Publication Search

Occupational Health and Safety (OHS) is a crucial aspect of the textile industry to ensure worker protection and maintain smooth production processes. This study aims to identify and analyze potential occupational accident risks in the sizing machine process. The methods used in this research are Job Safety Analysis (JSA) and Risk Assessment to evaluate workplace hazards that may disrupt operations. The results indicate several major risks in the sizing process, including mechanical hazards due to direct contact with machines, ergonomic hazards caused by improper working postures, and chemical hazards from exposure to cotton dust and sizing solution vapors. The main risk factors include human aspects, the work environment, and machinery. The recommended improvements in this study include enhancing work supervision and control, providing safety training for workers, optimizing workplace ergonomics, and conducting regular machine maintenance to reduce accident risks. The proper implementation of safety strategies is expected to improve production efficiency and create a safer and more conducive work environment.

This study aims to describe and analyze: the application of the MPaspor application as one of the bureaucratic reform strategies and the supporting and inhibiting factors for the implementation of the M-Passport application as one of the bureaucratic reform strategies in the Special Class 1 Immigration Office of the City of Surabaya.  The research method to be used is qualitative descriptive.  Data Collection Techniques include: Interviews, Observations, Documentation. The research informant is the Head of the Suarabaya Special Class I Immigration Office. The data analysis technique using interactive model analysis was developed by Miles et al., (2014), namely data condensation, data presentation, and conclusion drawn. The results of the study show . Mobile Passport as one of the Bureaucratic Reform Strategies at the Surabaya Special Class I Immigration Office is carried out based on the Institutional Aspect, namely the M-Passport Application based on the Letter of the Director of Immigration Traffic Number IMI.2-UM.01.01-4.0700 dated February 18, 2022 concerning the Follow-up of the Implementation and Implementation Policy of Mobile Passport (M-Paspor) at Immigration Offices throughout Indonesia. In terms of bureaucratic reform, M Passport services in the institutional field, office employees must have proficiency, that is, each office employee must be able to master the skills and knowledge needed in serving citizens, especially foreign citizens who take care of ITAS and ITAP. In terms of resources, it is already available and given full support with the availability of competent IT experts to support the development and implementation of the M-Passport application. The Financial Resources provided continue to be provided for the launch, maintenance, and renewal of the M-Passport application. In terms of governance, as a Technical Implementation Unit of the Special Class I Immigration Office of Surabaya by paying attention to the provision of facilities to support the running process of a bureaucracy.  The Surabaya Special Class I Immigration Office provides a guarantee of passport processing time by officers during the interview session, photo taking, and fingerprint scanning for three working days. Passport application service fee Passport costs Rp 350,000, while electronic passport costs Rp 650,000. One of the supporting factors for the implementation of the M Passport Application at the Surabaya Special Class I Immigration Office is supporting facilities and infrastructure, such as queue machines, service counters, and computers. Inhibiting factors include application servers that often crash or have problems. In addition, there is a lack of public understanding that in taking care of passports, original documents must still be brought. 

The Shipyard Company is a company that operates in the shipping sector, including ship production, overhaul and ship maintenance. In this case, the Shipbuilding Company has an important role in providing quality and timeliness in every project that has been accepted. Quality and timeliness are closely related to the planned production schedule, with efficient and effective scheduling it will have an impact on the quality results and production progress carried out, and cut excess production costs. Based on these needs, an analysis was carried out using the Precedence Diagram Method (PDM) on the Install Machinery Engine Deck 1 sub-job on the Warship project. The data needed in this research is the work breakdown structure (WBS). Based on the analysis that has been carried out, it was found that the most efficient path was found by eliminating dummy factors, so that work can be carried out simultaneously without waiting for previous work. In this case, it was found that the processing time for the Install Machinery Engine Deck 1 work on the Warship project was 471 days, compared to 657 days previously. Thus, the conclusion of this research is that the PDM method schedule has more efficient results than previous planning.

