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The rapid growth of Solar Power Plant (PLTS) installations in Indonesia faces efficiency challenges due to the continuously changing position of the sun. Fixed panel systems cannot absorb solar radiation optimally throughout the day. This study aims to evaluate and compare the efficiency of various solar tracking system methods developed over the last five years using a simple literature review approach. The research method was conducted by collecting, screening, and synthesizing secondary data from five reputable scientific journals using a synthesis matrix. The review results indicate that single-axis tracking systems increase power efficiency by 15% to 24.5%. Meanwhile, dual-axis systems achieve higher efficiency, ranging from 30% to 35%, by tracking both horizontal and vertical solar movements. In terms of control systems, astronomical algorithms are found to be more reliable in cloudy weather conditions than pure light sensors. However, the internal power consumption of the actuator motors remains a critical factor that can reduce the system's net energy gain. The implication of this study emphasizes the importance of shifting future research focus toward energy-saving algorithm optimization to maximize net power yield in dynamic solar panel implementations.

The increasing demand for electricity and high dependence on the utility power grid have driven the adoption of photovoltaic (PV) power generation as an environmentally friendly alternative energy source. This study aims to analyze the techno-economic feasibility of a load-sharing PV power generation system, focusing on technical analysis, operational costs, electricity bill savings, and environmental impacts. The research was conducted using quantitative methods to compare energy from photovoltaic generation and the grid, carbon emission calculations using an emission factor of 0,87 kgCO₂/kWh, and economic evaluation through parameters such as Net Present Value (NPV), Return on Investment (ROI), Cost of Energy (COE), and Payback Period (PP). The research results indicate that the photovoltaic panel generation system enables reducing utility grid energy consumption and reducing carbon emissions by 4,85 tons of CO₂ in 2024, increasing to 8,45 tons of CO₂ in 2025. The economic analysis indicates that the NPV value is not yet optimal; however, the ROI stands at 15%, the COE is Rp. 980.08/kWh, and the Payback Period is 6,56 years. The system is feasible for long-term investment. Overall, the implementation of the photovoltaic power plant demonstrates good potential in supporting carbon emission reduction, increasing the utilization of renewable energy, and reducing dependence on fossil-based energy.

Background: The rising global burden of hypertension presents a massive public health challenge, with its mitigation heavily tied to modifiable dietary factors. Among these, the escalating intake of sugar-sweetened beverages (SSBs) has emerged as a significant dietary risk. Objectives: This study aims to synthesize recent epidemiological evidence regarding the association between various types of sweetened beverages and the risk of hypertension. Methods: A narrative-analytical review was conducted by retrieving observational studies and meta-analyses published between 2023 and 2026 from the Scopus database using explicit keyword combinations targeting SSBs, prospective cohorts, and blood pressure outcomes. Findings: Synthesized data reveals that high consumption of traditional SSBs significantly increases hypertension risk by 13% (RR: 1.13) and elevates systolic blood pressure in children, primarily driven by excess liquid calories. Artificially sweetened beverages (ASBs) show a 14% risk increase in observational data, potentially confounded by reverse causation, whereas acute pure fructose ingestion triggers rapid transient blood pressure elevation. Energy drinks induce rapid 24-hour blood pressure spikes due to high caffeine content. Conversely, 100% pure fruit juice exerts a protective effect (RR: 0.74) via endothelial-improving polyphenols, while honey demonstrates a neutral effect. Implications: Public health policies must implement aggressive regulatory interventions, such as taxation on SSBs and energy drinks, alongside targeted dietary counseling that differentiates between harmful liquid sugars and protective natural fruit juices.

The development of electric vehicle technology has encouraged the use of electric bicycles as an environmentally friendly and energy-efficient mode of transportation. One of the main components of an electric bicycle is the hub BLDC (Brushless Direct Current) motor, which is considered more efficient because it is directly integrated into the wheel without requiring a chain transmission system. However, the performance of the hub BLDC motor is affected by operating conditions, particularly variations in load and speed, which influence current consumption and overall electrical performance. This study aimed to analyze the effect of load and speed variations on the current consumption of a hub BLDC motor. A quantitative experimental method was employed by conducting direct tests on an electric bicycle using load variations of 50 kg, 60 kg, and 70 kg, as well as speed variations of 30 km/h, 35 km/h, and 40 km/h. Current measurements were carried out in real time using a PZEM 051 sensor, and the collected data were analyzed using average values obtained from repeated measurements. The results showed that increasing the load and speed led to higher current consumption of the hub BLDC motor, from 4.6 A to 5.0 A under load variation and from 4.5 A to 5.1 A under speed variation. These findings indicate that both load and speed significantly affect the electrical current required by the hub BLDC motor, demonstrating that greater operating demands result in higher energy consumption.

