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Background: Plastic film sheet production at PT EA1 requires a uniform M-Coat layer to meet product quality specifications; therefore, the suitability of the coating-media container is critical to process stability and coating consistency. Objective: This study aims to redesign the M-Coat tank so that its dimensions and configuration match the coating process requirements and the machine working mechanism. Methodology: The research employed an industrial case-study design, collecting data through observation, interviews, and documentation, supported by direct dimensional measurements of the product and equipment constraints. Data were analyzed using the Design for Manufacturing and Assembly (DFMA) method to develop and evaluate the redesigned tank concept in terms of manufacturability, assemblability, and operational suitability. Findings: The proposed M-Coat tank redesign is applicable to the production line and can be operated optimally to support the plastic sheet coating process at PT EA1. Implications: The results indicate that DFMA-based redesign of process equipment can improve implementation feasibility on the shop floor while supporting consistent coating performance in coating production environments. Originality: This study extends DFMA from conventional product/component redesign to coating-line process equipment redesign by integrating manufacturability and assembly considerations with real operational constraints in plastic sheet coating production.

This study aims to determine the impact of Financial Technology (Fintech) and digital banking on the overall performance of conventional banking institutions in Indonesia. This research employs a qualitative approach using a comprehensive literature review as its primary method. Data analysis in this study was conducted through an extensive review of various sources, including books, peer-reviewed journal articles, research reports, official websites, newspapers, and magazines, to obtain a holistic understanding of the topic. The results of this study indicate that both Fintech and digital banking significantly affect the performance of conventional banks in Indonesia. Specifically, the effect of Fintech on conventional banking is reflected in a decrease in traditional banking profitability, because Fintech platforms provide alternative channels and competition in credit distribution and financial services. Meanwhile, the effect of digital banking on conventional banking includes improvements in operational efficiency, wider service accessibility for customers, and noticeable changes in customer behavior and expectations. These findings suggest that conventional banks must adapt strategically to remain competitive in the evolving financial ecosystem.

Cooling towers are widely used in various industrial applications for water cooling systems. In these towers, water is cooled by air, and the heat released from the water to the air consists of both sensible and latent heat. The efficiency of this heat transfer significantly impacts the performance of the cooling tower. Objective: This study aims to determine the effectiveness and mass flow rate of water in a cooling tower, focusing on the relationship between water temperature, flow rates, and overall cooling tower performance. Method: The analysis includes measurement of the water temperature (48.176 °C), mass flow rate (175.235 kg/s), and the wet bulb temperature (16.988 °C) in the cooling tower. These parameters are analyzed to assess the cooling tower's performance in terms of its heat transfer efficiency. Results: The cooling tower's performance analysis shows an effectiveness of 59.36%. The mass flow rate of water is 175.235 kg/s, and the hot water flow rate is 9373.32 kg/s. The data indicate that the improved water flow and air flow have a positive impact on the heat transfer rate. Novelty: This study highlights the critical role of maintenance and the optimization of flow rates and cooling tower components in enhancing the heat transfer efficiency. It offers valuable insights into the relationship between operational parameters and cooling tower performance. Implications: The findings suggest that proper maintenance and improvements to water and air flow can significantly enhance cooling tower efficiency, leading to better heat transfer and overall system performance. This has important implications for industrial applications that rely on cooling towers for effective water cooling systems.

Engine performance is a critical parameter in automotive engineering as it directly affects power output, torque characteristics, and overall vehicle efficiency. One of the components that significantly influences engine performance is the exhaust system, which regulates exhaust gas flow and back pressure. This study aims to experimentally analyze the effect of replacing a standard exhaust system with an aftermarket exhaust system on the performance of a 2NR-VE engine. The research employed an experimental method using a comparative design with two testing conditions: a factory-standard exhaust system and an aftermarket exhaust system. Engine performance testing was conducted using a dynamometer (dyno test) to measure horsepower and torque across various engine speed ranges (RPM). Each testing condition was performed repeatedly, and the resulting data were averaged to reduce measurement errors and minimize the influence of operational fluctuations. The experimental results indicate that the aftermarket exhaust system significantly improves engine performance. The maximum power increased from 82.5 HP to 91.6 HP, while the maximum torque rose from 131.2 Nm to 154.4 Nm. These improvements suggest that the aftermarket exhaust configuration effectively reduces exhaust back pressure and enhances exhaust gas flow efficiency. Therefore, replacing the exhaust system can be considered an effective approach to improving engine performance, provided that the modification is technically appropriate and complies with applicable emission and noise regulations.

