This study evaluates the implementation of Behavior Based Safety (BBS) in improving occupational safety compliance at PT Wichindo Pratama, a heavy equipment manufacturing company with high work risks. The topic was chosen due to frequent violations of safety procedures, such as the improper use of Personal Protective Equipment (PPE) and neglect of Standard Operating Procedures (SOP), which increase the risk of accidents. A qualitative method was used through direct observation, in-depth interviews with 12 shift workers, and field assessments of safety facilities. The findings show that although the company has an Occupational Health and Safety (OHS) management system, non-compliance still occurs. Issues include incomplete PPE usage, disorganized work tools, and unsafe rest habits. Observations revealed potential hazards such as physical (noise), chemical (oil exposure), ergonomic (awkward postures), mechanical (machine use without full PPE), biological, and psychological risks (stress from shift work). BBS is seen as a strategy to connect existing OHS policies with safe work behavior by focusing on mindset and daily habits. The implementation of BBS is expected to reduce accidents and support the creation of a safer, more sustainable workplace.

Small-scale industries such as tofu and tempeh factories play a significant role in the local economy but often face serious issues such as workplace accidents. This study aims to describe the working conditions and their relation to unsafe acts by workers at the XYZ tofu and tempeh factory. A descriptive qualitative approach was used through direct observation and interviews with four workers. The findings show that a hot and noisy work environment, poor ventilation, and disorganized workspace affect workers’ behavior in carrying out tasks without using proper personal protective equipment (PPE). Unsafe work practices have become a common habit due to factors such as routine behavior, limited facilities, and demands for work efficiency. The study concludes that an unsupportive work environment is directly associated with the increase in unsafe behavior. Therefore, efforts are needed to improve the working environment, including ventilation, workspace layout, and adequate provision of PPE, as well as to increase workers’ awareness of occupational health and safety through regular training and outreach. These measures are essential to foster a safe and healthy work culture in small-scale industrial settings.

One of the key processes in the production of APAR cylinders is the welding of cylindrical plates, which is carried out using a Longitudinal Welding Cylinder machine and the CO₂ welding method. However, in practice, operators face several challenges, mainly due to the number of process steps that do not significantly contribute to the quantity or quality of the output, resulting in reduced productivity. This study aims to improve the productivity of the welding process through a series of improvements. The methods used include data analysis and root cause identification through the Overall Equipment Effectiveness (OEE) approach and Lean Six Sigma using the DMAIC stages (Define, Measure, Analyze, Improve, Control). Based on the Define stage, the initial OEE value was 67.49%, which is still far below the world-class standard of 85%. In the Measure stage, Pareto analysis revealed that the largest downtime (2,097 minutes or 40%) was caused by cycle time issues. Further analysis showed that activities such as material setup, additional plate placement, and additional plate cutting (totaling 85 seconds) could still be optimized. During the Improve stage, modifications were made, such as eliminating the additional plate cutting process and adding a stopper to ensure accurate welding alignment. These improvements successfully reduced the cycle time from 180 seconds to 120 seconds, thereby decreasing downtime and increasing the OEE value to 76.12%.

Traditional markets are characterized by fast-paced and diverse transactions, necessitating a reliable product monitoring system to enhance the efficiency of stock management and transactions. This study develops a monitoring system based on a loadcell sensor and a TCS230 color sensor to automatically classify product weight and type. The loadcell is used to measure product weight with high accuracy, while the TCS230 detects the color characteristics of products to distinguish between different types of commodities, such as various varieties of chili peppers. The development process includes sensor calibration, dataset collection, and the training and evaluation of a classification model. Experimental results show that the classification accuracy exceeds 90%, demonstrating the effectiveness of combining weight and color data for market product recognition.

Applying ergonomic principles in work tool design for manufacturing industries is a crucial factor in improving productivity while maintaining worker health. This research aims to analyze the effectiveness of adaptive work tool design models based on cognitive and physiological ergonomic principles, identify interaction patterns between workstation design and operational performance, and develop a conceptual framework for integrating ergonomic principles into production cycles. The research method adopts a cognitive-physiological approach with qualitative analysis of human-machine interactions, biomechanical simulations using digital human modeling, and muscle load measurements through electromyography. Implementation was conducted using a participatory ergonomics approach and IMU sensor-based real-time monitoring systems. Results show that using materials with controlled deformation capabilities (15-20%) in work tools reduces muscle work by up to 27%, while adaptive automation system integration improves assembly accuracy by 18%. Workstations with ergonomic adjustments increase assembly speed by an average of 12%, and low-cost ergonomic interventions effectively improve productivity by 11-15% in resource-limited environments. Longitudinal analysis reveals that evidence-based ergonomic investments yield a 230% ROI through increased productivity, reduced injury compensation costs, and decreased employee turnover. IMU-based posture monitoring systems integrated with adaptive feedback loops reduced musculoskeletal disorder incidents by up to 41%. In conclusion, ergonomic optimization based on cognitive-physiological principles creates synergy between production efficiency and worker well-being, making it an essential component in achieving sustainable productivity.