Food and nutrition governance is a central issue in subnational development, especially in areas with high agricultural potential but persistent malnutrition. This study analyzes how Grobogan Regency, Indonesia, implements food and nutrition governance through its 2025–2029 Regional Action Plan for Food and Nutrition Based on Local Resource Potential (RAD-PGBPSDL). Using qualitative policy analysis, the research examines policy design, strategic priorities, coordination mechanisms, and indicator frameworks, supported by secondary data on food production, consumption, poverty, and nutrition. Findings show that Grobogan possesses strong local food assets, including surplus production, improved dietary quality, and diverse commodities. However, major governance challenges persist, such as high stunting rates, maternal energy deficiency, uneven food access, climate-related production risks, and fragmented cross-sector data systems. The policy demonstrates a shift toward multisectoral governance by integrating food availability, affordability, utilization, institutional coordination, climate resilience, and local food diversification. Implementation may be constrained by limited operational detail, uneven agency readiness, and insufficient monitoring and accountability. The study highlights that effective food and nutrition governance requires institutional coordination, evidence-based planning, and adaptive public management. The Grobogan case provides practical lessons for strengthening subnational governance in Indonesia and similar developing contexts.

This study aims to analyze the effect of renewable energy, energy consumption, and Gross Domestic Product (GDP) per capita on carbon dioxide (CO2) emissions in Indonesia for the period 1995-2024. This study uses secondary data over time (time series) with the Ordinary Least Square (OLS) multiple linear regression analysis method corrected using the Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) approach. The results show that renewable energy does not have a significant effect on CO2 emissions, which is caused by the still low share of renewable energy in the national energy mix which only reaches 10.95% in 2024. Energy consumption has a positive and significant effect on CO2 emissions, where every 1% increase in energy consumption increases CO2 emissions by 84.23%. Gross Domestic Product (GDP) per capita has a positive and significant effect on CO2 emissions. Every 1% increase in GDP per capita increases CO2 emissions by 35.03%, indicating that Indonesia remains on the EKC curve. Simultaneously, all three variables have a significant effect, with an adjusted R-squared value of 53.63%. This finding confirms that Indonesia's energy mix, still dominated by fossil fuels, is a major factor in high carbon emissions. Comprehensive energy efficiency policies, accelerated renewable energy transitions, and greener and more sustainable economic growth strategies are needed.

This study examines changes in nutritional knowledge, dietary patterns, nutrient intake, and food acculturation among first-year migrant and non-migrant students. A 5 months prospective cohort design was employed involving 32 students from the Faculty of Public Health, Universitas Airlangga. Students were randomly divided equally into migrant and non-migrant groups. Data were collected using questionnaires, food frequency questionnaires, 3×24-hour food recall, and analyzed using descriptive and inferential statistics. The findings indicated no significant differences or changes in nutritional knowledge between groups throughout the observation period (p > 0.05). However, dietary patterns varied, with migrant students showing increased consumption of practical and fast foods. Nutrient intake, particularly energy and protein, was initially lower among migrant students but improved significantly over time, reflecting adaptation to a new environment. Food acculturation was evident among migrant students, with a significant increase in scores during the study period (p = 0.007), indicating gradual adjustment to local eating habits. These results highlight the influence of environmental adaptation on students’ dietary behavior and emphasize the need for targeted nutrition interventions to promote healthy eating habits during the early university transition.

The global consumption of energy drinks is currently faced with a paradox between the demand for instant performance enhancement and concerns regarding health risks. This study aims to analyze the strategic role of brand image in stimulating consumer purchase intention amidst this perceptual dilemma. Employing a qualitative approach through a literature review method, this research synthesizes findings from nine empirical studies published between 2019 and 2024. The analysis reveals that brand image consistently exerts a positive and significant influence on the escalation of purchase intention. Within a marketing framework, brand image functions as a risk mitigation instrument and a signal of trust capable of constructing positive consumer attitudes. These findings validate the Theory of Planned Behavior (TPB), where a strong brand identity triggers the psychological drive to perform a purchase. Practically, this study concludes that the effectiveness of energy drink marketing strategies highly depends on building an authentic and responsible brand image to foster public trust and loyalty amidst intense industrial competition.

This study analyzes an innovative, energy-efficient chicken egg incubator made from Styrofoam waste, developed within the 2024 Appropriate Technology Competition Program in Sidoarjo Regency. The aim of this study was to explore the diffusion of this energy-efficient Styrofoam-based chicken incubator, its background, benefits, process, and challenges and supporting factors in its implementation. A qualitative case study method was used, involving structured interviews with the innovation owner, the tool user, and the competition organizer, direct observation, and document analysis. The results indicate that the innovation is effective in reducing electricity consumption, lowering production costs, and increasing hatching rates by 80–100%. The incubator is easy to use, affordable, and environmentally friendly. It also contributes to community empowerment by supporting small-scale poultry farming. Despite initial public skepticism regarding the innovation as a “Misguided Sect” and limited institutional support, it’s practicality. and efficiency have led to its widespread acceptance. The innovation demonstrates the potential of local, low-cost solutions to promote sustainable rural development.

