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The aim of this study is to design a battery charging system using solar panels, integrated with an IoT-based monitoring system to improve efficiency and ease of supervision. The system utilizes the photovoltaic effect to convert solar energy into electrical energy, which is then stored in a battery through the regulation of a solar charge controller. The main components in this design include a solar panel, lead-acid battery, ESP32 microcontroller, current and voltage sensors, and the Blynk application for remote monitoring. The research method involves several stages, including the design of hardware such as solar panels, current and voltage sensors, and a step-down regulator, as well as software development using the Blynk platform and ESP32 programming. Test results show that the system is capable of achieving charging efficiencies between 89.45% and 99.57%, with optimal performance under clear weather and maximum sunlight conditions. The IoT system performs well up to a distance of 10–15 meters, with an average data transmission delay of less than 1.5 seconds. Therefore, this device proves to be effective as an independent battery charging and energy monitoring solution in locations without permanent electricity access.

This study discusses the application of Internet of Things (IoT) technology in the monitoring and control system of smokehouse machines at PT Serena Harsa Utama. IoT enables the integration of sensors to monitor critical parameters such as temperature and humidity in real-time, thereby enhancing production efficiency and product quality. An experimental method was employed to test the effectiveness of the IoT system, with data collection through integrated sensors. The results indicate that IoT implementation can increase production efficiency by up to 30%, reduce human error, and optimize energy consumption. Despite challenges related to initial investment and operator training, the long-term benefits of IoT adoption, such as improved product quality and reduced downtime, make it a promising solution in the food processing industry. This research emphasizes the importance of adopting IoT technology to enhance competitiveness in the modern industry.

Background: Porang contain glucomannan. Glucomannan is a water-soluble fiber, and has low calories, so porang needs to utilize its nutritional value in food products. The addition of mung beans to flakes products will add nutritional value because they contain iron, vitamin C and zinc. Objective: The aim of the research was to determine the sensory value and chemical content (carbohydrate, protein, fat, water, ash, crude fiber and breakability) of porang flour flakes and mung bean flour. The research use experimental method with a completely randomized design (CRD) with 3 formulations, namely F1 (60%:40%), F2 (50%:50%), F3 (40%:60%). The parameters observed were sensory tests involving 50 panelists. Chemical content test methods include: carbohydrate by difference method, protein by Kjeldhal method, fat by Soxhlet method, content (water, ash and crude fiber) using the gravimetric method, breakability by tensile strength method. Sensory test data were analyzed using ANOVA and Chemical test data was quantitatively descriptively. Result: The F1 sensory test results showed a color value of 3.51, aroma 3.37, texture 3.33, taste 3.14. F2 shows a color value of 3.14, aroma 3.47, texture 3.46, taste 3.27. F3 shows a color value of 3.72, aroma 3.98, texture 3.76, taste 3.83. The average value of chemical test content (carbohydrates, protein, fat, water, ash, crude fiber and breakability) respectively is 74.85%, 9.35%, 4.76%, 6.64%, 4.39 %, 2.29%, and 12.16 N. Conclusion: Porang flour and mung bean flour have an influence on sensory tests (color, aroma, texture, taste) and chemical content tests (carbohydrate, protein, fat, water, ash, crude fiber, breakability).

International airports are complex work environments with unique occupational health and safety risks. As hubs of intensive transportation activities, airports involve various operations, including flight services, passenger handling, cargo management, and fuel operations. Early detection of fuel leaks is crucial to ensure the aircraft’s fuel system is free from leaks, blockages, or potential fire hazards. This study aims to analyze the causes of fuel leakage and identify appropriate mitigation measures, using observation and literature review methods. The findings indicate that fuel leakage occurring while the aircraft is parked suggests damage to certain fuel system components, such as seal gaskets, sealants, and dome nuts, which may suffer from wear or cracking due to age-related degradation. Such damage poses serious risks, including fire hazards, corrosion to aircraft structures—particularly in hard-to-reach areas like the wings or fuselage—and a drop in fuel pressure that could impair engine performance or cause flight cancellations. To address these issues, thorough inspections of all fuel system components are essential. Additionally, the use of sensors to detect abnormal changes in fuel pressure and flow, combined with rigorous leak testing after repairs, is recommended to ensure operational safety and reliability.

The detection of hazardous gases, such as carbon monoxide (CO), is crucial for ensuring human safety and health, especially in enclosed environments or areas with a high potential for exposure to toxic gases. Carbon monoxide is a colorless, odorless gas that is extremely dangerous when inhaled in large amounts. Therefore, a CO detection system that can provide early warnings is essential. This research aims to design a carbon monoxide (CO) detector using the MQ-7 sensor, which can detect CO concentrations in the air. The detection system is developed based on Internet of Things (IoT) technology, enabling real-time CO detection data to be transmitted to a cloud platform or user devices via an internet connection. This allows users to monitor CO levels in their environment anytime and anywhere. The system consists of the MQ-7 sensor to detect CO levels, a microcontroller as the controller, and a communication module to send data to the internet. The main advantage of this system is its ability to notify users if CO levels exceed a safe threshold. Thus, this detector can function as a preventive tool against CO exposure accidents. The use of IoT in this system also facilitates remote monitoring, improving the effectiveness and efficiency of managing a safe environment.