Distribution transformers play a crucial role in delivering electrical energy from the distribution system to consumers to ensure power quality and supply continuity. However, in practice, overload conditions often occur due to increasing demand and load growth that exceed the transformer’s rated capacity. This situation can lead to reduced efficiency, increased power losses, and accelerated equipment aging. This study aims to analyze the performance of the CMY distribution transformer at PT PLN (Persero) ULP Labuan, which operates beyond its nominal capacity, and to propose an alternative solution through transformer mutation, namely the replacement of the existing unit with a transformer of more appropriate capacity based on load analysis results. The Least Square Method is employed to predict future load growth and determine the projected time when the transformer will again experience overload after the mutation. The results indicate that the existing 100 kVA transformer is overloaded and should be replaced with a 160 kVA unit. After the mutation, the loading percentage decreases significantly, the transformer’s lifespan is extended, and the reliability of the distribution system improves. Furthermore, the Least Square prediction suggests that the new transformer may experience overload again in future years if no further planning is carried out. Therefore, transformer mutation can be considered an effective and medium-term solution to enhance and maintain the reliability of the electrical distribution system within the operational area of PT PLN (Persero) ULP Labuan.

Distribution transformers play a critical role in delivering electrical energy from medium-voltage networks to low-voltage consumers. At ULP Medan Kota, several distribution transformers have been operating with loads exceeding 80% of their nominal capacity, posing risks of overloading, efficiency reduction, and equipment failure. This study aims to analyze the performance of distribution transformers based on actual load data and evaluate mitigation strategies through the implementation of additional parallel transformers (trafo sisip). The methodology includes data collection, load and current calculation, and simulation of load distribution after transformer insertion. The results show that the installation of trafo sisip reduces the load on the main transformer by approximately 50% and significantly lowers the current to safer levels. Moreover, placing the trafo sisip at an optimal position minimizes voltage drop to as low as 0.0745 Volts. Therefore, the addition of trafo sisip is proven to enhance the reliability, efficiency, and operational life of the power distribution system at ULP Medan Kota.

Energy efficiency in water heaters is a crucial factor in ship operational environments due to limited electricity resources that rely on generators. This study aims to design and build an IoT-based water heater monitoring system with an innovative heat storage medium in the form of a mixture of silica sand and paraffin wax to improve thermal efficiency. Although previous studies have developed temperature monitoring and control systems in IoT-based water heaters, this study specifically fills this gap by analyzing the performance of adding silica sand to overcome the low thermal conductivity of paraffin wax. Using the Research and Development (R&D) method, this system was built with an ESP32 microcontroller as the control center, a DS18B20 temperature sensor for accurate measurements, and the Blynk and Google Sheets platforms for real-time monitoring and data recording. Performance testing was conducted by comparing the water heating rate between pure paraffin wax media and the mixed media. The results showed that the monitoring system functioned reliably, and the main finding proved that the addition of silica sand to paraffin wax significantly increased heating efficiency. This was clearly seen from the reduction in time required to raise the water temperature to 40°C, from 2.5 hours to only 1 hour in the second heating cycle. The results of this study indicate that the integration of silica sand and paraffin wax media with IoT technology can increase the efficiency of water heaters and provide an innovative solution for energy-efficient and environmentally friendly temperature control.

The growing demand for transparency, accountability, and measurable performance has transformed higher education institutions into complex public organizations required to deliver reliable and stakeholder-oriented services. Within this governance-driven environment, institutional governance plays a fundamental role in shaping service excellence and institutional legitimacy. Drawing on a public administration perspective, this study examines how governance dimensions influence academic service performance in higher education. This research employs a qualitative descriptive-analytical design. Data were collected through in-depth interviews, document analysis, and institutional observations involving university leaders, academic administrators, faculty members, and students. The analysis focuses on governance dimensions—transparency, accountability, participation, effectiveness, and responsibility—and their integration into institutional systems such as performance management, quality assurance, and digital infrastructure. The findings reveal that governance frameworks are formally established through regulations and digital systems; however, their operational integration remains uneven. Transparency improves service reliability when supported by consistent information management, while accountability mechanisms tend to emphasize procedural compliance rather than performance-based evaluation. Stakeholder participation is institutionalized but largely consultative. The study concludes that service excellence in higher education is a governance-driven outcome that requires systemic alignment between governance principles, institutional capacity, and performance management processes. Strengthened governance integration enhances service reliability and institutional legitimacy.

