International Journal of Engineering Continuity

Development and Evaluation of an ESP32-based Temperature and Humidity Control Unit for Textile Storage

Dedy Harianto — 2024-11-18

Monitoring temperature and humidity in textile storage warehouses is vital for maintaining product quality and ensuring optimal conditions. This study focuses on developing a temperature and humidity control unit using an ESP32 microcontroller, evaluating its performance through black-box testing. The ESP32-based system offers a scalable and energy-efficient solution for climate control, delivering precise environmental monitoring through integrated Wi-Fi and Bluetooth for affordable connectivity. Its advanced real-time data processing capabilities and compatibility with multiple sensors make it highly suitable for cost-effective, large-scale implementations in textile storage environments. The experimental approach involves controlling temperature and humidity as independent variables, while ensuring optimal storage as the dependent variable. The DHT21 sensor, ESP32 microcontroller, and relay are used as control variables. Software was developed in the Arduino IDE to manage temperature and humidity, and after validation, the program was uploaded to the ESP32 for black box testing. Results confirmed that the system effectively regulates these conditions, crucial for preserving textiles. The ESP32 efficiently serves as the control unit, and testing validated its ability to maintain desired limits. Future improvements could include wireless access for remote monitoring, enhancing flexibility and operational efficiency in environmental control for textile storage.

Analysis of Determining Holding Area for Flights: Case Study of Halim Perdanakusuma Airport

Rika Kariani — 2024-11-27

This study analyzes the determination of holding areas for flights at Halim Perdanakusuma Airport, which serve as temporary zones for aircraft during delays in departure or arrival. With increasing flight activities, optimizing holding areas is essential to ensure smooth airport operations. The study integrates weather radar and rainfall data with evaluations of airport operational capacity and flight safety considerations. This approach identifies optimal holding area locations by accounting for rainfall intensity, visibility, and weather disruptions. Key findings highlight that using weather radar for predictive analysis can significantly reduce delays and enhance safety in challenging weather conditions. The contribution of this research lies in proposing a data-driven methodology for holding area management that can be applied to airports facing similar challenges. This approach not only supports better decision-making but also offers practical strategies for adapting to dynamic weather conditions. Future research could explore incorporating advanced technologies, such as artificial intelligence, to further refine predictive capabilities and expand the scope of analysis to multiple airports.

Predicting Water Levels from Environmental Parameters Using Random Forest Models

Arum Putri Kusuma Anggraini — 2024-12-12

Real-time monitoring of sea water levels is essential for maritime safety, coastal management, and disaster mitigation. This study addresses the challenges of sensor dependency and environmental vulnerability in traditional monitoring systems by proposing a machine-learning-based soft sensor. A Random Forest model was developed to predict sea water levels using atmospheric parameters such as barometric pressure, temperature, and relative humidity, leveraging data collected over seven months at one-minute intervals from a Marine Automatic Weather Station (AWS) in Tanjung Priok, Indonesia. Data preprocessing included outlier removal, normalization, and temporal feature extraction. The model achieved a high correlation coefficient (R = 0.8415) and low error metrics (MSE = 0.0209, RMSE = 0.1448), demonstrating robust predictive performance. The findings confirm the model's ability to capture tidal patterns and its potential to complement or replace physical sensors in harsh maritime environments. This research contributes to the field by improving monitoring resilience and reducing dependency on hardware sensors. Future work will explore integrating additional environmental variables, temporal modeling techniques, and hybrid approaches to further enhance prediction accuracy and robustness.