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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.

The traditional homes of indigenous communities in Indonesia are highly vulnerable to natural disasters, particularly earthquakes, due to Indonesia's location in the Pacific Ring of Fire, which experiences high seismic activity. This situation demands the strengthening of traditional buildings to withstand potential earthquakes. Stilt houses, as a form of traditional architecture, possess characteristics that actually support earthquake resilience, such as flexible structures, the use of lightweight materials, and the application of local wisdom passed down through generations. With the development of modern construction techniques, stilt houses have the potential to become safer and more adaptable dwellings to earthquake shocks. However, indigenous communities, particularly those living in earthquake-prone areas and with lower levels of education, often face limited knowledge and skills related to the basic principles of earthquake-resistant construction. This lack of understanding results in traditional house construction without considering structural safety aspects, ultimately increasing the risk of serious damage and even collapse during an earthquake. These impacts not only threaten life but also cause significant material losses. This community service activity aims to improve the preparedness and resilience of the Sentani Indigenous community by developing earthquake-resistant stilt house models, increasing knowledge and skills in earthquake-safe house construction and maintenance, and encouraging the use of innovative technology and materials. The expected outcomes are the creation of model houses that can serve as examples and the dissemination of information on earthquake-resistant construction technology.

This community service program was carried out to enhance the technological literacy of residents in Telang Sari Village through the introduction of an automated street lighting system based on sensor technology. The system presented to the community utilizes an Arduino microcontroller integrated with an LDR sensor to detect light intensity and an ultrasonic sensor to identify the presence of nearby objects. With this configuration, the street lights operate automatically: they turn on when the environment becomes dark and an object is detected, and turn off when the surroundings are bright or no activity is detected in the sensing area. The program activities included device installation, technical explanation, and a live demonstration to ensure that residents comprehended its functions and benefits. Additionally, the use of solar panels was introduced as an alternative power source to support sustainable operation without relying on grid electricity. The results of the program showed a positive response from the community, as the system was considered effective in improving nighttime safety, reducing energy consumption, and requiring minimal maintenance. Overall, this activity successfully increased public understanding of automation technology and renewable energy applications suitable for rural community development.

This community service program was carried out to enhance the technological literacy of residents in Telang Sari Village through the introduction of an automated street lighting system based on sensor technology. The system presented to the community utilizes an Arduino microcontroller integrated with an LDR sensor to detect light intensity and an ultrasonic sensor to identify the presence of nearby objects. With this configuration, the street lights operate automatically: they turn on when the environment becomes dark and an object is detected, and turn off when the surroundings are bright or no activity is detected in the sensing area. The program activities included device installation, technical explanation, and a live demonstration to ensure that residents comprehended its functions and benefits. Additionally, the use of solar panels was introduced as an alternative power source to support sustainable operation without relying on grid electricity. The results of the program showed a positive response from the community, as the system was considered effective in improving nighttime safety, reducing energy consumption, and requiring minimal maintenance. Overall, this activity successfully increased public understanding of automation technology and renewable energy applications suitable for rural community development.

Road preservation is a type of maintenance that keeps roads in satisfactory working order by using preventive, corrective, rehabilitation, and reconstruction methods. This approach makes sure that the road stays usable for the entire time it is planned to be used. In practice, preservation projects often run into delays that can lead to important contract conditions. To avoid this, there needs to be a structured way to evaluate things through a Show Case Meeting (SCM). This study looks at how SCM was used in the 2024 Girian–Kema–Rumbia–Buyat Road Preservation Project to find out what caused the delays and how well the agreed-upon fixes worked. The method included looking at physical progress, differences between planned and actual performance, and how well the contractor did during SCM Stages I and II. The results indicate that the contractor failed to provide enough workers and move the right equipment, which led to the critical contract condition. SCM Stage I did not meet the required test-case target, but SCM Stage II did, showing that the contractor was able to meet the required performance targets and finish the work on time. These results show that SCM is an important way to control contracts and fix problems, which helps construction projects get done on time and well.