Worldwide trade is conducted 80% through maritime routes, including the export and import of coal. Coal is a non-renewable energy source, often used as fuel for power plants. As technology advances, detecting metal contaminants in coal cargo becomes crucial to prevent contamination of coal with metal materials. This study aims to (1) understand the operation of a conveyor in transporting coal, and (2) assess how the Proximity inductive sensor detects metal presence. The methods used in this research include real data analysis and literature review, conducted aboard the ship. The researcher designed and developed a prototype system for this purpose. The Proximity inductive sensor is utilized to detect metal contaminants, such as aluminum, iron, and stainless steel, among the coal cargo. The research was carried out during sea practice (prola) aboard the WHS ISKANDAR 1. The findings indicate that the conveyor is driven by a DC motor, and the Proximity inductive sensor effectively detects metals to separate them from the coal cargo, thus purifying the coal from metal contaminants.

Dry noodles are one type of noodles in Indonesia. Noodles are a type of food that is low in protein and other nutrients, but high in carbohydrates. This is because the main ingredient in making noodles uses various types of flour, one of which is wheat flour. The purpose of this study was to ascertain the effect of adding genjer and spinach on the chemical and sensory qualities of dried noodles and to identify the optimal treatment combination in terms of nutritional value, antioxidant activity, and color that consumers like. The method used in this study was a completely randomized factorial design (CRD) in a two-factor variation with two repetitions, namely genjer puree concentrations of 10, 20 & 30% and spinach puree concentrations of 10, 20 & 30%. The results of the study found that the higher the concentration of genjer pulp, the higher the ash, protein, fiber, and total phenol content but not for the antioxidant activity. The addition of spinach puree apart from being a natural colorant can also help increase the fiber, ash, and antioxidant content of the noodles. Noodles had the best results of chemical analysis in the combination treatment of 20% genjer puree and 30% spinach puree with the results of 5.03% water content, 0.90% ash content, 12.12% protein, 9.93% fiber, 73.15% antioxidants, phenol 2.62 mgGAE/g. Dry noodles made from genjer and spinach have the potential to be developed because they have high antioxidant activity.

Pukis is a traditional cake from Indonesia, made of wheat flour, sugar, eggs, yeast and coconut milk. Cowpeas contains high protein and fiber. Nutrition in cowpea can be used to enrich the nutritional value of pukis. The purpose of this study was to identify characteristics of cowpea flour and mocaf with different yeast concentrations in pukis which are high in protein, fiber and the most preferred by consumers. The experimental design used was a Completely Randomized Design (CRD), the first factor was the ratio of cowpea flour and mocaf (50:50, 70:30, 90:10). The second factor was addition of yeast (2.2; 2.4; and 2.6 g). The analysis indicated that the rasio of cowpea flour to mocaf (50:50, 70:30, 90:10) and the proportion of yeast (2.2; 2.4; and 2.6 g) has a significant effect to chemical analysis of protein, fat, carbohydrate, crude fiber and specific volume, also has a significant effect to sensory tests of color, taste, texture and overall sensory tests. The formulation of pukis which is high in protein, fiber and the most preferred by consumers was found in the ratio of cowpea flour and mocaf 90:10 with the addition of 2.2 g yeast. The best quality of Pukis contain protein 9.12%; fiber 12.75%; moisture content 34.88%; ash content 1.42%; fat 11.45%; carbohydrate 43.14% and specific volume 1.10%. Pukis formulation with the highest level of preference by the panelists has a value of 3.63 (neutral). Pukis from cowpea flour and mocaf with different concentrations of yeast has potency to be a source of protein and dietary fiber.

Indonesia is rich in biodiversity, there are around 40,000 species of plants, and they have different uses,such as traditional medicine, can be made into handicrafts, used as decorations, used as natural dyes. Butterflypea flowers have the potential as a natural dye because the anthocyanins contained in their petals emit a bluecolor. The purpose of this study was to determine the anthocyanin content in butterfly pea flowers using themaceration extraction method which is commonly used to determine the best anthocyanin concentration. Thisstudy used a completely randomized factorial design (CRD), namely drying time, (fresh, 1, 2 days) andmaceration temperature (50, 60, 70oC), so that 9 combinations were obtained and each treatment was repeatedtwice. The results of this study indicate that the longer the drying time and the higher the macerationtemperature, the higher the levels of anthocyanin, total phenol, and the darker the color. The researchconducted showed that the best treatment combination was the drying time of 2 days with a macerationtemperature of 70°C to produce butterfly pea flower extract which had antioxidant activity of 54.66%, totalphenol 4.16 KTF (mgGAE/g), anthocyanin 123.48 mg/g, pH 6.03. Color sensory test analysis was 4.5 andwater content of butterfly pea flower was 10.79%. The optimal drying time and maceration temperature willproduce high total phenol and anthocyanin levels. Butterfly pea flowers have high levels of anthocyanins, sothey have the potential to be studied further. 

