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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Media Elektrik
Irvawansyah
Politeknik Bosowa
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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ENVIRONMENTAL CONDITION MEASUREMENT SYSTEM WITH A MINI WEATHER STATION USING ESP32 Umar Muhammad; Irvawansyah; Dirham Muhammad
Jurnal Media Elektrik Vol. 21 No. 1 (2023): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v21i1.657

Abstract

Weather conditions are important to know because they have an impact on various human activities, such as agriculture, infrastructure, hydrology, and more. Several factors affect the weather, including wind speed, temperature, and air humidity. However, in this study, a mini weather station was designed to provide information not only on wind speed, temperature, and humidity but also on solar radiation. The research employed several sensors, including the DHT11, anemometer, and pyranometer. The research results indicate that the DHT11 error indoors is 1.4%, but it increases to 9% outdoors. The pyranometer shows a 3.62% error from direct solar radiation measurements. However, the wind speed sensor is still unstable, suggesting the need for improvement in future research.
Modeling and Simulation of a Heat and Airflow Control System in a Fish Smoking Chamber Using K-NN Muhammad Edy Hidayat; Alang Sunding; Umar Muhammad; Irvawansyah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2492

Abstract

This study presents the modeling and simulation of a heat and airflow control system in a fish smoking chamber using the K-Nearest Neighbors (K-NN) algorithm. Accurate control of temperature and airflow is crucial for ensuring consistent product quality, flavor, texture, and microbial safety in smoked fish. Traditional methods often face challenges in maintaining stable chamber conditions due to nonlinear interactions between heat sources, airflow distribution, and chamber geometry. The research was conducted using a structured methodology consisting of system modeling, K-NN algorithm development, simulation, and performance evaluation. The results demonstrate that the K-NN model achieved optimal performance at k = 5, with an overall prediction accuracy of 92.8%. The Root Mean Square Error (RMSE) was recorded at 1.85 °C for temperature prediction and 0.18 m/s for airflow, confirming the model’s robustness. Compared with conventional approaches, K-NN outperformed Linear Regression and achieved higher accuracy with less complexity than Artificial Neural Networks (ANN). The implications of these findings indicate that predictive modeling enables better process stability, reduces the risk of uneven smoking, and lowers energy consumption. The novelty of this research lies in the dual prediction of heat and airflow, providing a comprehensive framework for smart control in traditional food processing. While the study is limited to simulations, it offers valuable insights for future experimental implementation and integration into intelligent smoking chamber systems.
Co-Authors Alang Sunding Dirham Muhammad Muhammad Edy Hidayat Umar Muhammad