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.

In engineering practice, various methods have been implemented to stabilize landslide-prone slopes, one of which is the use of bore piles. Bore piles are structures that hold back soil and make slopes more stable by reinforcing the ground inside them. However, the application of bore piles in landslide mitigation presents its own challenges, making it essential to identify and anticipate potential issues that may arise in field engineering practice. Bore piles are installed vertically, either parallel or staggered, at specific spacing and are often combined with capping beams and struts to resist soil movement and provide lateral restraint. This method is commonly applied to high-risk slopes or critical infrastructures such as roads, bridges, and buildings. Anticipation strategies in engineering practice consist of three stages: planning, implementation, and long-term maintenance, with this study focusing specifically on anticipation strategies during the implementation stage. Although bore piles serve as an effective solution to reduce landslide risk, their success depends heavily on proper planning, construction execution, and maintenance. Issues such as soil conditions, construction quality, and external factors must be addressed through a comprehensive engineering approach.

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.

This study examines the optimization of military defense in North Sulawesi as a supporting pillar for Indonesia’s new import gateway. Positioned strategically on the Pacific rim, North Sulawesi is expected to become a hub for imported goods to support national economic equalization. However, this import route shift also brings security challenges, including military and non-military threats such as smuggling, maritime crimes, and potential infiltration by terrorist groups. This research employs a qualitative descriptive method based on literature study, focusing on the analysis of naval and air defense strategies, modernization of main weapon systems (alutsista), and the enhancement of human resources within the Indonesian National Armed Forces (TNI). The results indicate that strengthening defense infrastructure, including the addition of warships, patrol vessels, and modern fighter aircraft, is urgently needed to maintain regional stability. Furthermore, the application of the smart defense concept, integrating advanced technology with local community empowerment, can sustainably enhance maritime resilience. Investment in the defense sector has been shown to create a conducive climate for international trade, boost investor confidence, and strengthen Indonesia’s economic connectivity with the Asia-Pacific region. This study recommends synergy between the Ministry of Defense, TNI, and the national defense industry to accelerate the modernization and maintenance of alutsista, while ensuring the security of trade routes in North Sulawesi. With these measures, North Sulawesi can function optimally as a strategic import gateway that supports national economic growth and reinforces state defense.

This study was conducted to address contamination risks in Refill Drinking Water Depot (DAMIU) operations when sanitation, equipment maintenance, and quality control are not consistently implemented. This emphasizes the critical importance of systematic risk monitoring and review processes to ensure water quality and safety. The research aims to analyze the implementation of risk monitoring procedures, identify operational risks at each processing stage, and evaluate the effectiveness of risk mitigation strategies at Depot Isi Ulang Air Galon Barokah. A descriptive qualitative approach was employed, utilizing data collection techniques including in-depth interviews, direct field observations, and documentation analysis. The findings reveal that monitoring and risk review processes are conducted routinely, encompassing raw water quality checks, equipment sanitation protocols, and customer gallon container inspections. No significant risks were identified during the study period, as potential hazards such as microbiological contamination and equipment malfunction were effectively prevented through timely filter replacement and daily equipment cleaning procedures. Overall, the depot's risk management system operates effectively and consistently maintains water quality standards, ensuring product safety for consumers.

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.

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.

Community service activities in traditional fish ponds on Tibi Island, Tarakan, aim to increase the productivity of fish and shrimp farming using a simple yet effective approach. The methods applied include manual pond maintenance, such as spreading molasses solution (EM-4) to improve water quality, removing pests such as snails and oysters, and routine net maintenance and water drainage. Feed management is carried out every three days to maintain the nutritional balance of fish and shrimp. Although without the use of sophisticated technology, the success of cultivation depends heavily on the experience and thoroughness of the farmers in managing the pond environment. Overall, this activity demonstrates that a simple and structured approach can support sustainable cultivation, increase productivity, and strengthen collaboration between the implementation team and the pond owners. It is hoped that this method can serve as a useful example for other traditional fish farmers in increasing aquaculture yields and supporting more efficient and environmentally friendly farming practices.