Pressure measurement is a fundamental aspect of mechanical engineering used to monitor and control various fluid and mechanical systems. This study discusses different pressure measurement methods, including the use of manometers, pressure transducers, and microcontroller-based sensors. Experimental methods were conducted to compare the accuracy and sensitivity of various measuring instruments in different testing environments, such as hydraulic systems, pneumatic systems, and internal combustion engines. The results show that microcontroller-based sensors provide high accuracy and easy integration with industrial automation systems. Additionally, environmental factors such as temperature and vibration significantly affect the accuracy of pressure measurements. This study provides insights into the advantages and limitations of each method and recommendations for optimal applications in mechanical engineering.

This applied scientific work is entitled "Design of Automatic Smart Berthing System Based on Arduino". Ships are a comfortable alternative transportation. Currently, ship development is needed so that they can operate properly. During the docking process, ship accidents often occur in ports both nationally and internationally. Therefore, a better development or safety is needed that can be given to ships during the docking process. This tool uses the Arduino Nano microcontroller as the main control and an ultrasonic sensor as a distance counter to prevent accidents. This applied scientific work aims to realize and facilitate work on the ship by abandoning manual methods. Berthing is the location of the ship's mooring or security, the area around the ship where the anchor is thrown, the ship's residence, employing the ship's crew and positioning the ship in the desired place. This scientific paper uses experimental research as its research method. The use of experimental research methods in educational research will be faced with problems concerning research subjects. Experimental research is also research that is carried out intentionally by researchers by providing certain treatments to research subjects in order to create a tool or design that is expected by the researcher.

Rapid development and population growth in urban areas have caused various problems, one of which is the decreasing availability of clean water. This has an impact on the agricultural and fisheries sectors which are highly dependent on good water quality. As an alternative to overcome this problem, the aquaponic system offers a solution by combining fish and plant cultivation in one efficient water recirculation system. However, the quality and productivity of the aquaponic system are highly dependent on good management, which requires proper monitoring and control of environmental conditions such as temperature, pH, humidity, and water quality. This study aims to design and build a Node-RED-based aquaponic monitoring and control system. This system uses various sensors to monitor environmental conditions and provide automatic control of parameters that affect plant and fish growth. By using Node-RED as a platform for sensor and control integration, this system is expected to provide an efficient and affordable solution to increase the productivity of the aquaponic system. The results of the study show that the designed tool can carry out effective monitoring and control of parameters that affect the aquaponic system, and can be accessed in real-time to facilitate system management and supervision.

This research aims to design a web server-based heart rate monitoring system, which enables real-time heart rate monitoring from a remote location. The system utilizes a heart rate sensor connected to a microcontroller to measure and transmit heart rate data to a web server via an internet connection. The data obtained can be accessed directly by the user through a web-based interface, thus facilitating the monitoring of individual health conditions in an efficient manner. In addition, the system is equipped with a notification feature that alerts the user if any irregularities in the heart rate are detected, such as beating too fast or slow. As such, the system has the potential to increase alertness and speed up medical action if needed. The development of this system shows significant potential in supporting technology-based health applications, both for personal use and in a broader healthcare context. It is hoped that the system can contribute towards the development of more integrated and accessible health monitoring solutions.      

Research aims to design and develop a boiler system for heating Marine Fuel Oil (MFO) 180 CST by integrating Internet of Things (IoT) technology to enhance efficiency and operational monitoring. The methods used include boiler system design, selection of types and materials according to international standards, and the implementation of an optimal combustion system. IoT sensors are strategically placed to monitor key parameters such as temperature, pressure, and fluid flow in real-time. The collected data is transmitted to a cloud platform, enabling remote monitoring and automated performance analysis through a web or mobile-based application.. The research results indicate that IoT integration in the boiler system improves fuel heating efficiency, optimizes energy consumption, and facilitates easier monitoring and process control. Testing includes pressure tests, combustion efficiency, steam capacity, and material durability, with real-time monitoring to support performance analysis and early problem detection. Operational data evaluation allows for design adjustments or system settings to further enhance energy efficiency. With this innovation, the boiler system can operate more optimally, support energy efficiency, and facilitate predictive maintenance for sustainable industrial operations. The implementation of IoT in this system is expected to improve safety, effectiveness, and automation in boiler management, making it a more reliable and modern solution.