PT. Tirta Fresindo Jaya, specifically its Pasuruan plant as the producer of Pucuk Harum Tea beverage, requires a large supply of steam to support various production processes. This steam is used in the process of heating tea leaves, heating water through a heat exchanger, and heating chemicals and water in the cleaning in place (CIP) process. Steam pressure of 5 bar that is flowed to the process equipment will undergo condensation to produce condensate. To separate steam and condensate, steam traps are used, consisting of several types, namely mechanical, thermostatic, and thermodynamic. However, in operational practice, steam trap malfunctions often occur, either in the form of steam leaks that are wasted with condensate or failure to drain condensate from the system. This condition causes a decrease in the efficiency of the steam piping system and increases the workload of the boiler. As a result, fuel consumption and boiler feed water requirements become greater than ideal conditions. Therefore, this study aims to analyze the energy and operational losses caused by steam trap malfunctions, as well as evaluate their impact on boiler system performance and steam utilization efficiency at PT. Tirta Fresindo Jaya Pasuruan plant.      

This study aims to analyze the impact of business combinations through acquisition on the financial performance of PT Sarana Menara Nusantara Tbk. The research employs a descriptive quantitative approach, focusing on the acquiring firm in the Indonesian telecommunications infrastructure sector. The data used are secondary data obtained from the company’s annual financial statements for the period 2019–2023, sourced from the Indonesia Stock Exchange and the company’s official website. Financial performance is analyzed using Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), and Debt to Equity Ratio (DER) by comparing the periods before, during, and after the acquisition conducted in 2021. The results indicate that the acquisition exerted short-term pressure on asset efficiency and profitability, as reflected by the decline in ROA and NPM in the year of acquisition. However, in the post-acquisition period, the company demonstrated an improvement in operational performance, particularly in Net Profit Margin, suggesting that the economic benefits of the business combination gradually materialized. Meanwhile, fluctuations in ROE and DER reflect adjustments in the capital structure following the acquisition. These findings suggest that the success of an acquisition cannot be evaluated solely based on short-term financial performance but requires continuous assessment to capture its medium- and long-term effects. This study provides practical implications for management in formulating post-acquisition integration strategies and contributes empirically to the accounting and finance literature on business combinations in Indonesia.

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.

This study aims to analyze the strengthening of higher education governance in realizing transparent and accountable academic services. In the context of the globalization of higher education and increasing public accountability demands, universities are required not only to excel academically but also to provide services that are open, responsive, and accountable. This research employs a qualitative approach with a descriptive-analytical design. Data were collected through in-depth interviews, observations, and documentation studies involving university leaders, academic administrators, lecturers, and students. Data analysis was conducted interactively through data reduction, data display, and conclusion drawing. The results indicate that the principle of transparency has been implemented through digital-based academic information systems; however, consistency in updating information still needs improvement. Accountability has been supported by standard operating procedures and service evaluations, although performance measurement based on indicators has not been fully integrated. Stakeholder participation has been facilitated through evaluation forums, but involvement in strategic decision-making remains limited. Overall, the dimensions of transparency, accountability, participation, effectiveness, and responsibility are interrelated in shaping the quality of academic services. This study emphasizes that strengthening governance must be systemically internalized within organizational culture and institutional operational systems to enhance trust and stakeholder satisfaction.

The objective is to analyze the competitiveness of modern retail through the integration of SWOT analysis as a basis for formulating more targeted strategies using qualitative descriptive methods. This paper identifies the strengths, weaknesses, opportunities, and threats that drive the performance of modern retail based on literature and empirical findings from various previous studies. The results of the analysis show that the main strengths of modern retail lie in product completeness, competitive prices, strategic locations, and operational efficiency. Meanwhile, weaknesses arise from suboptimal digitization, minimal online promotion, limited service innovation, and outdated inventory management. Meanwhile, opportunities arise from changes in digital-based shopping behavior, while threats emerge from the dominance of large retailers, e-commerce competition, and economic fluctuations. Based on the SWOT integration, a strategy is formulated that includes strengthening digitalization, optimizing online marketing, improving service quality, and modernizing operational systems to support long-term competitiveness.