Product quality is a key factor in enhancing a company's competitiveness. PT Nugraha Potong Tekuk, a producer of MPGG products, experienced a significant increase in the defect rate, reaching 11% of total production during the period from June 24 to July 16, 2024. This study aims to analyze the defect rate, identify the main causes, and provide improvement recommendations. The study follows the Six Sigma methodology, including the Define, Measure, Analyze, Improve, and Control (DMAIC) phases. Based on P-Chart analysis, the production process is within statistical control limits, but the defect rate still requires improvement. Two main types of defects were identified: unevenness and size defects, each accounting for 50% of the total defects. Fishbone diagram analysis revealed four main contributing factors: people, machines, materials, and environment. The people factor includes lack of operator skills and operator fatigue, while the machine factor covers worn-out tools and uncalibrated measuring instruments. The material factor is related to low-quality raw materials, and the environmental factor includes poor lighting and unstable work surfaces. Improvement recommendations include operator training, routine machine maintenance, quality control of raw materials, and improvement of the work environment. This study is expected to help PT Nugraha Potong Tekuk enhance product quality, reduce defect rates, and maintain competitiveness in the market.

Sea water cooling pump is an auxiliary machinery on board which is used to move sea water from one place to another place. The optimal performance of the sea water cooling pump is very important for the operation of the ship's engine, if there is damage in the cooling system it will disrupt the smooth operation of the ship. As happened when the ship was anchored in Jakarta, the sea water cooling pump on the generator did not work optimally, namely experiencing a decrease in pressure so that the supply of seawater to cool the lo cooler and intercooler in the generator did not match the required capacity. Therefore, the purpose of this study is to explain the factors that cause the pump performance is not optimal, analyze the impact that will occur, and provide relevant efforts related to the obstacles faced. The method used in this research is descriptive qualitative. Where observations are made by conceptually understanding the subject of research in the form of non-optimal seawater cooling pump maintenance. The results of this study are to describe the factors that cause the performance of the pump to be not optimal, such as the erosion of the pump impeller, and bearings that have worn out because they have passed the predetermined running hours. Based on these factors, efforts that can be made renewed for eroded impellers, and for worn bearings and routinely grease the bearings. And can make a maintenance schedule in accordance PMS (Planned maintenance system) in accordance with the instruction manual book. 

PT. Dian Swastatika Sentosa generates electricity through a private thermal power plant. This company produces steam and electricity for paper mills. The company hopes to meet its steam and electricity needs in 2023 with a boiler with a load capacity of 180 tons and a maximum capacity of 220 tons. However, from February to July 2023, that goal was not achieved. The most common problems faced by this company are the failure to conduct preventive inspections and timely machine maintenance, as well as the failure to carry out necessary repairs. Delays in machine repairs, which result in inconsistent machine downtime schedules to meet customer demand, leading to damage to boiler equipment, causing abnormal boiler operation and reduced steam production. This research aims to design equipment to withstand operational impacts using failure mode and effects analysis (FMEA) with the Soft Systems Methodology (SSM) and reliability-centered maintenance (RCM) techniques. The RCM method assists in identifying the processes that must be followed to guarantee that a machine or system performs as intended. RCM can also be used to design, choose, and create alternative maintenance methods that are focused on safety, operations, standards, and economics. For the entire problem in the investigation, a dynamic model is necessary. An analytical system is used in this model in order to recognize and duplicate the features of the overall system., as well as to provide repair solutions and address the burden of the problem. This research produces a presentation in the form of problem identification, causal loops, diagrams of machines that frequently experience failures, rich images of the causes and effects of the ball plate crusher liner damage, root cause definitions, and conceptual models for repair and maintenance. business productivity and debating process. 

