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Abstract. This research aims to analyze the effectiveness and reliability of production machines in the process of making public street lighting poles (PJU) at PT. XYZ The main problem faced by the company is high machine downtime so that production targets are not achieved. Therefore, a method is needed that is able to measure machine effectiveness as a whole and identify the main causes of production losses. The method applied includes Overall Equipment Effectiveness (OEE) to measure machine effectiveness based on three components, namely availability, performance and quality. The Total Productive Maintenance (TPM) approach is used to identify factors causing low effectiveness through Six Big Losses analysis. Apart from that, Mean Time To Repair (MTTR) and Mean Time Between Failure (MTBF) are calculated to determine the level of machine reliability. The data used includes machine working hours, downtime, operating time, production quantities, defective products, as well as machine damage and repair data. The analysis results are expected to show the level of machine effectiveness and identify the dominant factors causing downtime. Based on these results, improvement proposals are prepared to reduce downtime, increase machine reliability and improve production productivity Keywords: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime, Efektivitas   Abstrak. Penelitian ini bertujuan untuk menganalisis efektivitas dan keandalan mesin produksi pada proses pembuatan tiang penerangan jalan umum (PJU) di PT. XYZ Permasalahan utama yang dihadapi perusahaan adalah downtime mesin yang tinggi sehingga target produksi tidak tercapai. Oleh karena itu, diperlukan metode yang mampu mengukur efektivitas mesin secara menyeluruh dan mengidentifikasi penyebab utama kerugian produksi. Metode yang diterapkan meliputi Overall Equipment Effectiveness (OEE) untuk mengukur efektivitas mesin berdasarkan tiga komponen, yaitu availability, performance, dan quality. Pendekatan Total Productive Maintenance (TPM) digunakan untuk mengidentifikasi faktor penyebab rendahnya efektivitas melalui analisis Six Big Losses. Selain itu, dilakukan perhitungan Mean Time To Repair (MTTR) dan Mean Time Between Failure (MTBF) untuk mengetahui tingkat keandalan mesin. Data yang digunakan mencakup jam kerja mesin, downtime, waktu operasi, jumlah produksi, produk cacat, serta data kerusakan dan perbaikan mesin. Hasil analisis diharapkan dapat menunjukkan tingkat efektivitas mesin dan mengidentifikasi faktor dominan penyebab downtime. Berdasarkan hasil tersebut, disusun usulan perbaikan untuk mengurangi downtime, meningkatkan keandalan mesin, dan memperbaiki produktivitas produksi Kata kunci: Overall Equipment Effectiveness (OEE), Total Productive Maintenance (TPM), Six Big Losses, Downtime Mesin, Efektivitas Mesin

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

Wastewater Treatment Plant (WWTP) pada kawasan industri konvensional umumnya masih mengandalkan strategi pemeliharaan berbasis interval waktu yang tetap. Pendekatan tersebut berisiko menyebabkan penurunan performa pompa yang tidak teridentifikasi secara dini serta meningkatkan potensi pemborosan energi operasional. Penelitian ini mengembangkan Green Maintenance Framework berbasis machine learning untuk meningkatkan reliabilitas pompa sirkulasi pada sistem Moving Bed Biofilm Reactor (MBBR). Analisis dilakukan menggunakan dataset telemetri multi-sensor yang mencakup parameter getaran, temperatur, tekanan, debit aliran, dan rotasi per menit (RPM). Proses rekayasa fitur diterapkan melalui pembentukan System Efficiency Index untuk meningkatkan sensitivitas model terhadap indikator degradasi kinerja pompa. Model prediktif dibangun menggunakan algoritma Random Forest Classifier dengan skema pembagian data 80:20 secara stratified. Hasil pengujian menunjukkan bahwa model menghasilkan tingkat akurasi klasifikasi sebesar 100%, dengan variabel Vibration dan Temperature menjadi parameter yang paling dominan dalam proses prediksi. Analisis operasional memperlihatkan bahwa degradasi pompa menyebabkan penurunan flow rate meskipun nilai rotasi per menit (RPM) mengalami peningkatan, sehingga memicu kenaikan konsumsi energi dan meningkatkan risiko gangguan pada proses biologis Moving Bed Biofilm Reactor (MBBR). Dari aspek ekonomi, kondisi tersebut menyebabkan pemborosan energi sebesar 5.623 kWh atau setara Rp6.271.236, - per bulan untuk setiap unit pompa. Penelitian ini berkontribusi pada pengembangan sistem predictive maintenance berbasis kecerdasan buatan untuk mendukung efisiensi energi serta implementasi green manufacturing di kawasan industri.

