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This research develops an automated temperature control system for water heaters, that is both efficient and stable, driven by the need for energy-saving heating solutions, particularly for marine applications. The main objective was to create an automatic system capable of maintaining water temperature within an optimal range while maximizing energy efficiency through the use of thermal storage materials. The methodology involved an on-off control system based on a microcontroller as the main controller, capable of processing temperature sensor data in real time. This system intelligently activates and deactivates the heater to keep the water temperature stable. Paraffin wax was used as a latent heat storage medium, playing a crucial role in gradually storing and releasing thermal energy to support temperature stability. An automatic water heater system based on the ESP32 microcontroller with an on-off control mechanism was successfully designed using paraffin wax combined with silica sand as a thermal storage medium. The addition of silica sand significantly enhanced heat conductivity and temperature stability. Test results showed a substantial reduction in energy consumption, with daily savings reaching Rp12,762.88, equivalent to 67% of total daily energy costs. Over a one-year period, the total savings amounted to Rp4,658,103.20, demonstrating that the paraffin wax–silica sand combination is highly effective in improving energy efficiency and reducing long-term operational costs.

This study aims to analyze the thermal performance of the heating system in a small-scale plastic bolt molding machine using LDPE material, in order to determine process parameters that are efficient while maintaining product quality. The method used includes experimental testing at two set-point temperatures (90 °C and 120 °C), measurement of melting time and feed mass per cycle, as well as heat balance calculations separating the contributions of conduction, convection, and radiation on the barrel heated by a band heater. In addition, the power/energy requirement per cycle and productivity projections based on hopper capacity were calculated. The results show that increasing the set-point from 90 °C to 120 °C accelerates melting from ±240 s to ±180 s (≈25% faster). Heat transfer analysis confirmed the dominance of conduction (≈329.7 W at 90 °C and ≈471 W at 120 °C), while convection and radiation contributions were much smaller; the total system heat rate was ≈342.7 W (90 °C) and ≈490.8 W (120 °C). The discussion highlights the process trade-off: higher set-points increase production rate and mold filling quality (due to lower melt viscosity), but may raise energy consumption per cycle and require tighter mold temperature control to limit shrinkage/warpage. The practical implications for SMEs are the need for efficiency strategies based on barrel insulation, heater contact area optimization, and correlation of temperature-time settings with quality and energy consumption targets. This study concludes that controlled temperature and heating duration, supported by simple yet targeted thermal design, can improve cycle time consistency, dimensional precision, and energy efficiency in small-scale plastic bolt molding machines.

This article examines the economic defense reforms implemented by China under Xi Jinping's leadership through a literature review. This research aims to examine the key strategies employed by the Chinese government to strengthen its economic resilience amidst global pressures, such as the trade war with the United States, the COVID-19 pandemic, and escalating geopolitical rivalries. The findings indicate that Xi Jinping prioritizes a dual-circulation strategy, namely strengthening domestic consumption while maintaining connectivity with international markets, as a crucial step in addressing global uncertainty. Furthermore, the Made in China 2025 policy serves as a key pillar for promoting technological independence, reducing dependence on imports, and enhancing the competitiveness of domestic industries in strategic sectors such as information technology, renewable energy, and artificial intelligence. Furthermore, through the Belt and Road Initiative (BRI), China seeks to expand its economic influence globally, open new markets, and transfer excess domestic industrial capacity to partner countries. These reforms are not solely oriented toward economic aspects but are also closely linked to political and national security interests. This strategy demonstrates China's systematic efforts to build economic resilience integrated with geopolitical interests, using the economy as an instrument of both soft and hard power. This study demonstrates that Xi Jinping's economic defense reforms are not merely a short-term response to the global crisis, but rather part of a long-term vision to establish China as a dominant global power. Thus, this study emphasizes the importance of understanding China's economic strategy holistically, as these reforms, in addition to significantly influencing domestic dynamics, also have significant implications for the future global economic and political order.