This research was conducted at PT PLN Nusantara Power UP Paiton, one of the largest coal-fired power plants (PLTU) in Indonesia, which focuses on reducing the generation of hazardous and toxic oil waste through the implementation of an oil purification system. The use of large amounts of lubricating oil in the Electro-Hydraulic System (EHS) has the potential to produce high amounts of used oil waste. To address this, the company implemented two main technologies, namely Water Content and Varnish Removal, which function to reduce water content and varnish layers so that the oil can be reused without reducing engine performance. This research used a quantitative descriptive method with data collection techniques through field observations, interviews, and operational documentation from 2021 to 2024. The results showed that the oil purification system was able to reduce hazardous and toxic waste generation by 11.46 tons over four years. In addition to providing environmental benefits, the implementation of this system also resulted in savings in oil waste costs of approximately Rp6,200,560,000. Technically, purification maintains engine performance by reducing water and varnish content, while from an environmental perspective, this activity supports the principle of reduce in hazardous and toxic waste management. Overall, the oil purification system has proven effective in improving operational efficiency, extending oil life, and supporting sustainable waste management for industrial operations.

This study aims to examine the effect of green innovation on company value, with financial performance as a mediating variable, in the mining industry. This study uses a systematic literature review approach by examining various relevant previous studies. The results of the study indicate that green innovation plays a significant role in improving environmental performance and operational efficiency of companies, which in turn positively impacts financial performance. Good financial performance is a key factor in strengthening company value and stakeholder trust. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies. This study also found that companies that successfully implement green innovation tend to have a better image in the eyes of investors and the public, which contributes to increasing the company's market value. These findings confirm that the implementation of green innovation not only supports environmental sustainability but also provides long-term economic benefits for mining companies, strengthening their position in an industry that increasingly prioritizes sustainability and social responsibility.

The increasing intensity of business competition requires companies to maintain strong financial conditions to avoid financial distress that may disrupt business continuity. This study aims to assess the financial stability and predict the potential bankruptcy of PT Sido Muncul Tbk for the 2022–2024 period using the Altman Z-Score model. A descriptive quantitative approach was applied, utilizing secondary data obtained from annual reports published by the Indonesia Stock Exchange and the company’s official website. Five key ratios in the Altman model were used as indicators to evaluate the company’s financial position and resilience. The results show Z-Score values of 4.74 in 2022, decreasing slightly to 4.66 in 2023, and rising again to 4.79 in 2024. These scores are significantly above the safe threshold of 2.675, indicating that the company is in a healthy financial state with a very low risk of bankruptcy. Overall, PT Sido Muncul Tbk demonstrates stable financial performance, supported by a strong capital structure and consistent operational results. The Altman Z-Score model also proves to be an effective early-warning tool for identifying potential financial problems.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

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.

This study aims to evaluate the effectiveness of various doses of 6.25% Poly Aluminium Chloride (PAC) and 0.1% polymer flocculant in reducing Total Suspended Solids (TSS) and assessing pH changes in coal stockpile wastewater at PT PLN Nusantara Power UP Paiton Unit 9. Stockpile wastewater typically contains high levels of suspended solids originating from water spray activities that carry fine coal particles. The coagulation–flocculation process was performed using the jar test method with PAC dosages of 35 ppm, 50 ppm, and 65 ppm, along with flocculant dosages of 6 ppm and 7 ppm. pH and TSS were analyzed before and after treatment to assess process effectiveness. The results indicate that a PAC dosage of 35 ppm combined with a 6 ppm flocculant achieved the highest TSS removal efficiency of 98.15%. Increasing PAC dosage resulted in reduced performance due to overdosing effects, leading to charge destabilization and impaired floc formation. These findings highlight the importance of optimizing coagulant dosage to improve stockpile wastewater quality for safe reuse in operational activities.

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.