XYZ is a manufacturing company engaged in steel pipe production. In the production process of non-American Petroleum Institute (API) steel pipes, the company still experiences various types of waste, resulting in an inefficient production process. The identified wastes include defects, waiting, transportation, and non-value-added activities, which contribute to increased production time and reduced productivity. This study aims to analyze the major wastes occurring in the non-API steel pipe production process and propose improvements using the Lean Manufacturing approach. The methods employed in this study include Value Stream Mapping (VSM), Value Stream Analysis Tools (VALSAT), Process Activity Mapping (PAM), and Failure Mode and Effect Analysis (FMEA). Data were collected through direct observation, interviews, and documentation of the production process. The results indicate that the dominant wastes affecting the production process are defects, waiting, and transportation. PAM analysis shows that non-value-added activities remain relatively high, leading to production time inefficiencies. Based on the FMEA results, the main causes of waste are machine conditions, work methods, and operator skills. Proposed improvements include periodic machine maintenance, production quality control, work method improvement, and the optimization of material flow.

The implementation of predictive maintenance supported by SAP Plant Maintenance (SAP PM) at PT Xyz has proven to be effective in reducing machine downtime, lowering maintenance costs, and improving asset reliability. The integration of SAP PM with Industry 4.0 technologies such as IoT sensors, AI-based analytics, and real-time notification systems strengthens operational efficiency and ensures continuous performance. Empirical results show improvements in key performance indicators, including a 20-25% reduction in downtime, a 30% reduction in maintenance costs, an increase in asset availability to 97%, an MTBF extension of up to 511 hours, and an OEE rate of 92.1%. These findings highlight the strategic role of digital predictive maintenance in increasing competitiveness and supporting long-term sustainability in manufacturing operations.

This household appliance and machine repair and maintenance program is designed to promote economic independence for the community in Sedati Village, Sidoarjo, by strengthening technical skills based on local potential. The underlying problem of this activity is the community's high dependence on external repair services and limited ability to handle household appliance damage independently. Therefore, the Asset-Based Community Development (ABCD) approach is used, which focuses on utilizing the assets, potential, and capacity already possessed by the community. The program is implemented in a participatory manner, with stages including identification and mapping of local assets, joint planning, technical skills training, hands-on practice, and activity evaluation. The results show an increase in the community's ability and understanding in performing simple household appliance maintenance and repairs. Furthermore, the community is beginning to recognize economic opportunities from their skills, thus potentially developing independent repair service businesses. Thus, the application of the ABCD approach in this activity has proven effective in strengthening the community's economic independence in a sustainable manner through optimizing local assets. Going forward, ongoing mentoring efforts are needed to ensure that the potential that has been established can continue to develop and provide broader economic benefits.

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.

The Preliminary study results indicate that 7 out of 10, or around 70% of production workers at PT Summi Adyawinsa Indonesia experienced a high workload. This research employed a quantitative descriptive method with a cross sectional study design. The sample consisted of 132 workers, selected using a non-probability sampling technique. The study was conducted from June to July 2025. Data were analyzed using univariate analysis and bivariate analysis with the chi-square test. Primary data were collected through questionnaires as the research instrument. The univariate results showed the highest proportions was hish workload (93,9%), long working hours (65,9%), short work period (66,7%), productive age (93,2%), male gender (93,9%), high wages (53%), and good work environment (94,7%). Bivariate results show a relationship between working hours (p value 0,019), and work period (p value 0,05) with workload. There is no association between age (p value 1,000), gender (p value 0,402), wages (p value 0,147), and work environment (p value 1,000) with workload. Workload Companies need to increase the number of employess, perform routine maintenance on machinery and heavy equipment (forklifts and hoist cranes), and provide training for both new and existing workers.