Electricity dependence is increasing as people have easier access to electronic devices. However, this ease of access is not always accompanied by adequate energy literacy. In its Community Service program, Universitas 17 Agustus 1945 Jakarta helped to educate the residents in RW06 Kalibaru, Jakarta, about the safe and efficient use of electricity. This community service program was conducted through participatory seminar with a qualitative approach for residents. The activity used informal visual presentations and discussions in an informal atmosphere. The outreach program demonstrated residents' high enthusiasm for the efficiency of electrical equipment usage such as air conditioners, refrigerators, and water pumps. Through the discussion session, residents realized that using air conditioners with too low a power consumption actually leads to higher energy consumption because the compressor runs continuously. Participants understood that investing in automation concepts, such as using water reservoirs, provides significant long-term electricity cost savings. This outreach successfully shifted residents' paradigm from just simply seeking low prices to selecting devices with optimal capacity according to their needs. This education program demonstrates the effectiveness of energy literacy in improving electricity consumption habits at the household level. Therefore, simple technical understanding can boost family economic financial advantages through sustainable effective energy consumptions.

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

This study examines the extent to which West Java Province has achieved Sustainable Development Goal (SDG) 7 on clean and affordable energy in the electricity sector. The study uses a qualitative method with a case study approach to evaluate policies and achievements in terms of energy access, renewable energy use, energy efficiency, and the dynamics of cooperation between government agencies. The results show that the electrification rate in West Java has almost reached 100% thanks to government policies such as the free electricity program for underprivileged communities. However, the share of renewable energy in the province was still around 15% in 2022, which has not yet reached the target of 17% by 2025. Furthermore, energy efficiency is also an important issue because primary energy consumption in West Java increased in 2022. This study emphasizes the need to enhance inter-agency cooperation, innovation in local policies, and political commitment to achieve SDG 7 targets in line with national directives.

The commercial sector, especially the hospitality industry, is one of the largest consumers of electrical energy, with energy costs often ranking as the second highest operational expense. This study aims to conduct a specific Electrical Energy Audit in the Office Engineering unit of Aston Denpasar Hotel & Convention Center to optimize electricity usage and improve energy efficiency. The research applies a detailed audit approach with a focus on lighting systems and air conditioning (AC), which are major contributors to energy consumption. The initial stage involves calculating the actual Energy Consumption Intensity (IKE) in kWh/m²/month and comparing the results with ASEAN and SNI standards to determine the efficiency classification of the building. Data collection is carried out through direct field measurements as primary data, using instruments such as a Clamp Meter and Lux Meter. The expected outcome of this study is the identification of detailed Energy Saving Opportunities (ESO), along with the estimation of potential monthly energy cost savings and the calculation of the investment Payback Period.

Increasing operational efficiency in power generation systems is crucial in overcoming the increasing energy demand, especially in the industrial sector. One solution to improve the efficiency is to optimize the use of economizer in the boiler of Steam Power Plant (PLTU). This study analyzes the impact of using an economizer in a 240 ton/hour capacity CFB boiler at PT X on system efficiency and fuel consumption. The results of the analysis show that the use of an economizer can increase the average feed water temperature from 207.73°C to 298.96°C, with a temprature increase of 91.23°C. This temperature increase reflects the economizer's ability to utilize flue gas heat to heat the feed water, which has an impact on reducing fuel consumption by 17.18%, from 45716.56 kg/hour to 37862.50 kg/hour. The boiler efficiency also increased significantly, from 68.36% to 82.54%, which shows the positive impact of the economizer on boiler performance. This study concludes that the use of economizer can improve energy efficiency and reduce fuel consumption, and is recommended to be applied to large capacity boiler systems to optimize energy savings and operational efficiency. Further research is needed to explore the influence of other variables on economizer and boiler performance in more depth.

The increasing consumption of packaged food products in Indonesia reflects modern lifestyle changes but simultaneously raises public health concerns related to high calorie, sugar, and fat intake. Nutritional information presented on food labels consists of multiple interrelated variables, making it difficult to identify dominant nutritional factors that characterize packaged food products. This study aims to apply Principal Component Analysis (PCA) to reduce the dimensionality of nutritional data and to map the nutritional characteristics of packaged food products in Indonesia. The research employs a quantitative exploratory approach using secondary data obtained from nutrition facts labels of 1,651 packaged food products. Seven nutritional variables were initially analyzed, namely total energy, protein, total fat, total carbohydrates, sugar, sodium, and dietary fiber. Data preprocessing included data cleaning, Z-score standardization, and iterative variable selection based on the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity to ensure sampling adequacy and sufficient correlation among variables. Variables with low sampling adequacy and perfect multicollinearity were eliminated, resulting in five variables retained for the final PCA model. Principal components were extracted using the eigenvalue greater than one criterion and confirmed through a scree plot, followed by Varimax rotation to enhance interpretability. The results indicate the formation of two principal components explaining approximately 69.7% of the total variance. The first component represents energy density and macronutrient richness, while the second component reflects carbohydrate-related characteristics, particularly the contrasting pattern between sugar and dietary fiber. Biplot visualization further illustrates product distribution based on these components. The findings demonstrate that PCA effectively simplifies complex nutritional information and provides a clear nutritional mapping of packaged food products, offering practical insights for consumers, producers, and policymakers in supporting healthier food choices in Indonesia.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.