Wireless Medical Sensor Networks (WMSNs) are a key component of modern Healthcare Internet of Things (IoT) systems, enabling continuous and real-time monitoring of patients’ physiological parameters. These networks support timely medical intervention, improve patient outcomes, and facilitate remote healthcare delivery. However, due to the open and resource-constrained nature of WMSNs, they are highly susceptible to various security threats, particularly during the authentication phase. Existing authentication protocols have been found vulnerable to a range of attacks, including impersonation, session key disclosure, and gateway database compromise, which can lead to severe privacy breaches and potentially life-threatening situations. To address these issues, this paper proposes a secure and lightweight three-factor authentication protocol tailored for WMSNs in healthcare IoT environments. The proposed protocol integrates Elliptic Curve Cryptography (ECC) for strong public key-based security with minimal computational overhead, fuzzy extractors to securely handle biometric information and ensure resistance against biometric template compromise, and session-based randomness to achieve forward secrecy and prevent replay or key-compromise impersonation attacks. Security analysis demonstrates that the proposed protocol successfully mitigates prominent threats such as impersonation attacks, man-in-the-middle attacks, session key leakage, and database compromise. In addition, the protocol ensures mutual authentication between the user, the gateway, and the sensor nodes, while maintaining data confidentiality and integrity. Performance evaluation indicates that the protocol offers significantly reduced computational cost and communication delay compared to existing schemes. Its low energy consumption and minimal storage requirements make it suitable for deployment in resource-constrained medical devices and large-scale IoT healthcare networks. The results highlight the protocol’s scalability, energy efficiency, and robustness, making it a practical and secure solution for safeguarding patient data and ensuring trustworthy communication in WMSNs-based healthcare IoT systems.

The rapid development of the smart city concept encourages the need for energy management that is more efficient, sustainable and adaptive to the needs of modern urban communities. In this context, renewable energy is the main solution to reduce dependence on fossil energy sources that are limited and pollute the environment. This research aims to optimize the utilization of renewable energy in smart cities by integrating Big Data technology and Decision Support Systems (DSS). The approach used in this research is a case study and system modeling method, which involves collecting energy data from various sources such as IoT sensors, weather stations, and energy distribution systems in real-time. The data is then analyzed using Big Data Analytics techniques to identify energy consumption patterns, potential renewable energy production, and peak load predictions. Furthermore, a decision support system was designed to assist policy makers and city managers in determining optimal energy distribution and usage strategies based on the available data and simulations. The results show that the integration of Big Data and DSS is able to increase the efficiency of renewable energy utilization up to 25% compared to conventional systems. In addition, the system is also able to dynamically respond to changing conditions and provide more accurate and adaptive decision recommendations. These findings indicate that the synergy between data technology and decision support systems plays a strategic role in creating sustainable and environmentally sound smart cities.

This community service project aims to improve the quality of street lighting around the Waringin Tunggal Studio, located in Soneyan Village, Pati Regency. As part of efforts to improve community comfort and safety, this project focuses on addressing the issue of inadequate lighting that can impact community activities, particularly at night. Inadequate lighting around the studio creates discomfort and reduces the sense of security for both studio users and the local community. The project began with a thorough inspection of the lighting conditions around the studio. The next step was to design technical solutions to improve the existing lighting system, including increasing the number of lamps and using more efficient types of lamps in terms of energy consumption and light distribution. Furthermore, good collaboration with village officials and other relevant parties was crucial for the smooth implementation of this project. The improvement process was carried out with a focus on energy efficiency and durability, ensuring long-term benefits. The results of this project showed a significant improvement in the quality and coverage of lighting around the Waringin Tunggal Studio. This improvement not only increased comfort but also increased the sense of security for studio visitors and the surrounding community. Access to the studio at night has become easier and safer, positively impacting the arts and cultural activities held there. A post-event evaluation showed increased community participation in the studio's evening events. This success demonstrates that improved lighting can encourage community participation in community activities. Going forward, it is hoped that this activity can be enhanced by utilizing renewable energy, such as solar power, to ensure optimal lighting even during power outages, thus ensuring the desirability of this activity in the future.