This research investigates intelligent cooling system design for main ship engines operating in tropical waters, integrating advanced machinery engineering with human factors to address thermal management challenges affecting engine performance, reliability, and crew operational effectiveness. Tropical maritime environments impose severe cooling demands through elevated seawater temperatures (28-32°C), high ambient conditions (28-35°C), and accelerated biofouling, reducing conventional cooling system effectiveness by 15-25% while increasing maintenance burdens and operational risks. Through qualitative analysis involving marine engineers, chief engineers with tropical operational experience, cooling system manufacturers, naval architects, automation specialists, and maritime training institutions, this study examines how intelligent cooling systems incorporating variable-speed pumps, adaptive control algorithms, predictive maintenance, and crew-centered interfaces can optimize thermal management while supporting effective human-machine collaboration. Results demonstrate that intelligent systems can reduce cooling energy consumption by 20-35%, improve temperature stability by 50-65%, extend maintenance intervals by 40-80%, and enhance crew situational awareness through intuitive monitoring interfaces, while requiring comprehensive training programs developing technical understanding and operational competencies. Key implementation challenges include control system complexity, sensor reliability in harsh marine environments, integration with existing engine management platforms, crew competency development requirements, and lifecycle cost justification. Findings reveal that successful intelligent cooling system implementation requires holistic sociotechnical approach addressing machinery engineering optimization, automation technology deployment, and human capability development through coordinated design and training strategies. This research contributes to marine engineering literature by providing integrated frameworks for intelligent system design incorporating machinery performance, automation capabilities, and human factors supporting operational excellence in tropical maritime operations.

MMP is a metal manufacturing company engaged in casting, forging, and machining processes with a three-shift work system, including night shifts that may increase mental and physical workload due to disruptions in workers’ biological rhythms. This study aims to analyze the mental and physical workload of die casting machine operators during the night shift and to propose improvement measures using the Fault Tree Analysis (FTA) method. The study involved 23 operators, with mental workload assessed using NASA-TLX and physical workload measured using CVL. The results indicate that mental workload falls into high to very high categories, with WWL values ranging from 46.6 to 97.3, where 12 operators experienced very high mental workload. The dominant contributing dimensions were effort, physical demand, and temporal demand. Meanwhile, physical workload ranged from 19.48% to 36.36% CVL, with most operators not experiencing fatigue. Although physical workload remains within acceptable limits, the high mental workload indicates the need for improvements. FTA analysis identified key contributing factors, including work methods, work systems, ergonomics, machine conditions, and the work environment. Proposed improvements include job rotation, improvements in work methods and task distribution, adjustments to the work system, enhanced machine maintenance, and ergonomics-based workplace improvements.

The development of multimedia-based interactive games requires a system capable of effectively managing game logic, character behavior, and the integration of visual and animation elements. This study aims to implement GDScript in the development of a 2D RPG game using the Godot Engine. The research method was carried out through the design of scene and node structures, the implementation of game logic using GDScript, and the application of Finite State Machine (FSM) to regulate enemy behavior. GDScript is used to control character movement, animation systems, and interactions between players and objects in the game. The implementation of FSM allows enemies to have dynamic behavior through state settings such as idle and wander. Functional testing results show that the game system can run according to the design and is capable of producing responsive interactions. In addition, the use of modular architecture in the Godot Engine facilitates system development and maintenance. Based on the research results, the Godot Engine and GDScript are considered effective for developing multimedia-based interactive games.

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in supporting Indonesia’s economic development, not only by contributing to economic growth but also by creating employment opportunities. Along with the rapid increase in the number of MSMEs, competition among business actors has become increasingly intense, including in the knitted industry, which requires consistent product quality and efficient production processes. In this context, this community service program is designed to strengthen the competitiveness of MSMEs by improving their understanding and implementation of machine maintenance. Proper and well-planned maintenance practices are expected to reduce the risk of machine failure and minimize product defects. The activities were carried out through a series of socialization sessions, training programs, and technical assistance focused on machine maintenance calculations. The expected output of this program is a community service report that will be further developed into a scientific article and published in the Indonesian Journal of Community Service.

This study aims to analyze the transformative impact of digitalization and technology in the manufacturing sector on improving operational efficiency, particularly in budgeting and resource utilization, as well as to identify the main barriers to technology adoption. Using a Literature Review and Case Study Analysis of secondary data (journals, company reports, and industry publications), it was found that digitalization and Automation supported by Artificial Intelligence (AI) fundamentally transform budgeting functions. This transformation has been shown to improve budget accuracy by up to 50% (reducing human errors) and process efficiency by up to 25%, turning budgets from static documents into adaptive and predictive control tools. Positive impacts are also observed in operations through increased production capacity (revenue surge) and the implementation of Predictive Maintenance, which reduces expenditure and asset downtime, in line with the principles Cost Efficiency and Lean Manufacturing. Nevertheless, the adoption of advanced technology faces significant obstacles, namely high initial capital investment and skill gaps among the workforce. It is concluded that the success of digitalization heavily depends on strategic budget planning to overcome capital barriers and adequate allocation of funds for Human Resource (HR) training to support effective collaboration between humans and machines.

