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All Journal Sinergi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Bimasakti, Genta Garuda
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Indonesia rupiah currency detection for visually impaired people using transfer learning VGG-19 Alfatikarani, Raissa; Suciningtyas, Laras; Bimasakti, Genta Garuda; Mardhatillah, Faqisna Putra; Paragas, Jessie R.; Tjahyaningtijas, Hapsari Peni Agustin
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.022

Abstract

People with visual impairments often face difficulties in determining the authenticity of paper money, which is a crucial skill to avoid fraud. The limitations of traditional methods, like blind codes for visually impaired people, require a more advanced and efficient solution. Previous methods of currency detection using Convolutional Neural Network (CNN) techniques, including the VGG-19 architecture, have often encountered challenges, particularly the long training times required. Therefore, we propose using transfer learning techniques and modifying the top layers of the VGG-19 model, known as fully connected layers, within a mobile application with audio feedback built using Android Studio. These modifications involve substituting the three fully connected layers with dense and flattened layers. We also implemented hyperparameter tuning, including adjusting the batch sizes and setting the number of epochs. The datasets used Indonesian Rupiah paper currency from the 2022 emission year, specifically Rp 50,000 and Rp 100,000 denominations. The best transfer learning VGG-19 model achieved a batch size of 32 and an epoch of 50, resulting in a high accuracy of 88%. Response speed testing with performance profiling on Android Studio showed an overall average response time of 458 ms. The main advantage of using transfer learning with the VGG-19 model is that it significantly reduces training time while still achieving high accuracy, differentiating this work from previous studies that relied on training from scratch, which is more time-consuming and resource-intensive. Therefore, this mobile app can be categorized as having a fast response time.
LSTM-SARIMA Based Prediction Method for Environmental Quality in Enclosed Poultry House Bimasakti, Genta Garuda; Kartikasari, Anisja Noni; Tjahyaningtijas, Hapsari Peni Agustin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2557

Abstract

Closed-type poultry houses facilitate consistent output by ensuring a steady microenvironment conducive to optimal avian growth. Nevertheless, numerous farms continue to depend on manual oversight of temperature, humidity, and ammonia levels, resulting in delayed reactions, diminished productivity, and heightened environmental stress on poultry. These constraints underscore the necessity for predictive and automated systems that can monitor and forecast environmental variables in real time. Prior research indicates that LSTM networks are proficient in nonlinear time-series forecasting nonetheless, when used in isolation, LSTM models encounter difficulties in capturing linear seasonal patterns and long-term trends present in chicken house environmental data. This research presents a hybrid forecasting framework that combines LSTM and SARIMA models to concurrently represent nonlinear temporal dependencies and linear seasonal components. Environmental metrics such as temperature, soil moisture, and ammonia concentration were acquired using SHT31, Soil Moisture, and MQ137 sensors, processed using a Python-Flask backend, saved in MongoDB, and visualized through a cross-platform Flutter-based web interface. Experimental findings indicate that the proposed LSTM–SARIMA model exhibits robust predictive efficacy, with MAE = 0.62, MSE = 0.55, RMSE = 0.58, MAPE = 7.89%, and R² = 0.86. The findings demonstrate that the suggested method efficiently facilitates early warning systems and real-time microclimate evaluation, allowing for expedited environmental management measures and minimizing production losses due to unstable poultry house conditions.
Co-Authors Alfatikarani, Raissa Kartikasari, Anisja Noni Mardhatillah, Faqisna Putra Paragas, Jessie R. Suciningtyas, Laras Tjahyaningtijas, Hapsari Peni Agustin