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Analysis of Occupational Safety in the Flour Production Process Application of Occupational Safety and Health Using the HIRADC Method in the HIRARC Flour Production Process Gunawan, Taopik Sendy; Sena, Boni
Journal of Education Technology Information Social Sciences and Health Vol 3, No 2 (2024): September 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetish.v3i2.3195

Abstract

Occupational Safety and Health (K3) is a critical aspect in the rice production industry, which is often ignored, due to non-compliance with K3 standards, various problems have been identified. Meanwhile, the HIRADC (Hazard Identification, Risk Assessment, and Determining Controls) method is an important tool in occupational safety and health management that is used to identify hazards, assess risks, and determine appropriate control measures. This research aims to provide a brief explanation regarding the application of the HIRADC method in industrial work environments. The HIRADC process includes three main stages: hazard identification, risk assessment, and control determination. Hazard identification is carried out to identify potential causes of injury or loss. A risk assessment assesses the frequency and impact of each identified hazard, while a control determination focuses on implementing measures to eliminate or reduce the risk. The research results show that the systematic application of the HIRADC method can improve work safety and reduce the incidence of work-related accidents and illnesses. In conclusion, HIRADC is an effective method that can be integrated in a safety management system to create a safer work environment. This research aims to identify hazards, assess risks, and determine control measures in the flour production process using the HIRADC (Hazard Identification, Risk Assessment, and Determining Controls) method. This method is applied at several stages of flour production in industry, from milling to packaging. In this research, the method of direct observation was used during the testing process. The research results show that some of the main hazards identified include exposure to flour dust which can cause respiratory problems, the risk of fire and explosion due to flammable dust, and physical injury from grinding machines. The risk assessment indicates that these risks have a high probability and serious impact on worker health and safety. As control measures, it is recommended to use an effective ventilation system, implement safe machine operating procedures, and provide personal protective equipment (PPE) such as masks and ear protectors. Systematic implementation of the HIRADC method has been proven to increase work safety and reduce accident incidents in flour mills. In conclusion, HIRADC is an effective tool in managing occupational safety and health risks in the flour production industry.
Pemodelan Prediksi Temperatur Freezer Menggunakan Pendekatan Machine Learning Berbasis Framework TensorFlow Davi, Ahmad; Sidiq, Farkhan Jatmiko; Arrizal, Muhammad Aziz; Wibowo, Fahrizal Agil; Gunawan, Taopik Sendy; Aisyah, Andini Nur; Hidayat, Alif Nur
Go-Integratif : Jurnal Teknik Sistem dan Industri Vol 5 No 02 (2024): Go-Integratif : Jurnal Teknik Sistem dan Industri
Publisher : Engineering Faculty at Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35261/gijtsi.v5i02.12524

Abstract

Temperature control in freezers is crucial to maintaining product quality and safety, particularly in the food and pharmaceutical industries. Uncontrolled temperature fluctuations can lead to product damage, increased waste, and reduced quality. Machine learning technology offers an effective solution for predicting and controlling temperature, enabling more accurate monitoring and rapid responses to changing conditions. This study aims to develop a machine learning model using the TensorFlow framework to predict freezer temperatures. Temperature data were collected from sensors installed inside the freezer and used to train and test several machine learning architectures, including Long Short-Term Memory (LSTM) and 1D Convolutional (Conv1D) networks. Model development leveraged TensorFlow's advanced features, enabling efficient model creation, training, and testing. The results show that the Conv1D model with a data composition of 90% training, 5% validation, and 5% testing achieved the best predictions, with a test RMSE of 0.02085°C and a test MAPE of 0.33522%. This predictive model has the potential to be used as an early warning system to prevent product damage. This research is expected to significantly contribute to the development of more efficient temperature monitoring and control systems in freezers, with potential applications across industries such as food and pharmaceuticals. The findings also reinforce the substantial potential of machine learning in environmental prediction and monitoring.