Electricity has emerged as an essential requirement in modern life. As demand escalates, electricity costs rise, making wastefulness a drain on financial resources. Consequently, forecasting electricity usage can enhance our management of consumption. This study presents an IoT-based monitoring and forecasting system for electricity consumption. The system comprises two NodeMCU micro-controllers, a PZEM-004T sensor for collecting real-time power data, and three relays that regulate the current flow to three distinct electrical appliances. The data gathered is transmitted to a web application utilizing the k-Nearest Neighbor (k-NN) algorithm to forecast future electricity usage based on historical patterns. We evaluated the system's performance using four weeks of electricity consumption data. The results indicated that predictions were most accurate when the user’s daily consumption pattern remained stable, achieving a Mean Absolute Error (MAE) of approximately 1 watt and a Mean Absolute Percentage Error (MAPE) ranging from 1% to 1.7%. Additionally, predictions were notably precise during the early morning hours (3:00 AM to 8:00 AM) when k=6 was employed. This study demonstrates the effectiveness of integrating IoT-based systems with machine learning for real-time energy monitoring and forecasting. Furthermore, it emphasizes the application of data mining techniques within embedded IoT environments, providing valuable insights into the implementation of lightweight machine learning for smart energy systems.

The number of school-age adolescent girls in Banggai Regency, Central Sulawesi, still faces serious challenges related to nutritional problems such as anemia and chronic energy deficiency (SEZ). Low nutritional literacy and unbalanced food consumption habits also worsen their nutritional status, especially during puberty when nutritional needs increase significantly. This service activity aims to increase the understanding and awareness of young women on the principles of balanced nutrition through a participatory educational approach. The implementation of the activity was carried out at SMP Negeri 2 Bunta, Simpang Raya District, involving 30 students in grades VII and VIII as well as UKS supervisory teachers. The intervention included providing material on "Fill My Plate", the 4 pillars of balanced nutrition, and the risk of nutritional disorders in adolescents. The material was delivered in the form of interactive counseling using leaflet media, accompanied by knowledge measurement through pre-test, post-test, and one-week post-activity evaluation. The results of the activity showed a significant increase in students' understanding of the principles of balanced nutrition, with the knowledge score of the good category increasing from 40% to 87%. Most of the participants were able to mention the composition of the meal portion, the importance of breakfast, restrictions on fast food consumption, and the need to drink enough water every day. This activity also has an impact on increasing the capacity of teachers in assisting and monitoring the health status of students through the UKS program. Teachers become more active in conducting periodic counseling and recording the nutritional development of female students on a regular basis. School-based balanced nutrition counseling has been proven to be effective in improving adolescent nutritional literacy. This educational model can be replicated in other regions with cross-sectoral support such as education offices, health centers, and village governments, to support the achievement of sustainable development goals in the health sector.

This community service activity aims to provide education and technical recommendations related to energy efficiency and thermal comfort in the BIN Apartment building through simulations using DesignBuilder software. The purpose of the activity is to analyze and optimize the building’s energy consumption and ensure thermal comfort for the residents. The simulation results show that the building's Energy Use Intensity (EUI) is relatively low for the residential category, based on calculations made by the BGH team. The air conditioning (AC) system in conditioned spaces meets the thermal comfort standards according to ASHRAE, ensuring comfortable living conditions in these areas. However, in unconditioned areas, such as the family room, the Predicted Mean Vote (PMV) value reaches 1.5 (hot), indicating thermal discomfort. To address this issue, mechanical ventilation is recommended, with the capacity to produce an average air velocity of at least 0.6 m/s, which will improve airflow and enhance thermal comfort. Alternatively, adding an additional AC unit can also be considered, though it would result in a 20% increase in energy consumption. The findings and recommendations of this activity are expected to provide valuable insights and assist partners in making informed technical decisions when designing efficient and comfortable HVAC systems for vertical state-owned residential buildings. Ultimately, this program aims to optimize both energy usage and comfort, contributing to sustainable building management practices in the future.

