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Korespondensi: susanti@aksibukartini.ac.id Abstract. A hairclip is a hair extension that is later woven or clamped into real hair. Hairclips give a thick effect to hair. Hair clips are made from synthetic materials, one of which uses organic materials from pineapple leaf fiber waste. The aims of this research are: 1) to know the process of making hairclips using pineapple leaf fiber 2) to know and validate hairclip products using pineapple leaf fiber 3) to know the public's acceptance of hairclips using pineapple leaf fiber. The methods in this research are observation, literature, experiments, documentation, interviews and questionnaires. The experiment was carried out at Ds. Jangkungharjo RT.01 RW.01 Kec. Brati District. Grobogan, Central Java. The experiment was carried out three times, namely by coloring 1) Staining Pineapple Leaf Fibers Using Clothes Dye 2) Staining Pineapple Leaf Fibers Using Color Spray 3) Staining Pineapple Leaf Fibers Using Hair Dye. Product validation is carried out by three validators through sensory tests. The product validation results are the products in the first experiment. The results of the public test conducted on 60 respondents were that the public's level of liking for the product was 3.32, they were interested in the color of the hairclip, the level of liking for the texture was 3.17, the level of ease of use was 3.45 and the level of liking for the aroma of  the hairclip was 3.12. The conclusion is that overall public acceptance is 3.26 with the description category like score. Repairs need to be made so that the hair remains strong and does not break or fall out easily and tools are needed to make the fiber making process easier..    

The purpose of this research are 1) knowing the acceptability of putu ayu with coconut water and red dragon fruit peel flour which is most preferred by the panelists, 2) knowing the best recipe formula 3) knowing  the protein content of putu ayu with coconut water and dragon fruit peel flour as ingredients. red is the panelists' favorite. This research method uses literature, documents, experiments, sensory evaluation and chemical tests. The results showed that putu ayu with coconut water and red dragon fruit peel flour Formula A was most liked and accepted by the panelists. The best putu ayu recipe formula uses coconut water and red dragon fruit skin flour. Formula A is 237.5 g wheat flour, 12.5 g red dragon fruit skin flour, 200 ml old coconut water, 3 eggs, 200 g granulated sugar, 1 tsp SP, and 1 grated coconut. Based on the results of chemical tests for the protein content of putu ayu using coconut water and red dragon fruit peel flour, formula A per 100 g is 6.117% protein.

. Ice cream product development is now increasingly preferred and varied, so ice cream is innovated into high-fibre ice cream that can be accepted by all people. The objectives of this research include: 1) Knowing the public acceptance of high-fibre ice cream with dragon fruit and oat milk, 2) Knowing the right composition of ingredients in high-fibre ice cream with dragon fruit and oat milk, 3) Knowing the nutritional content of high-fibre ice cream products with dragon fruit and oat milk. The research methods used included literature, experiments, documentation, sensory tests, and nutritional content tests. The first experiment was carried out using oatmeal and dragon fruit, because the results were unsatisfactory, the second experiment was carried out using Oat milk and dragon fruit, the results of the second experiment the texture of the ice cream was still unsatisfactory, so the third experiment was carried out by adding skim milk powder. Organoleptic test results obtained ice cream products with dragon fruit as much as 20% of the liquid is the most preferred product by panellists. The formula preferred by panellists in ice cream products is with oat milk 500g, dragon fruit 100g, stevia sugar 5.2g, ice cream stabiliser 2g, and skim milk powder 25g. The nutrient content per 100g of ice cream is 19.23% carbohydrate, 5.18% fat, 3.93% protein, 16.40% fibre

Physics Doctor Sensor technologies have improved the everyday life of human beings through their applications in almost all fields. Sensors are devices that detect changes in the source/environment and collect signals, and accordingly, the reaction is designed. There is a range of sources, including light, temperature, movements, and pressure etc., which may be used. A wide range of applications are utilized using innovative sensor technologies in lifestyle, healthcare, fitness, manufacturing, and daily life. In the medical field, the difficulty of taking medicine is eased by drug donors fitted with sensors. It reminds them to take medicine via a signal and supply the necessary medicine at the specified moment. In health care, older individuals, athletes, and risk patients benefit from modern sensor technology. The current industrial trends driving innovation include ultrasound, radar, and non-contact optoelectronic solutions and laser technology. The paper gives a brief overview of the numerous types of sensors that are utilized in everyday life. Various capabilities of sensors for day-to-day healthcare are discussed. Various features, associated nomenclature, and measures for sensors in day-to-day routine life are discussed diagrammatically and finally, the paper identifies and discusses twenty-two significant applications of sensors for daily life. Sensors also produce vital information and exchange data with other connected devices and administration systems when linked to a network. Thus, for the effective running of many companies, sensors are critical. Various types of sensors are used in our daily life, which is more accurate and makes quicker analysis.

