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All Journal PROSIDING SEMINAR NASIONAL Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer INFORMATIKA JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Infortech Jurnal Pengabdian kepada Masyarakat Nusantara Abditeknika - Jurnal Pengabdian Kepada Masyarakat Wasana Nyata Jurnal Abdimas Ekonomi dan Bisnis Journal Basic Science and Technology Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Media Computer Science Paradigma Indonesian Journal Computer Science (ijcs) Dikdimas : Jurnal Pengabdian Kepada Masyarakat WASANA NYATA Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan Prawara Jurnal Pengabdian Masyarakat
Heribertus Ary Setyadi
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health 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%.
Benchmarking Deepseek-LLM-7B-Chat and Qwen1.5-7B-Chat for Indonesian Product Review Emotion Classification Nurohim, Galih Setiawan; Setyadi, Heribertus Ary; Fauzi, Ahmad
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11369

Abstract

Upon completing their shopping experience on an e-commerce platform, users have the opportunity to leave a review. By analyzing reviews, businesses can gain insight into customer emotions, while researchers and policymakers can monitor social dynamics. Large Language Models (LLMs) utilization is identified as a promising methodology for emotion analysis. LLMs have revolutionized natural language processing capabilities, yet their performance in non-English languages, such as Indonesian, necessitates a comprehensive evaluation. This research objective is to perform a comprehensive analysis and comparison of Deepseek-LLM-7B-Chat and Qwen1.5-7B-Chat, two prominent open-source Large Language Models, for the emotion classification of Indonesian product reviews. By leveraging the PRDECT-ID dataset, this study evaluates the performance of both models in a few-shot learning scenario through prompt engineering. The methodology outlines the data preprocessing pipeline, a detailed few-shot prompt engineering strategy tailored to each model's characteristics, model inference execution, and performance assessment using the accuracy, precision, recall, and F1-score metrics. Analytical results reveal DeepSeek achieved an accuracy of 43.41%, exhibiting a considerably superior ability to comprehend instructions compared to Qwen, which attained a maximum accuracy of only 20.35% and often yielded near-random predictions. An in-depth error analysis indicates that this performance gap is likely attributable to factors such as pre-training data bias and tokenization mismatches with the Indonesian language. This research offers empirical evidence regarding the comparative strengths and weaknesses of DeepSeek and Qwen, providing a diagnostic benchmark that underscores the significance of instruction tuning and robust multilingual representation for Indonesian NLP tasks.
Co-Authors - STIE AUB Surakarta, Yusuf Sutanto Agung Nugroho Agus Kristianto Agus Kristianto Ahmad Fauzi Akhmad Syukron Al Amin, Budi Amin, Budi Al Annida Purnamawati Bambang Eka Purnama Bambang Eka Purnama Budi Al Amin Cahyo Nugroho, Ernes Candra Agustina Dahlan Susilo Doddy Satrya Perbawa Ernes Cahyo Nugroho - STMIK AUB Surakarta Fauzi Ahmad Muda Galih Setiawan - UBSI Galih Setiawan Nurohim Galih Setyawan Nurohim Haryanto, Wawan Nugroho, Wawan Nurohim, Galih S Pudji Widodo Pudji Widodo Rahmawati-Universitas Bina Sarana Informatika, Eka Rousyati, Rousyati Sardiarinto Sardiarinto Satrya Perbawa, Doddy Sri Rejeki Sundari . Sundari Sundari Supriyanta Supriyanta Sutanto Sutanto Sutanto Sutanto Wahyuningsih, Hartati Dyah Wawan Haryanto Wawan Nugroho Yulianto Yusuf Sutanto Yusuf Sutanto