Instant noodles are a widely consumed fast food product, and consumer understanding of their nutritional content and potential health risks is thought to influence both attitudes and behaviors related to their consumption. Specifically, this understanding may affect how individuals meet their energy requirements—measured by the Recommended Energy Intake (REI)—through the consumption of instant noodles. This study aims to examine the relationship between knowledge and attitudes and the consumption practices of instant noodles among consumers at Warmindo K17 in Surabaya. A quantitative, cross-sectional research design was employed. Data were collected using a structured questionnaire distributed to 100 respondents selected through simple random sampling. Descriptive statistics, Spearman’s rank correlation, and Fisher’s exact tests were utilized to analyze the relationships between knowledge, attitudes, and consumption practices. The results indicated a statistically significant association between knowledge levels and consumption practices (p = 0.030), as well as between attitudes and consumption practices (p = 0.006), with both p-values falling below the 0.05 threshold. These findings suggest that higher levels of nutritional knowledge and more positive attitudes are associated with healthier instant noodle consumption practices. The study underscores the importance of enhancing public nutrition education to encourage healthier dietary behaviors. Furthermore, the findings may offer valuable insights for Warmindo business operators in developing more effective strategies for communicating product information to consumers.

This study aims to analyze the electrical energy consumption of a bench-type drilling machine, RYU RDB 13, by varying machining parameters in the form of spindle speed and feed rate. Electrical energy is a critical aspect of production efficiency in the manufacturing industry, particularly in machining processes that require continuous power consumption throughout the cutting operation. The material used in this study is aluminum 6061, chosen for its lightweight, corrosion resistance, and wide application in the automotive and aerospace industries. The drilling process was carried out using three spindle speed variations: 620 rpm, 920 rpm, and 1280 rpm, along with three feed rate variations: 0.04 mm/rev, 0.08 mm/rev, and 0.1 mm/rev. Current and voltage were measured using a digital wattmeter in real-time, and energy consumption was calculated in wattseconds (Ws) using power calculation formulas. The results indicate that increasing the feed rate and spindle speed leads to higher instantaneous power consumption. However, total energy consumption tends to decrease at higher speed and feed combinations due to shorter machining times. The optimal parameter combination was found at a feed rate of 0.1 mm/rev and a spindle speed of 1280 rpm, which resulted in the lowest energy consumption of 387 Ws and the fastest drilling time. This demonstrates that selecting the right machining parameters not only improves energy efficiency but also maintains or enhances productivity. The observed power consumption pattern typically shows a sharp increase at the beginning of the drilling process, a stable phase during the main cutting stage, and a rapid decrease towards the end of the cut. These findings contribute to a better understanding of the relationship between machining parameters and energy efficiency, serving as a basis for developing sustainable production strategies in the manufacturing sector that prioritize energy savings and cost reduction.

The rapid growth of motor vehicles, exceeding 169 million units in Indonesia by 2025, highlights the urgent need for energy-efficient and environmentally friendly transportation solutions, such as hybrid vehicles. Brushless Direct Current (BLDC) motors are crucial components in these systems due to their high efficiency and compact design, particularly when directly integrated into wheels. This study specifically aims to analyze the impact of varying loads on the power consumption of a BLDC motor within a front-wheel-drive hybrid motorcycle prototype equipped with a Continuously Variable Transmission (CVT) system. Experimental data was collected using a wattmeter on a prototype subjected to different passenger loads: 55 kg, 75 kg, and 100 kg, at constant speeds of 20 and 40 km/h. The findings consistently show that an increase in load significantly and positively correlates with higher power consumption of the BLDC motor. For instance, at 40 km/h, power consumption for a 100 kg load reached 729.578 Watt, whereas for a 55 kg load it was 649.605 Watt. This demonstrates that greater effort (power) is required from the motor to overcome the inertia and increased resistance associated with heavier loads. These results contribute to optimizing energy efficiency in hybrid vehicle design and underscore the importance of load management for sustainable transportation.