Technological advancements have significantly contributed to the robotics industry, yet mobility remains a challenge that can be addressed with mobile robots. In education, it is crucial to understand the basic principles of robotics and the use of related software and hardware. This research focuses on using a 4WD mobile robot as a learning tool. This robot offers flexibility and is easily controlled via a smartphone with built-in sensors. A smartphone application was developed using App Inventor, an open-source web-based development platform. The HC-05 Bluetooth module was used to connect the smartphone application with the 4WD robot. The goal of this research is to design innovative learning media to aid in understanding the concepts and control of the 4WD robot, as well as to increase students' interest and motivation in learning robotic systems. The results of the research demonstrated success in creating a 4WD robot prototype controllable via an Android smartphone. The distance between the Arduino and smartphone did not significantly affect the robot's movement, although communication was disrupted if the distance exceeded 10 meters. Directional control was achieved through the rotation of the right and left wheels, and sufficient power supply was needed to drive all wheels. Programming with C/C++ and Bluetooth communication functioned as planned. This innovative learning media is expected to help students understand and operate the 4WD mobile robot more effectively

Innovation in robotics and the Internet of Things is a technological trend that continues to be developed, including in transportation innovations, such as a car. What is developed in this research is the development of the application of robots and the Internet of Things (IoT) in a mini car prototype design which can be remotely controlled as well. This prototype design uses the engineering design process method which integrates all supporting devices in the form of NodeMCU with ESP8266 base as a control center that allows communication between various sensors and actuators with the internet, TT Motor Gear as an actuator and L298n as an output translator. This mini smart car is controlled using Blynk as an internet-connected controller and for remote control of car movement using a smartphone. The results of this study are the average remote control capability has a car movement delay of 0.87 seconds and 100% of cars move in all directions. The better the internet connection, the more stable the movement of the car to be driven remotely. 

Background: The rapid growth of warehouse automation and autonomous mobile robots has increased the need for adaptive navigation systems capable of operating safely and efficiently in dynamic industrial environments. Classical path planning algorithms such as A* and RRT perform well in structured settings but exhibit limitations when handling moving obstacles and environmental uncertainty. Objective: This study aims to develop and evaluate a reinforcement learning based navigation framework integrated with sensor fusion to improve path efficiency, collision avoidance, and robustness in dynamic warehouse scenarios. Method: An experimental research design was implemented combining high-fidelity simulation and real-world warehouse prototype testing. Deep Q-Network and Proximal Policy Optimization models were developed and trained using multi-sensor inputs from LiDAR, camera, and inertial measurement units. Performance was evaluated using path efficiency, collision rate, computational cost, and robustness metrics, with benchmarking against classical algorithms. Results: The results demonstrate that the Proximal Policy Optimization model achieved the highest path efficiency and lowest collision rate while maintaining stable computational performance under dynamic conditions. Reinforcement learning models significantly outperformed classical planners in adaptability and robustness, confirming their suitability for scalable industrial warehouse automation.

The purpose of this research was to determine the effect of tannia flour and pigeon pea to produce the best snack bar with high dietary fiber and also with the good sensory. This research was an experimental study using a Completely Randomized Design (CRD) with a treatment ratio of tannia flour and pigeon pea were 70%:30%, 60%:40%, 50%:50%, 40%:60%, and 30%: 70%. Each treatment was repeated three times. The variables observed were moisture content, ash content, protein content, fat content, carbohydrate content, crude fiber content and sensory of snack bar (color, flavor, taste, texture). This best snack bar contains 5,31% water, 2,11% ash, 11,22% protein, 18,33% fat, 61,58% carbohydrate, 19,50% crude fiber. After getting the best snack bar with the effectiveness test, it was observed the levels of dietary fiber. Based on the results of the effectiveness test, the best treatment in this research was tannia flour and pigeon pea with ratio 30%:70%. This best snack bar contained 5.58% of fiber. The results of the sensory test of color were liking, while the flavor, taste, texture, and overall acceptance were like moderately. The results of the scoring test for color were brown, with a slightly distinctive tannia flavor, no itching taste, and a dense texture.    

