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All Journal JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Adi Widya : Jurnal Pengabdian Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) International Journal of Business, Law, and Education Wasana Nyata WASANA NYATA
Yusuf Sutanto
STIE-AUB Surakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Veterinary Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Extreme Learning Machine Method Application to Forecasting Coffee Beverage Sales Sutanto, Yusuf; Setyadi, Heribertus Ary; Nugroho, Wawan; Al Amin, Budi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10465

Abstract

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%.
Co-Authors Al Amin, Budi Aris Tri Haryanto Bambang Eka Purnama Budi Al Amin Endang Saryanti Galih Setiawan Nurohim Haryanti Haryanti Heribertus Ary Setyadi Nani Irma Susanti Nugroho, Anggoro Panji Nugroho, Wawan Pudji Widodo Rapiyanta , Paulus Tofan Rina Ani Sapariyah Siti Fatonah Sumaryanto Sumaryanto, Sumaryanto Supriyanta Supriyanta Susanti, Nani Susanti, Nani Irma Wahyuningsih, Hartati Dyah wardhana, galih wisnu