Indonesia, as the world's largest archipelagic nation, holds a strategic position while simultaneously facing significant challenges in maintaining the sovereignty and security of its maritime territory. Increasingly complex maritime threats, such as illegal fishing, smuggling, and potential geopolitical conflicts, demand a transformation in maritime defense strategies to be more efficient and sustainable. In an era of increasing global awareness of the climate crisis, the maritime sector, including the defense sector, is being encouraged to decarbonize through the implementation of environmentally friendly ship technology. This research uses a literature review approach by examining various findings related to sustainable propulsion technology innovations relevant to the development of Indonesia's maritime defense fleet. The study results show that a hydrogen- and battery-based hybrid propulsion system can reduce carbon emissions by up to 73% and increase energy efficiency by 35%. In addition, wind-assisted propulsion sistem (WAPS) technology such as Flettner rotors and wing-sails contributes significantly to reducing fuel consumption by up to 30%, while extending ship cruising range without the need for intensive refueling. Meanwhile, the application of Computational Fluid Dynamics (CFD)-based propeller design optimization has been proven to reduce energy consumption by 13.2% and reduce noise levels by up to 15 dB, which greatly supports the needs of stealth operations in military missions. This study concludes that the implementation of environmentally friendly ship technology not only provides benefits in terms of energy efficiency and emission reduction, but also strengthens the operational resilience and strategic competitiveness of the Indonesian naval fleet amidst the dynamics of global maritime security. Thus, the Green Navy concept can be seen as a relevant and urgently needed sustainable defense strategy.

This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effectiveness of this framework in real-world smart city applications such as traffic monitoring, environmental sensing, and smart utilities management. The proposed method integrates lightweight AI models, edge computing, and adaptive resource management techniques, including Federated Learning and Neural Architecture Search, to ensure optimal performance while addressing hardware constraints. The main findings reveal that the framework significantly improves real-time inference speed, reduces energy consumption of IoT devices, and enhances CPS reliability by minimizing communication delays and ensuring continuous system operation despite network disruptions. The application of this framework to smart transportation and urban utilities further demonstrates its potential to optimize city management processes. The study concludes that the Adaptive Edge-AI framework offers a promising solution for smart cities, enhancing operational efficiency, sustainability, and resilience. It is recommended for integration into smart city infrastructures to enable better resource management and decision-making in real-time applications.

This study investigates the impact of tail configuration variations on flight stability and battery energy efficiency in Unmanned Aerial Vehicles (UAVs). Three distinct tail types were tested: the conventional tail, the T-Tail, and the V-Tail. The objective was to compare how these tail designs affected the overall performance of UAVs, especially focusing on the balance between stability and energy efficiency. The experiments were conducted through a series of flight tests in a controlled outdoor environment, providing reliable and accurate data. During these flight tests, several performance metrics were recorded, including IMU-based angular deviations for pitch, roll, and yaw, energy consumption, flight time, voltage, and battery power. The data collected allowed for a thorough analysis of how the tail design influenced the flight characteristics of the UAVs. The results showed that the T-Tail configuration provided the highest flight stability, as indicated by the smallest angular deviations and minimal vibration during flight. This design’s enhanced stability made it the most reliable, especially for missions requiring precision control. In contrast, the V-Tail configuration proved to be the most energy-efficient, consuming only 22.80 Wh. Despite its low energy consumption, the V-Tail showed the lowest stability due to control coupling between the pitch and yaw axes, resulting in higher angular deviations and less precise control. The conventional tail, while not the best in terms of either stability or energy efficiency, struck a reasonable balance between the two. This configuration provided adequate stability while ensuring efficient battery usage, making it a suitable choice for general UAV applications. The findings of this study highlight the direct influence of tail design on UAV performance. There is a clear trade-off between flight stability and energy efficiency, with the conventional tail offering the best compromise.

Carbon emissions in Indonesia continue to increase in line with the growing energy consumption needed to meet public demands. Energy sector companies, which contribute significantly to carbon emissions, are expected to take responsibility for their operational activities. One form of accountability is through carbon emission disclosure as a means of transparency to stakeholders. This study aims to examine the impact of institutional ownership, managerial ownership, and foreign ownership on carbon emission disclosure. The research objects are energy sector companies listed on the Indonesia Stock Exchange (IDX) for the 2021–2023 period. The study utilizes secondary data derived from annual reports, with a sample of 35 companies and 95 observations in total. Hypothesis testing was conducted using the t-test. The results indicate that institutional ownership and foreign ownership have a positive effect on carbon emission disclosure, while managerial ownership has a negative effect on carbon emission disclosure.