Autoclaves are the most widely used tool for sterilization worldwide and are considered the most effective and cost-effective method for sterilizing medical equipment. The research aims to design and build an autoclave that uses an Arduino microcontroller as the brain of the system and is equipped with a temperature sensor, heater driver, heater and power supply. This autoclave will help ensure that the medical equipment used is sterile by using a working system using a 121℃ pressurized steam heat method for 15 minutes. Based on the results obtained, an Arduino nano-based autoclave device has been successfully created. By measuring each measurement point, the error value is very small, namely TP1 at a voltage of 5.1 with an error of 2%, TP2 at a voltage of 4.8 with an error of 0.4%, TP2 at a voltage of 4.8 with an error of 0, 4%, TP4 at a voltage of 225 with an error of 2%, TP5 at a temperature of 121 with an error of 0%.      

Snack bar is a stick-shaped snack made from flour, seeds and nuts. This research aims to determine snack bar products made from a mixture of millet flour ad arrowroot flour with the addition of walnuts, including physical properties (L*a*b* color values), chemical properties (water, ash, fat, protein and crude fiber contents), and sensory properties based on hedonic test. This study was experimental using Complete Randomized Design (CRD) with the percentage ratio treatments of millet flour and arrowroot flour consisting of formula S1 ((20%:80%); S2(30%:70%); S3(40%:60%); and S4(50%:50%). The data obtained were processed using the Variance Analysis with a confidence level of 95% (α=0,05), and Duncan’s follow-up test. Snack bars have color values L* 41,660-52,580; color a* 6,6317-9,0667; color b* 19,9017-23,9667. The results of proximate analysis showed that snack bars contains 13,7176-17,8433 of water; 0,8459-1,4455% of ash; 5,8175-7,3264% of protein; 20,5320-24,2649% of fat; 48,7558-50,1120%; and 3,6339-5,1826% of crude fiber. Millet flour increases ash and protein content, but fat and crude fiber content decrease. Based on the hedonic test, it is known that panelists liked the taste and texture of S4 snack bar with an average score 3,94 and 3,68; while for the aroma in the treatment of S3 snack bar with an average score of 3,97 and the color in the treatment of S2 snack bar with an average score of 3,97 (hedonic test scale 1=very dislike, 2=dislike, 3=neutral, 4=like, 5=very like). Snack bars combination 30% millet flour:70% arrowroot flour have the best chemical characteristics (value 1,167) and the best sensory characteristics on snack bars from combination of 50% millet four:50% arrowroot flour (value 0,745) based on the DeGarmo effectiveness index test.

Introduction: High blood sugar can cause neuropathy and peripheral artery disease, reducing sensation in the limbs and leading to diabetic foot ulcers (DFU), about 30% of diabetics develop DFU. The purpose of this study was to analyze the intervention of home foot temperature monitoring in patients with diabetic foot ulcers (DFU). Methods: using the PICO formulation to conduct a literature search collection of materials from databases such as PubMed, Google Scholar, and science direct. The search was conducted using relevant keywords,and focused on articles published between 2021 and 2024 according to the inclusion and exclusion criteria. Results: Low compliance in reducing activities after detecting hotspots hinders ulcer prevention, especially in those with high self-care scores. Home foot temperature monitoring is ineffective without activity reduction, requiring behavioral changes and a comprehensive approach. Sensor-based smart footwear better prevents ulcers by reducing plantar pressure, while telehealth and interactive dashboards enhance patient monitoring and communication for more efficient care. Conclusion: The articles stress combining behavior change and technology to prevent diabetic foot ulcers. Low activity reduction limits effectiveness, but smart footwear aids prevention, while telehealth improves monitoring and communication, highlighting integrated solutions.

