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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.

Mining sequence design is one of the important stages in open pit mining activities that aims to organize the excavation stages so that production activities run effectively, efficiently, and in accordance with the established targets. This research was conducted at PT Putra Perkasa Abadi Site SKS with the aim of designing a mining sequence in January and February 2025. The methods used include analysis of topographic data, geology, pit design, and monthly production targets. The data was processed using mining software to determine the excavation sequence based on elevation, overburden volume, and coal reserves. The design results show that the total planned overburden and coal volume is able to meet the company's production targets by considering slope stability and the efficiency of digging and loading equipment. In addition, the resulting sequence design also takes into account aspects of work safety and field operational conditions, such as mine road access and drainage systems. With a structured design, it is expected that mining activities during this period can run according to schedule, minimize operational obstacles, and support the achievement of production targets and cost efficiency at PT Putra Perkasa Abadi Site SKS.

This study addresses the urgent need for digital transformation at Percetakan Rajawali, a printing company currently reliant on manual order processes, which leads to service delays, recording errors, and lack of transparency. The research aims to design and implement a web-based online ordering information system to facilitate customer orders, accelerate confirmation and order tracking, and improve the efficiency of order data management. Adopting the linear and sequential Waterfall methodology, the study progressed through stages of system analysis, design, coding, testing, and maintenance. The key findings include the successful design of a comprehensive system featuring product catalogs, an online ordering module with real-time cost calculation, integrated payment management, and an admin dashboard for order processing. The system is designed with user-centered and secure principles, expected to significantly enhance operational efficiency, service speed, and customer satisfaction by providing an integrated, transparent, and accessible online ordering platform.

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.

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 rapid advancement of digital technologies and Artificial Intelligence (AI) has fundamentally reshaped the management and development of the tourism industry. Digital transformation strategies offer substantial opportunities to enhance destination competitiveness while simultaneously supporting economic, social, and environmental sustainability. This study aims to systematically examine the role of digital transformation and AI in strengthening sustainable tourism competitiveness through a literature review approach. A total of 42 peer-reviewed journal articles published between 2019 and 2025 were analyzed, drawing from Scopus, Web of Science, and Google Scholar. The analysis employed thematic synthesis to identify dominant patterns, conceptual relationships, and emerging themes across the literature. The findings indicate that AI-driven digital transformation enhances operational efficiency, enables personalized tourist experiences, supports data-informed resource management, and facilitates the development of smart tourism destinations. Nevertheless, persistent challenges related to human resource readiness, digital inequality, data governance, and ethical considerations remain evident. This review provides an integrated conceptual perspective on digital transformation and AI in sustainable tourism competitiveness and offers insights for policymakers, practitioners, and future research.

The development of the digital era has triggered a significant transformation in consumer shopping patterns, which have now shifted from conventional retail to Quick Commerce (Q-Commerce). This article analyzes the phenomenon of changing consumer behavior driven by preferences for speed, practicality, and time efficiency, with the COVID-19 pandemic as the main catalyst. The study highlights that the success of Q-Commerce is highly dependent on Logistics Service Quality (LSQ), particularly in terms of timeliness, courier interaction quality, and order condition. Despite offering convenience that disrupts physical retail, this business model faces serious sustainability challenges, including high last-mile operational costs, difficulty achieving profitability leading to the closure of several market players, and intense competition from hybrid retail models. In addition, traffic safety issues and increased carbon emissions are highlighted as social and environmental impacts. This study concludes that while Q-Commerce holds great potential, its sustainability requires strategic innovations that balance service speed with cost efficiency and ecological responsibility.vThe development of the digital era has triggered a significant transformation in consumer shopping patterns, which have now shifted from conventional retail to Quick Commerce (Q-Commerce). This article analyzes the phenomenon of changing consumer behavior driven by preferences for speed, practicality, and time efficiency, with the COVID-19 pandemic as the main catalyst. The study highlights that the success of Q-Commerce is highly dependent on Logistics Service Quality (LSQ), particularly in terms of timeliness, courier interaction quality, and order condition. Despite offering convenience that disrupts physical retail, this business model faces serious sustainability challenges, including high last-mile operational costs, difficulty achieving profitability leading to the closure of several market players, and intense competition from hybrid retail models. In addition, traffic safety issues and increased carbon emissions are highlighted as social and environmental impacts. This study concludes that while Q-Commerce holds great potential, its sustainability requires strategic innovations that balance service speed with cost efficiency and ecological responsibility.

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.

The rapid growth of e-commerce has increased the strategic role of freight forwarding and delivery service companies in maintaining the efficiency of supply chains. However, the increasing complexity of delivery processes also raises various operational risks, such as package damage, loss, delivery delays, and distribution errors. This study aims to analyze the implementation of risk management in freight forwarding companies operating in Brebes Regency and to assess the effectiveness of the risk management practices applied. This research employs a descriptive qualitative approach, with data collected through in-depth interviews, observations, documentation, and questionnaires. Informants were selected using purposive sampling, involving company management, operational staff, couriers, and customer service personnel. Data analysis was conducted using the Miles and Huberman interactive model and supported by average risk score analysis. The results show that most aspects of risk management fall into the high and very good categories, with an overall average score of 3.89. This indicates that the company has implemented risk management effectively, particularly in incident evaluation, risk measurement, and cooperation with insurance providers. Nevertheless, improvements are still needed in reducing the frequency of operational risks and enhancing the use of digital technology for risk monitoring to ensure a more comprehensive and sustainable risk management system.