This study aims to use the fishbone diagram as a tool to identify the causes of damage to the feeder mechanism of scrap machines and to develop Standard Operating Procedures (SOP) that can improve performance and reduce the level of damage. This diagram is used to evaluate various elements that contribute to the damage of the feeder mechanism, such as human, machine, material, method, and environmental factors. Through this evaluation, the main causes of the damage can be identified, and appropriate corrective actions can be formulated. In addition, this research also aims to design SOPs that ensure more efficient operation of the scrap machine and reduce the likelihood of operational errors. The implementation of mechanism improvements and SOPs is expected to enhance the reliability of the scrap machine, reduce downtime, and increase productivity in the mechanical technology laboratory. The findings of this study are expected to make a significant contribution to the development of management systems and equipment maintenance in both industrial and educational sectors.  

SMT (Surface Mount Technology) is an automatic technology used in the installation of electronic components on a PCB (Printed Circuit Board). There are 3 stages in the manufacture of PCB boards with SMD components, namely Solder Paste, Chip Mounter Machine and Reflow Oven. Chip Mounter Machine is equipment used in the installation of SMD components on a PCB board or can be called a Pick and Place machine. This Chip Mounter Machine is an important piece of equipment in the SMT line, because this machine is the axis in the production of PCB board modules. Therefore, maintenance is carried out to maintain this Chip Mounter Machine so that it can continue to be used. The maintenance carried out is preventive maintenance or preventive maintenance, which aims to prevent unexpected damage to the machine and determine the conditions that cause damage to the machine when the machine is operating. Preventive maintenance carried out includes inspection, cleaning and repair of equipment that is carried out in a planned manner within a certain period of time.

PT XYZ is a company that produces various gases in the form of Oxygen, Nitrogen, and Argon in gas and liquid form. In the production process, the machine runs 24 hours non-stop and will stop production when maintenance is employed once a year. The Air Separation Plant (ASP) machine performance measurements are needed to determine the effectiveness of its production. The reseacrh aims to analyze the factors that affect machine productivity. The method used is quantitative, using the Overall Equipment Effectiveness (OEE) approach. In addition, this study uses interviews to determine the factors that affect engine performance. The results show that the OEE value of the machine is 75%, which is still below the international standard OEE value of 85%. Therefore, it is necessary to optimize engine performance. Based on the analysis using a fishbone diagram, there are five influencing factors: human factors, machines, work methods, environment, and materials. Based on these five factors, the engine factor most influences the low value of engine performance. The predictive maintenance calculations are conducted, especially on expender and cold box machines, every 95 days of machine should use with work efficiency of around 40 hours.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.

The problem faced by the Bmrsign Wallgallery company is that products are found that do not match the specifications set by the company. The types of product damage are wrong size frames, broken frames, and uneven corners. To solve this problem, the SQC method is used to reduce the causes of defects in the product. The purpose of this method is to reduce the risk of damage to the product. The object studied was Bmrsign Wallgallery with a sample of 29 days. The use of the SQC method is accompanied by analysis tools in the form of control charts (p-charts), Pareto diagrams, cause and effect diagrams (fishbone). Based on the results of data analysis implementing the Statistical Quality Control (SQC) method using the P-Chart control map, the largest value of the Upper Control Limit (UCL) can be shown as 0.272, while the Lower Control Limit (LCL) is -0.013. The results show that the number of defective products during the frame making process is still within normal control limits. The analysis using the Pareto diagram shows that the types of frame defects are dominated by broken with a percentage of 35% or a total of 66 pcs, inappropriate size shapes as much as 33% or a total of 62 pcs, and uneven corners as much as 32% or a total of 60 pcs. Based on the results of the fishbone diagram analysis, defective products are caused by workers who are not focused, work standards are not adhered to, machine maintenance is not given attention, and there is a decrease in the quality of raw materials.