This research examines the optimization of brain plasticity in Arabic language learning through a neurolinguistic approach, using a literature review method. Brain plasticity, the brain's ability to adapt and change through learning and experiences, plays a crucial role in language acquisition. Neurolinguistic approaches, which integrate techniques such as task-based learning and the use of educational technology, have proven effective in enhancing language learning outcomes. Literature analysis indicates that learning techniques involving various sensory modalities and educational technology can stimulate new neural pathways, strengthening language comprehension and memory. Studies focused on Arabic language learning highlight that traditional approaches are less effective compared to more interactive and communicative methods. The use of technology such as mobile applications and augmented reality provides a dynamic and engaging learning experience for students. Practical recommendations include developing more interactive curricula, utilizing educational technology, and training educators in neurolinguistic techniques. This research emphasizes the importance of a multidisciplinary approach to enhance the effectiveness of Arabic language learning, providing insights and strategies to optimize language learning through a better understanding of brain plasticity and neurolinguistics.

This research examines the optimization of brain plasticity in Arabic language learning through a neurolinguistic approach, using a literature review method. Brain plasticity, the brain's ability to adapt and change through learning and experiences, plays a crucial role in language acquisition. Neurolinguistic approaches, which integrate techniques such as task-based learning and the use of educational technology, have proven effective in enhancing language learning outcomes. Literature analysis indicates that learning techniques involving various sensory modalities and educational technology can stimulate new neural pathways, strengthening language comprehension and memory. Studies focused on Arabic language learning highlight that traditional approaches are less effective compared to more interactive and communicative methods. The use of technology such as mobile applications and augmented reality provides a dynamic and engaging learning experience for students. Practical recommendations include developing more interactive curricula, utilizing educational technology, and training educators in neurolinguistic techniques. This research emphasizes the importance of a multidisciplinary approach to enhance the effectiveness of Arabic language learning, providing insights and strategies to optimize language learning through a better understanding of brain plasticity and neurolinguistics.

An experiment examined micrometer shifts in optical fibers to understand sensor characteristics: range, linearity, sensitivity, and resolution. Findings showed voltage output decreases with fiber length. The fiber coupler sensor's range was 2 mm, and the fiber bundle sensor's was 25 mm.While not perfectly linear, the study identified operational ranges using y = ax + n. The fiber bundle had two linear regions: Front Linear (0.2-1.6 mm) with R² = 0.9918 and Rear Linear (2.3-8.3 mm) with R² = 0.977. The fiber coupler's linear trend spanned 0.3-2.1 mm with an R² of 0.944. Sensitivity was 4668.5 V/mm (front) and 780.91 V/mm (rear) for the fiber bundle, and 215.79 V/mm for the fiber coupler. Resolution was 0.0107 µm (front), 0.0640 µm (rear) for the fiber bundle, and 0.2317 µm for the fiber coupler. These findings shed light on optical fiber shift sensor parameters.

Ship stability problems are one of the causes of ship accidents such as ships that are difficult to control, unbalanced and sinking, either at sea during shipping or during loading and unloading. This is the background for the need to improve ship safety systems to reduce ship accidents. The purpose of this research is to accurately monitor the loading and unloading slope and determine the maximum distance that can be connected to LoRa. The working system of this tool is made with the intention of preventing ship accidents and creating a better sea transportation system. The work process of this tool is done by reading the tilt angle of the ship which will then be read through the LCD screen and the LED lights will light up and the buzzer will sound if it reaches a certain tilt angle. Making it easier for loading and unloading operators to manage their cargo so that the ship remains stable.The methodology and design of the applied scientific work tool system that will be carried out using experimental research methods. Experimental research methods are used to produce certain products, and test their effectiveness. The results of research on this applied scientific work are the accuracy of the gyroscope sensor GY-521 mpu-6050 which is 98.71% with an error of 1.29% and the distance of LoRa Ra-02 SX1278 that can be reached on a stable wireless system is as far as 370m in water and 205m on land. More than that the receiver cannot receive data anymore or is invalid.

Air pollution is a problem that needs attention because it can threaten human life. Many human activities cause air pollution. Especially in closed rooms such as ship engine rooms. Therefore, an air pollution detection tool is needed to determine the air pollution index in that place in order to maintain pollution levels below the threshold value. Therefore, it is necessary to have a tool to detect air pollution using the MQ-135 gas sensor to detect carbon dioxide (CO2) gas and TGS2106 to detect nitrogen dioxide (NO2) gas. The type of research method used is the experimental research method. After designing the tool, testing the tool, the air pollution detection system in the engine room wirelessly using the MQ135 and TGS2106 sensors based on LoRa Ra-02 had an average error rate of 1.3% for the MQ135 sensor reading while the TGS2106 sensor reading had an average level. -average error of 6%. Therefore, the Air Pollution Detection System in the Engine Room Wirelessly Using the MQ135 and TGS2106 Sensors Based on LoRa Ra-02 can work well.  