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.

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 community service program aims to optimize construction project planning through assistance in preparing a business feasibility study at PT Sahabat Anugrah Sejati. The construction sector is a strategic industry that requires effective resource management, proper risk mitigation, and accurate planning to ensure project success. Therefore, the program supported the partner in analyzing financial, managerial, market, and economic aspects prior to making investment decisions for the proposed project. The results show that the project is feasible based on the Net Present Value (NPV) and Internal Rate of Return (IRR) analyses. An efficient organizational structure, a competent management team, and strong supervision mechanisms further strengthen the project’s operational readiness. Increasing demand for construction services also enhances the company’s market prospects. However, several areas still require improvement, including heavy equipment utilization, material efficiency, and digital marketing strategies. This program provides strategic recommendations to help PT Sahabat Anugrah Sejati improve project feasibility, operational effectiveness, and long-term business sustainability.

The attendance recording system at PLN ULP Tanjungbalai still relies on manual, paper-based methods, resulting in delays in data recap, reduced efficiency, and a high potential for recording errors. This condition affects the accuracy of employee attendance information, which is essential for administrative activities and managerial decision-making. Based on these issues, this practical work aims to design and develop a web-based e-attendance application as a solution to enhance efficiency, processing speed, and the accuracy of attendance recapitulation. The system was developed using PHP as the programming language and MySQL as the database management system, following several stages including requirement analysis, system design using UML, and implementation of a web-based user interface. The application provides essential features such as user login, daily attendance recording, employee data management, attendance notes (permission, sickness, etc.), and automatic attendance report generation. The system is designed for two types of users—Admin and Employees—each with specific access rights. The implementation results indicate that the e-attendance application significantly improves the efficiency of attendance administration at PLN ULP Tanjungbalai. Data collection and recapitulation become faster, more structured, and less prone to errors, while also enabling administrators to monitor employee attendance in real time. Therefore, this web-based e-attendance application serves as an effective solution to support operational activities and enhance the quality of employee attendance management.

This study aims to evaluate the public transportation service policy of Trans Jatim Bus Corridor 2, operating on the Mojokerto–Surabaya route, using the six evaluation indicators proposed by William N. Dunn: effectiveness, efficiency, adequacy, equity, responsiveness, and appropriateness. Employing a qualitative descriptive approach, data were collected through interviews, direct observations, and secondary data analysis. The findings reveal that the Trans Jatim Corridor 2 service has significantly contributed to improving accessibility and mobility for the people of East Java. In terms of effectiveness, the service meets transportation policy objectives by offering strategic routes, consistent schedules, and accessible bus stops. Efficiency is demonstrated through optimal utilization of limited fleets and operational costs, while still meeting high passenger demand. Regarding adequacy, the service is generally sufficient; however, overcrowding during peak hours indicates the need for capacity improvements. Equity is reflected in the widespread distribution of bus stops, although disparities remain in the availability of facilities and route information across several stops. The service shows high responsiveness through quick handling of passenger complaints via applications and social media. Additionally, service appropriateness is evident in its punctual operations supported by GPS-based monitoring and real-time information through the TRANSJATIM-AJAIB application. Overall, the evaluation shows that Trans Jatim Corridor 2 provides effective, efficient, and responsive public transport services, yet requires improvements in capacity and equitable distribution of facilities to achieve optimal service quality.

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.

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 community service activity was conducted at Grind Now Coffee Bar, an MSME in Batam City’s food and beverage sector, to address challenges in managing raw material inventory, which often led to inaccurate stock records, shortages, and overstocking, affecting daily operations and efficiency. The program aimed to improve inventory management by applying the Economic Order Quantity (EOQ) and Reorder Point (ROP) models, providing a systematic and measurable approach to procurement. Activities included detailed observation of operational practices, interviews with staff to understand current challenges, collection of data on raw material usage and purchasing patterns, and the implementation of a website-based inventory control system to streamline record-keeping and monitoring. The results showed that EOQ and ROP models helped determine optimal order quantities and appropriate reorder timing, while the inventory system improved organization, reduced errors, and enabled real-time stock monitoring. Overall, this intervention enhanced decision-making, promoted operational efficiency, and supported the sustainability and competitiveness of Grind Now Coffee Bar.

The integration of artificial intelligence chatbots as virtual teaching assistants (VTAs) represents a transformative shift in student support services within higher education. This study investigates the implementation, effectiveness, and impact of AI-powered chatbots in providing academic support, administrative assistance, and personalized guidance to university students. Employing a longitudinal mixed-methods approach over 18 months, this research analyzed data from 2,347 students across 15 universities that deployed VTA systems, examining interaction patterns, student satisfaction, learning outcomes, and cost-effectiveness. Quantitative analysis of 487,392 chatbot interactions revealed that VTAs successfully handled 78.4% of student queries without human intervention, with response times averaging 3.2 seconds compared to 4.7 hours for traditional support channels. Qualitative findings from focus groups and interviews highlighted students' appreciation for 24/7 availability, immediate responses, and non-judgmental interactions, while also revealing concerns about empathy limitations, complex query handling, and the desire for human connection in critical situations. The study demonstrates that VTAs significantly improve support service accessibility and efficiency while reducing operational costs by an average of 43%. However, optimal implementation requires careful integration with human support staff, continuous training of AI systems, and attention to equity issues in digital access. This research contributes to understanding how AI can augment rather than replace human educators, offering evidence-based recommendations for implementing VTA systems that enhance student success while maintaining the human elements essential to quality education.

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.