Aviation is a critical sector that requires high levels of security and safety, with regulations such as KP 139 Year 2018 in Indonesia setting operational standards for Aviation Security (AVSEC). Banyuwangi Airport, as a growing airport, must ensure that its security equipment meets these standards. This study aims to analyze the condition of AVSEC equipment at the airport, assess its compliance with regulations, and provide recommendations for enhancing aviation security and safety. This research employs a qualitative method to describe the condition of Aviation Security (AVSEC) equipment at Banyuwangi Airport and its compliance with KP 139 Year 2018. Data were collected through observation, interviews, and documentation, focusing on the validity and reliability of the data through triangulation. The study was conducted from July 20 to 26, 2024, involving sources from the AVSEC unit. The research steps included obtaining permissions, data collection, observation, interviews, and data analysis to draw conclusions.The research findings indicate that the condition of AVSEC equipment at Banyuwangi Airport reveals that some equipment, such as the X-ray Machine and Walk Through Metal Detector, requires technological upgrades, while other equipment, such as the Hand Held Metal Detector, CCTV System, and Patrolling Vehicle, functions well and meets standards. Although routine maintenance is performed, challenges related to funding and personnel training still need to be addressed. The implementation of Standard Technical Operations (STO) includes equipment feasibility assessments, standard operating procedures, personnel training and certification, as well as routine calibration and maintenance. Performance evaluations are also conducted periodically to ensure that the equipment operates optimally in supporting aviation security.

Workshop PT. XYZ is a part of a company that carries out activities in the field of maintenance and repair, especially hydraulic, pneumatic and lubrication equipment. These workshop activities include welding activities, operating machine tools and other mechanical work. This study aims to determine the value of the potential risk of occupational hazards and the level of risk of potential occupational hazards at PT. XYZ in the workshop area. This study uses an approach with the HIRA (Hazard Identification and Risk Assessment) method to determine the value of the risk of occupational hazards and the level of risk of potential occupational hazards. The results obtained by using HIRA (hazard identification and risk assessment) were obtained as many as 29 risks which were classified based on potential hazards, namely 28% extreme risk, 41% high risk, moderate risk of 31% and there is no low risk. Improvement recommendations are made based on the level of extreme risk (extreme risk) and high risk (high risk) to prevent and reduce the level of risk of work accidents in the workshop area, while for the moderate risk level, the mitigation recommendation is to supervise the work and make an SWI (simplified working instruction) to make it easier for workers to understand safe and correct work procedures.

The continued advancements in Internet of Things (IoT) and Machine Learning (ML) technologies have led to their adoption in various domains including in industries for predictive maintenance among other applications. Given the resource constraints of IoT devices, they cannot process the resource-intensive ML algorithms hence data collected by the devices are first sent to the cloud where the algorithms are hosted for processing and inference with the results being sent back to the devices for action and/or notifications. The need to transmit data to the cloud for processing leads to increased costs, energy consumption, and high latencies affecting the implementation of the solution. Interestingly with Tiny Machine Learning (TinyML), it is possible to develop algorithms enabling edge inference on resource-constrained devices. From existing review papers, the researchers were not able to find, a comprehensive review with a focus on this area showing the need for a targeted review that can shed light on how TinyML can be tailored for predictive maintenance tasks in industries. This study therefore presents a systematic literature review of the application of TinyML in predictive maintenance in industrial settings. TinyML overview and its benefits are presented, a TinyML process flow is proposed and various use cases and their classifications have been presented. Through this exploration, the study shows the critical need for TinyML-driven solutions in predictive maintenance, identifies the existing challenges, and proposes a roadmap for future research.

PT.X is a business located in Banyuwangi district, East Java. PT.X is engaged in manufacturing which produces mineral water. PT.X Banyuwangi's work procedures have implemented K3 in its work processes. The Lockout/Tagout (LOTO) locking and marking system has been implemented, but unsafe behavior is still found by workers when carrying out work processes using the Lockout/Tagout (LOTO) system, such as not using PPE during machine repair/maintenance processes, and not installing tags. . . during repairs. This research aims to analyze the level of K3 knowledge about Lockout/Tagout regarding unsafe actions in implementing Lockout/Tagout among workers at PT. In general, this research is a type of quantitative research. The aim of using the crosstab method is to identify data patterns, such as the distribution of variable preferences based on the level of knowledge in carrying out actions. The results of the analysis were that 3 respondents in the good knowledge category carried out unsafe actions in the high category representing 37.5% of the total respondents, 5 respondents in the good knowledge category carried out unsafe actions in the low category representing 62.5% of the total respondents. total respondents. Then for the poor knowledge level category, there were 13 respondents who carried out unsafe actions in the high category, representing 56.5% of the total respondents, and 10 respondents who had poor knowledge levels carried out unsafe actions in the low category. representing 43.5% of the total respondents.