This literature review presents a synthesis of the latest research results regarding temperature measurement techniques, including sensor technology, calibration methods, and their applications in various industries. This analysis highlights advances in the use of infrared thermometry, thermocouple-based systems, as well as digital temperature monitoring tools. Key innovations in this field include real-time temperature monitoring with IoT integration, artificial intelligence (AI)-based predictive modeling, as well as increased accuracy through statistical calibration methods. This study shows that non-contact temperature measurement methods are increasingly important, especially in industrial and medical applications. In addition, the research also highlights the influence of environmental factors on temperature measurement accuracy as well as various strategies to overcome obstacles in using sensors in various operational conditions.

This study aims to design and build a hydraulic system capable of operating hatch opening and closing automatically and manually, with the application of Internet of Things (IoT) technology to monitor and control the system remotely. This system is designed to improve efficiency and ease of hatch operation on ships or other industrial applications, as well as prevent fires in cargo due to several factors such as unintentional negligence of the ship's crew and also weather factors with increasing temperatures. Where the automatic system uses a rain sensor to optimize time and energy, while manual control remains available as a backup option. This design involves the integration of hydraulic actuators, rain drop sensors, microcontrollers, and IoT communication modules that allow users to control and monitor the position of the hatch through a web-based application or mobile device. In compiling this scientific paper, researchers use experimental methods. This study shows the results that testing will be carried out directly by researchers, by testing the performance of the prototype of the automatic and manual hatch opening and closing hydraulic system design tool based on IoT by testing the effectiveness of the control system and sensor performance. In conclusion, this system successfully combines hydraulic and IoT technology to create an efficient, safe, and easy-to-use solution in practical applications.

Unmanned Surface Vehicle (USV) is an unmanned vehicle that is currently being developed in Indonesian waters for the purposes of depth surveys in waters that combine autonomous navigation frames, environmental sensors, multibeam echosounders to collect topography, temperature, and other data that are utilized for the world of shipping. Researchers tested an unmanned ship tool that can move automatically and can determine the direction to a predetermined location. Output. The test results show that the unmanned ship can walk to the planned route. This unmanned ship is equipped with a Micro Air M10G GPS sensor, LoRa Sensor, Arduino Mega 2560 Pro Sensor... In testing as many as 15x experiments were carried out including 10x experiments carried out to turn right and 5x experiments with turning left. The experiments carried out were very effective and according to what had been planned, but there were 4x experiments that had differences from 11x experiments due to water waves and winds that were too large so that the ship was unstable.

This research aims to design a Plasma Blood Separator that can separate plasma from red blood cells automatical-ly using an Optocoupler sensor and a specimen tube cutter. This sistem is equipped with an Arduino Uno as the microcontroller, a servo motor to clamp the hose, a servo to open the blood bag pressure, a series of push buttons to open the servo, and a buzzer to indicate the completion of separation After testing the Arduino Uno-based Plasma Blood Separator, all functions can work well by showing the results of three experiments that varied the length of the hose. The length of the hose that was varied was 30 cm, 50 cm, 70 cm. The estimated separation time was 20 seconds, 30 seconds, and 40 seconds for mode 1, The estimated separation time was 40 seconds, 60 seconds, and 80 seconds for mode 2,  In the results of the measurement of the voltage of the tool, the measurement results were obtained with a small percentage of error with a value of <5% and a high accuracy value with >90%..

Blood pressure is a crucial factor in the circulatory system of the human body. It refers to the amount of force exerted by the blood on the inner walls of the arteries when it is pumped throughout the circulatory system. Blood pressure can be measured using a device known as a digital sphygmomanometer, which determines systolic pressure, diastolic pressure, and beats per minute (BPM) of the human heart. As technology evolves, the digital sphygmomanometer has been enhanced with features such as the Internet of Things (IoT). IoT in healthcare refers to the use of information technology to enable remote health monitoring by healthcare professionals. A digital sphygmomanometer equipped with IoT facilitates the exchange of diagnostic information, treatment decisions, and prevention of diseases and injuries. This IoT-based digital sphygmomanometer is designed using the ESP32 microcontroller. A DC motor is used to apply pressure to the cuff, and a solenoid valve is used to release air from the cuff. The MPX5700AP sensor detects systolic and diastolic blood pressure, as well as BPM. The system also includes a motor driver to control the DC motor and solenoid, a Nextion LCD display to show blood pressure results, and a web server to display and store measurement data. The percentage of measurement error for systolic pressure ranges from the smallest error of 3% to the highest error of 25%. For diastolic pressure, the error ranges from 4% to 36%. As for BPM, the smallest error recorded is 13.6%, and the highest is 70.2%. This device helps patients monitor their systolic pressure, diastolic pressure, and BPM, with results saved and monitored through a database system.

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.