Fresh water is a basic need of the ship's crew in supporting the creation of smooth ship operating activities, therefore it is also necessary to have an auxiliary aircraft that can produce its own fresh water on board to reduce ship operating costs, which is called the Fresh Water Generator (FWG).  FWG is an auxiliary aircraft that can convert seawater into fresh water by evaporation and condensation processes. Therefore, it is necessary to have a tool to monitor the performance of the fresh water generator. The purpose of this research is none other than to monitor temperature conditions, water TDS, and conditions on the input and output streams of the fresh water generator evaporator filter to be more optimal and efficient and to determine the reliability of the fresh water generator performance equipment system using a long distance in order to create better and higher quality fresh water production on board. This research uses a prototyping method system. After designing and testing the tool monitoring system for the performance of fresh water generators using Arduino mega 2560 based on LoRa Ra-02, this system is one of the new technologies that can monitor the performance of fresh water generators to be more efficient and reliable. Then the readings of all sensors can work properly with an average percentage error on the Max 6675 temperature sensor of 0.58%, pressure transmiter sensor 1 of 2.04%, pressure transmiter sensor 2 of 2.04%, and TDS  sensor of 1,70% and the distance to communication between the receiver and transceiver reaches a range of 200 meters without obstacles and 97 meters through obstacles around.

As an important means of sea transportation for the distribution of goods and human mobility between islands, ships require a reliable prime mover to avoid wear or damage, especially when overspeeding occurs which can result in fire. Therefore, a prototype was created that uses an IR Obstacle sensor to monitor the RPM speed of the rotating engine. By using Arduino Uno as a processor, this prototype is able to display the RPM value on the LCD and provide a signal in the form of a green light if the speed is below 4000 Rpm, as well as a red light and buzzer sound if overspeed occurs above 4000 Rpm, followed by the engine turning off after 15 seconds. Testing shows that this prototype has a high level of accuracy, with a difference between 0.08% to 2.73% and an average difference of 1.003%.

In the hold of the fishing boat, it is currently unable to control the pH of the water in accordance with the living environment of the fish. The pH of the water also needs to be considered so that the fish can survive. In addition, fish feeding on fishing vessels is also very necessary. The purpose of this research is to design a system that can control pH, fish feeding, fish water replacement. in the fish hold using an IoT-based microcontroller. The method used in this research uses quantitative methods through system design, design software design models, product trials.This method is used This research method includes steps taken to design and implement the system, as well as tests carried out to ensure the performance and success of the designed system. The results of the data designed by the system are analyzing the data in testing this tool according to its function. The accuracy of using the tool when feeding is 90.5% and the percentage error is 9.5%. The accuracy of using the tool when draining mode is 100% with a percentage error of 0%. And for the results of the percentage error of the pH sensor when the mode is on by 100% with a percentage error of 0%. This shows that the Smart Pond system designed has a high level of accuracy in terms of fish feeding and pond draining.

The condition of the ship's generator coolant is always monitored because it affects the performance of the ship's engine. Maintaining the engine coolant temperature is very important to avoid damage that occurs because the engine coolant is too hot on the engine components. With the internet of things, a technological concept has the ability to connect and facilitate the communication process between machines, devices, sensors and humans via the internet network. The aim of this research is none other than to monitor and automatically fill the water level of coolant radiators on generator engines on ships using an IoT system. This research uses a prototyping method system. After designing the tool, testing the tool, monitoring system and automatic filling of the water coolant radiator on the generator engine, the average error rate in the ultrasonic sensor readings when the first test was carried out was 3.02%, the second error was 2.57%, and the third error was 3.63%. Meanwhile, the coolant water discharge data is known to be 3 seconds to 10 seconds with a discharge of 60.7 mL/s. Where this data is monitored on the Blynk 64 mL/s application with an error rate of 5.43%. Therefore, the monitoring and charging system for this water coolant radiator using the IoT system can work well.