This study aims to find the highest value of criteria, sub-criteria and alternatives in prioritizing K3 strategies at PLN NP UP Muara Tawar to ensure and protect the workforce. Using the 5M approach (Money, Man, Machine, Methods and Milieu), 21 sub-criteria and 5 alternatives in order to prevent, overcome and minimize the occurrence of work accidents and their impacts by identifying, analyzing and controlling appropriately to improve K3 in the workplace. The Simple Additive Weighting (SAW) mixed method is used to calculate criteria and sub-criteria and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) calculates alternatives. The results of the analysis show that the highest criterion is man (M) with a score of 0.2113 and the highest sub-criteria are K3 Infrastructure Investment (MO1) and Fire Protection System Infrastructure Investment (MO2) with a score of 0.29577. The highest alternative is with a score of 0.7900 on maintenance of equipment and facilities (A2).    

PT Swadaya Graha is a company that works in the field of construction, fabrication and steel manufacturing. One of the machines used in carrying out its operational activities is the Baoji 1660A lathe. The lathe often fails during the production process. This study aims to determine critical components, critical component reliability values, maintenance scheduling intervals and maintenance cost estimates of the Baoji 1660A lathe using the Reliability Centered Maintenance (RCM) method. The results of this study show that the electrical system with an RPN value of 240, v-belt with an RPN value of 168, and gearbox with an RPN value of 60 as critical components. The reliability value of the electrical system approaches 0.5 at a time interval of 640 hours or 80 working days, the reliability value of the v-belt approaches 0.5 at a time interval of 208 hours or 26 working days, and the reliability value of the gearbox approaches 0.5 at a time interval of 384 hours or 48 working days. From the scheduling interval, the estimated total maintenance cost in 2025 is IDR 7,019,092, in 2026 is IDR 3,615,386, in 2027 is IDR 4,187,484, in 2028 is IDR 4,276,752, and in 2029 is IDR 3,615,386.

The research issues include the performance of three-phase induction motors and the impact of load on three-phase induction motors. This study aims to develop a driving system for empty fruit bunches (EFB) processing machines using induction motors at PT. Suryabumi Agro Langgeng, South Sumatra. The research was conducted at PT. Suryabumi Agro Langgeng in South Sumatra, from May to June 2024. Primary data in this research were obtained through observation and interviews, while secondary data were gathered from document analysis. In the context of this research on three-phase induction motors, document analysis techniques can be used to analyze various documents such as scientific literature, technical specifications, maintenance reports, production records, or other documents related to induction motors. The research methods include system requirements analysis, system design and implementation of the driving system, and performance testing of the developed system. The results show that the use of induction motors can provide better operational efficiency in EFB processing. This research is expected to contribute positively to improving the efficiency and productivity of PT. Suryabumi Agro Langgeng's operations.

The quality of a product is built by the company by paying attention to the needs and desires of customers because an industrial factory will not exist if the products made or ordered are not in accordance with consumer desires. This study aims to determine the causes of fabric defects, especially in one of the Alaska brands in April 2023 as many as 825 defect cases. This research uses the Fault Tree Analysis (FTA) method used to analyze the causes of production defects such as underfeed, double feed, and non-standard thickness. The results of the study were found several factors causing defects in less feed, double feed, and thickness not according to standards, namely raw materials, machinery, and human factors. The improvement strategies during the production process are. Strict supervision and control, periodic training, proper machine maintenance, and selection of quality raw materials are strategies implemented to minimize fabric defects. implement supporting infrastructure to reduce product growth rates.