Spring steel is one type of material that is widely used in various engineering applications, such as vehicle suspension systems and equipment that require resistance to dynamic loads. Spring steel possesses various mechanical characteristics that are strongly influenced by the microstructure of the material. These characteristics remain consistently affected throughout the entire material. Heating that occurs due to exposure to flame can alter certain microstructural properties of spring steel and affect its performance. In this study, an analysis was carried out on all changes in the microstructure of spring steel caused by flame heating, as well as its effect on the hardness of the material. The results of the study showed that heating can indeed cause changes in the microstructural phase and a decrease in fatigue resistance of the spring steel.

Ports are critical infrastructure in maritime transportation, facilitating the movement of goods and people while serving as hubs for economic activities. The Tanjung Pakis Class III Harbormaster and Port Authority Office (KSOP) utilizes the Electronic Reporting Information (SIRANI) application for reporting loading and unloading data. However, challenges such as discrepancies, technical disruptions, and delays in data processing often arise. This study aims to describe the loading and unloading data processing and the efficiency of reporting time on the SIRANI application at KSOP Class III Tanjung Pakis. Employing a descriptive qualitative approach through interviews, observations, and documentation, the research finds that data processing is less effective due to low data validity and errors in the Inaportnet platform. Reporting to SIRANI is also inefficient due to delays in data handling and technical issues with the application. Corrective measures, such as early data verification and manual data handling by reporting officers, have improved effectiveness by reducing the process from five to three steps and cutting reporting time from 6 hours to 3 hours and 30 minutes. The study concludes that optimizing data processing and inter-unit coordination can enhance the effectiveness and efficiency of reporting. Recommendations for future research include real-time integration and the development of backup mechanisms to address technical disruptions.

This study analyzes and optimizes production systems in the Industry 4.0 context, examining the fundamental shift from centralized, push-based production models to decentralized, adaptive, pull-based approaches. The research employs a mixed-method approach combining comprehensive literature review and multiple case studies across manufacturing sectors. Findings reveal that integration of Internet of Things (IoT), cyber-physical systems, artificial intelligence, and big data analytics enables real-time communication between production components, product personalization, and faster decision-making. Despite significant benefits in efficiency, flexibility, and competitiveness, implementation challenges persist, including high initial investment, employee resistance, technical expertise limitations, and integration complexity. Optimization approaches such as mixed-integer linear programming, digitally-integrated Lean Six Sigma, and digital twin simulations effectively enhance performance indicators including flexibility, reliability, and energy efficiency. The study concludes that successful production system transformation requires an integrated strategy encompassing process engineering, digital competency development, change management, and continuous evaluation to ensure sustainable optimization in the digital era

A reliable electrical grid the power plant scheduling system needs to optimize the plant's performance by considering the economic value and the value of emissions generated by the plant, in addition to the reliability and economic sectors. This system should also take into account the environmental impact, as well as the CO2 and CH4 emissions produced by the plant. This research will use the Dragonfly Algorithm with weighting parameters to schedule hydro and thermal facilities' emissions and economic activities. That the weighting value impacts production costs and emissions is demonstrated by the data acquired from the Dragonfly Algorithm simulation. If economic considerations take precedence in assigning weights, then low-cost generating will result in high-value emissions, and vice versa. The plant's Emission Intensity rating also meets the standards established by the government.

This study aims to evaluate production waste at PT XYZ, a food manufacturing company in Bandung, Indonesia, which experiences inefficiencies caused by overproduction and product defects. A descriptive qualitative method with a case study approach was used, employing data from production reports, field observations, and interviews. The analysis was conducted using the 7 Waste framework, the 5S workplace organization method, and the PDCA (Plan-Do-Check-Act) cycle. The findings show that the main types of waste at PT XYZ are overproduction and defects, which are caused by inaccurate demand forecasting, ineffective layout, and inconsistent quality control. By implementing 5S and PDCA, the company successfully reduced waste, improved workspace organization, and increased process efficiency. This study provides practical insights for manufacturing companies seeking to implement lean strategies for continuous improvement.