–468, 2025, doi: 10.33480/jitk.v10i3.5994.
[16] S. Febriani, V. Wati, Y. Wijayanti, and I. Siswanto, “Twitter Sentiment Analysis on Digital Payment in Indonesia Using Artificial Neural Network,” J. Appl. Informatics Comput., vol. 9, no. 2, pp. 526–533, 2025, doi: 10.30871/jaic.v9i2.8988.
[17] F. Mercaldo, L. Brunese, F. Martinelli, A. Santone, and M. Cesarelli, “Experimenting with Extreme Learning Machine for Biomedical Image Classification,” Appl. Sci., vol. 13, no. 14, pp. 1–20, 2023, doi: 10.3390/app13148558.
[18] M. Vinoth Kumar, G. Kiran Kumar, A. M. Ali, S. K. Rajesh Kanna, and K. Muthu Lakshmi, “Analysis of Extreme Learning Machine Based on Multiple Hidden Layers,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 95, pp. 96–103, 2024, [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/4208
[19] A. P. Ariyanti, M. I. Mazdadi, A.- Farmadi, M. Muliadi, and R. Herteno, “Application of Extreme Learning Machine Method With Particle Swarm Optimization to Classify of Heart Disease,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 17, no. 3, p. 281, 2023, doi: 10.22146/ijccs.86291.
[20] R. Fredyan, M. R. N. Majiid, and G. P. Kusuma, “Spatiotemporal Analysis for Rainfall Prediction Using Extreme Learning Machine Cluster,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 6, pp. 2240–2248, 2023, doi: 10.18517/ijaseit.13.6.18214.
[21] C. D. Nariyana, M. Idhom, and Trimono, “Prediction of Purchase Volume Coffee Shops in Surabaya Using Catboost with Leave-One-Out Cross Validation,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 11, no. 1, pp. 124–138, 2025, doi: 10.26555/jiteki.v11i1.30610.
[22] W. K. Setiadi, V. R. Prasetyo, and F. D. Kartikasari, “Comparison of Extreme Learning Machine Methods and Support Vector Regression for Predicting Bank Share Prices in Indonesia,” Teknika, vol. 13, no. 2, pp. 219–225, 2024, doi: 10.34148/teknika.v13i2.856.
[23] D. Das and S. Chakrabarti, “An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India,” Pertanika J. Sci. Technol., vol. 31, no. 6, pp. 3179–3198, 2023, doi: 10.47836/pjst.31.6.30.
[24] J. M. A. C. Permata and M. Habibi, “Autoregressive Integrated Moving Average (ARIMA) Models For Forecasting Sales Of Jeans Products,” J. Inform. dan Teknol. Inf., vol. 20, no. 1, pp. 31–40, 2023, doi: 10.31515/telematika.v20i1.7868.
[25] M. Khairunisa, D. M. S. A. Putri, and I. G. N. L. Wijayakusuma, “Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction,” J. Appl. Informatics Comput., vol. 9, no. 2, pp. 348–353, 2025, doi: 10.30871/jaic.v9i2.9076.
[26] S. Aisyah, N. Ulinnuha, and A. Hamid, “Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah,” KUBIK J. Publ. Ilm. Mat., vol. 7, no. 2, pp. 97–105, 2023, doi: 10.15575/kubik.v7i2.20460.
[27] S. Sulandri, A. Basuki, and F. A. Bachtiar, “Metode Deteksi Intrusi Menggunakan Algoritme Extreme Learning Machine dengan Correlation-based Feature Selection,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 103, 2021, doi: 10.25126/jtiik.0813358.
[28] C. Wiedyaningsih, E. Yuniarti, and A. Fadilla, “Forecasting Cardiovascular Drug Demand Using Triple Exponential Smoothing Additive,” Indones. J. Glob. Heal. Res., vol. 7, no. 1, pp. 29–34, 2025, doi: 10.37287/ijghr.v2i4.250.
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