Carbon emission disclosure is increasingly important for companies in establishing legitimacy, enhancing stakeholder trust, and drawing public attention to sustainability issues. This study aims to examine how profitability, leverage, and company size affect carbon emission disclosure. The research is grounded in legitimacy theory and stakeholder theory, which provide the theoretical foundation for understanding corporate carbon disclosure behavior. The population of this study comprises energy companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2023 period. The sample was selected using non-probability purposive sampling, resulting in 113 observations. Data were analyzed using SPSS with multiple linear regression techniques. The results indicate that profitability and company size have a positive influence on carbon emission disclosure, while leverage does not have a significant effect. Theoretically, the findings support the notion that profitability and company size drive carbon disclosure, in line with legitimacy and stakeholder theories.

Internal combustion engines generate thermal energy through the combustion of an air-fuel mixture. The incoming air must be filtered, as it contains impurities and particles. While air filters are commonly made from paper, they can also be developed using organic waste materials such as sugarcane bagasse and rice husk. This study aims to analyze the effect of biocomposite air filters on engine power, torque, and specific fuel consumption (SFC), by varying the material composition and the number of pleats, and comparing the results with those of a standard air filter. The method used is a quantitative experimental approach involving dyno testing on a 150 cc motorcycle engine. The tested material compositions include 50:50, 40:60, 30:70, 60:40, and 70:30 (sugarcane bagasse\:rice husk), with pleat counts of 28, 26, and 24. Results showed that the biocomposite filter with 28 pleats and a 30:70 composition delivered the best performance, producing 18.47 Hp and 16.66 Nm of torque—higher than the standard filter’s 17.98 Hp and 15.52 Nm. In terms of fuel efficiency, the 26-pleat filter with a 50:50 composition recorded the lowest SFC at 41.90 g/kWh, significantly better than the standard filter’s 53.03 g/kWh. Conversely, the 60:40 composition resulted in the highest SFC due to increased airflow resistance.

Energy efficiency in air conditioning systems is a critical factor in reducing energy consumption and environmental impact. This study aims to analyze the energy performance of an air conditioning system using the thermodynamic cycle method. The analysis focuses on the refrigeration cycle, particularly the vapor compression cycle commonly used in residential and commercial systems. Key parameters such as coefficient of performance (COP), refrigerant flow rate, and enthalpy at each cycle point were examined to determine overall efficiency. Data were collected through simulations and experimental measurements under standard operating conditions. The results show that optimizing system components, especially the compressor and expansion valve, can significantly improve energy efficiency. The COP increased by 12% when a high-efficiency compressor was utilized. In addition, the selection of eco-friendly refrigerants contributed to better thermal performance and reduced environmental risks. This research highlights the importance of thermodynamic analysis in designing and improving air conditioning systems for sustainable energy usage. Future studies are recommended to incorporate real-time monitoring and adaptive control systems to further enhance system performance and energy savings.

Background: The global energy transition requires low-carbon solutions that can be integrated into existing thermal systems without drastic infrastructure changes. Hydrogen blending in conventional combustion systems has emerged as a promising pathway to reduce carbon emissions while maintaining operational flexibility. Objective: This study aims to experimentally evaluate the effect of hydrogen blending ratios (0–100% by volume) on thermal efficiency, CO₂ emissions, and NOx emissions, and to determine the optimal blending range based on technical and economic feasibility. Methods: An experimental thermal system prototype was developed and tested under controlled conditions with three repetitions per operating point. Performance parameters included combustion temperature, fuel consumption rate, and thermal efficiency, while emissions of CO₂ and NOx were measured using a calibrated gas analyzer. Data were analyzed using descriptive statistics, one-way ANOVA at a 0.05 significance level, confidence interval estimation, and linear regression to examine the relationship between hydrogen fraction and emission reduction. Results: The findings indicate that increasing hydrogen fraction significantly improves thermal efficiency, reaching 87.5% at 100% hydrogen, while CO₂ emissions decrease linearly to zero. However, NOx emissions increase with higher hydrogen content due to elevated combustion temperatures. Statistical analysis confirms that hydrogen ratio has a significant effect on efficiency and emissions, with a strong linear correlation between hydrogen fraction and CO₂ reduction. A blending range of 40–60% hydrogen provides the most balanced performance in terms of efficiency improvement, emission reduction, and cost feasibility.