Monitoring The Temperature of The Immersion Freezing Process to Prediction and Analyzing Production Costs Mirroring Jewelry

Abstract

This research is motivated by problems at PT Sentral Kreasi Kencana in the immersion freezing process for jewelry production. This process is still carried out manually with a fixed time assumption of 45 minutes, which causes temperature uncertainty and difficulty in increasing production quantity. This has an impact on decreasing efficiency and increasing production costs per gram. The study aims to increase the efficiency of the immersion freezing process by implementing a sensor-based temperature monitoring system and production cost analysis. The methods used include developing a temperature monitoring system using the MLX90614 sensor, integration with Arduino Uno, and creating a graphical user interface (GUI) for real-time data analysis. The system developed consists of an MLX90614 infrared temperature sensor, an Arduino Uno microcontroller, a buzzer as an alarm, and a push-button for batch calculation. The GUI displays real-time temperature data, trend graphs, total batches, product weight, fixed costs, and cost/gram calculations. The results showed that the implementation of the temperature monitoring system increased the number of daily production batches by 50%, from 4 to 6 batches. The processing time was also reduced from 45 minutes to 30 minutes per batch. The optimum temperature of −7◦C was set as the reference point for the immersion freezing process. Production cost analysis showed a significant decrease in cost/gram from IDR 6,333.33 to IDR 2,638.89, far below the company’s standard cost of IDR 5,000.00 per gram. This system has proven effective for gold with a content of 34.0%, 67.1%, and 75.5%. The implementation of this technology has succeeded in increasing production efficiency, reducing the cost per gram, and increasing overall production capacity. This research provides a practical solution for optimizing the immersion freezing process in the jewelry industry, with the potential for wider application in other precision manufacturing sectors.