Problems in the transmission box of the Drilling Machine 438-02-814-0925 type (0.75 HP, 230/400V, 1425 rpm, produced in 1988) are the degradation of mechanical components for the pulley, V-belt, and rolling bearing. The purpose of maintenance and repair planning is to obtain maintenance costs, maintenance schedules in the period 2026, and the ratio of maintenance costs to profits. The maintenance and repair planning method includes collecting previous maintenance data; application of the inspection-replace-repair-overhaul (IRRO) method; evaluation of the working conditions of components, especially on the pulley, V-belt, spindle pulley (driven pulley), and rolling bearing; prediction of component service life; prediction of repairman costs; prediction of supporting equipment that will be used in maintenance; prediction of spare part replacement time or reinstallation of components after repair; estimation of maintenance and repair costs in 2026; and calculation of the ratio of maintenance costs to profits. The results of maintenance and repair planning obtained maintenance costs in 2026 amounting to Rp 4,627,000 with an estimated Drilling Machine rental rate of Rp 75,000/hour which has the opportunity to be rented for 400 hours/year, and a maintenance cost to profit ratio of 15.42% which implies that the Drilling Machine still has the opportunity to generate profits and is suitable for use in the coming years.

The rapid advancement of information technology has encouraged business actors to adopt digital transformation; this situation is also experienced by Pabrik Tahu Macanan, a small scale tofu factory in Magelang that still relies on manual systems in operation. This  study aims to analyze the implementation of management information systems in supporting digital transformation and risk management at Pabrik Tahu Macanan; a descriptive qualitative approach was applied, using interviews, observations, and documentation as date collection methods. The findings reveal that digital information systems have the potential to improve efficiency, recording accuracy, and internal control; however, their implementation remains limited due to human resource constraints and low adaptability to new technologies. The research also found that simple risk management practices such as regular machine maintenance and manual bookkeeping remain effective in maintaining business stability. The implication of this study indicates that a gradual implementation of digital based information systems, supported by training and supervision, can serve as a strategic step to enhance competitiveness, operational efficiency, and sustainability for traditional SMEs like Pabrik Tahu Macanan.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

The decline in the performance of the die casting machine in 1998 after a long period of producing copper terminals showed dimensional defects and instability in product quality, especially in nozzle clogging, reduced copper flow, crust buildup on the gooseneck, plunger movement obstruction, and hydraulic pressure leaks. The purpose of planning the replacement and repair of die-casting machine components is to obtain replacement and repair costs, replacement and repair schedules for the period 2026, and the ratio of maintenance costs to profits. The replacement and repair planning method includes collecting previous maintenance data, applying the inspection-replace-repair-overhaul (IRRO) method, evaluating component conditions, predicting component service life, predicting labor costs, predicting supporting equipment to be used in maintenance, predicting the time to replace spare parts or reinstall repaired components, estimating replacement and repair costs for the period 2026, and calculating the ratio of replacement and repair costs to profits. The planning results obtained replacement and repair costs for the 2026 period are 75.770.000,- IDR with an estimated die casting machine rental rate of  1,500,000 IDR/hour which has the potential to be rented for 1,200 hours/year, and the ratio of maintenance costs to profits is 10,02 % which implies that the die casting machine with a capacity of 40 units/hour is still suitable for use and has the prospect of generating profits for the next few years.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The problems encountered are damage to the rubber wheel mount and universal/cross joints on the 90 m/hour capacity wood profile making machine, which can affect the uniformity and speed of wood profile making. Maintenance and repair planning aims to be able to create a maintenance and repair schedule for the 90 m/hour capacity wood profile making machine for the period 2026, estimate maintenance costs and the ratio of maintenance and repair costs to machine profits. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing the specifications of the wood profile making machine, estimating the age and price of components that are estimated to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement spare parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the period 2026, and determining the ratio of maintenance costs to profits. The planning results in the form of a maintenance-repair schedule for the period 2026; maintenance and repair costs in 2026, the ratio of maintenance costs to profits, and their implications indicate that the machine is still prospective and usable.