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 problem with a 4-stroke gasoline engine-driven electric generator is a decrease in tool performance due to wear on important components for the stator, cooling fan, air filter, oil filter, and gasket. The purpose of component replacement planning is to obtain replacement costs, maintenance schedules in 2027, and the ratio of maintenance costs to profits. The component replacement planning method includes collecting maintenance data from previous years, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, predicting component lifespan, predicting labor costs, predicting supporting equipment to be used in maintenance, predicting spare part replacement times, predicting maintenance costs in 2027, and calculating the ratio of maintenance costs to profits. The results of the replacement planning obtained maintenance costs in 2027 amounting to IDR 570,007,- with an estimated electric generator rental rate of IDR 30,000,-/hour which has the potential to be rented for 128 hours/year, a profit of IDR 3,840,000,- was obtained, and the ratio of maintenance costs to profits was 14.84% which implies that a 2.5 kW electric generator that uses gasoline-pertalite fuel of around 1.5 liters/hour at maximum power is still suitable for use in the next few years and has the potential to generate profits.

Heritage language loss among immigrant and diaspora communities represents a critical challenge to cultural identity, intergenerational communication, and cognitive diversity. Traditional heritage language maintenance approaches face significant barriers including limited resources, lack of qualified instructors, geographic dispersion, and competing demands of dominant language acquisition. This study investigates the effectiveness of bilingual digital story applications as innovative tools for heritage language maintenance among children aged 4-12 years. Through a 24-month longitudinal mixed-methods study involving 1,843 families across eight language communities (Spanish, Mandarin, Arabic, Korean, Tagalog, Vietnamese, Hindi, and Polish), we examined language proficiency development, cultural identity formation, family engagement patterns, and app usage behaviors. Quantitative analysis of pre- and post-intervention language assessments revealed statistically significant improvements in heritage language vocabulary (effect size d=0.68), listening comprehension (d=0.54), and oral production (d=0.47) among children using bilingual story apps for at least 20 minutes daily. Qualitative findings from parent interviews and child focus groups highlighted the apps' role in making heritage language learning enjoyable, facilitating parent-child interaction, connecting children to cultural narratives, and normalizing bilingualism. However, effectiveness varied substantially based on app design features, with interactive elements, culturally authentic content, parent involvement scaffolds, and adaptive difficulty showing strongest associations with outcomes. This research demonstrates that thoughtfully designed bilingual story apps can serve as accessible, scalable tools for heritage language maintenance, though they function most effectively as complements to rather than substitutes for rich home language environments and community connections. The study contributes empirical evidence to inform app development, family language planning, and policies supporting linguistic diversity in increasingly globalized societies.

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.

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.

Unexpected downtime of a 2 kg/hour coffee grinder is crucial in cafe operations, thus less guaranteeing the availability of the grinder. The purpose of component replacement and repair planning is to obtain a prediction of the maintenance and repair schedule and costs in the 2026 period. The component replacement planning method includes collecting previous maintenance and repair data, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, predicting component life, predicting technician costs, predicting supporting work equipment and supporting materials to be used in maintenance, predicting the time to replace spare parts or reinstall components after repair, estimating maintenance and repair costs for the 2026 period, and calculating the ratio of maintenance costs to profits. The results of component replacement and repair planning obtained maintenance costs for the 2026 period are IDR 2,350,000, - with an estimated coffee grinder rental rate of IDR 25,000/hour which has the potential to be rented for 1440 hours/year, and the ratio of maintenance costs to profits is 6.5% which implies that the coffee grinder with a capacity of 2 kg / hour is still suitable for use for the next few years and still has the opportunity to make a profit.