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Red roses (Rosa damascena Mill) are a type of rose with a distinct aroma and a deep red color. Roses are usually used as decoration, but it also has health benefits. Roses have bioactive compounds and anthocyanin pigments that act as antioxidants. The expected outcome of this research is to produce rose water products that have a strong aroma and have high antioxidant activity. This study used a factorial Completely Randomized Block Design (RAKL) method. The factor used is the variation of the distillation method. While the length of the extraction process was used as the sampling time. The correct distillation method to produce rose water which has high antioxidant activity is treatment with variations of the steam distillation method. The steam distillation method obtained the following results: pH 4.07, vitamin C content 40.70 mg/100g, total phenol content 47.10 mg GAE/g, antioxidant activity of DPPH 64.20%, FRAP value 98.97% and sensory test (scent) 3.34.

Nugget is processed mashed meat. Several types of fish such as tuna, tilapia, and snapper have the potential to increase sales value and can be used as raw material for nuggets, because apart from having quite high proteins, they also have thick flesh. Carrot flour has high levels of fiber and also high levels of beta-carotene. The purpose of this study was to determine the characteristics of fish nuggets which were high in fiber and beta-carotene with variations in the addition of carrot flour, tapioca flour and to find out the highest level of preference for fish nuggets. research using a completely randomized design (CRD) 2 factors with a comparison of carrot flour and tapioca flour and variations of fish species. The best chemical test results were the comparison of carrot flour: tapioca flour (40%:20%) with tuna fish with beta-carotene content of 6.69 μg/g, crude fiber content of 36.54%. The highest preference sensory test result is the comparison of carrot flour with tapioca flour (40%: 20%) with the type of tilapia meat with a color test result of 3.14 (likes), taste test 3.18 (likes), aroma test 3.20 (likes), texture test 3.29 (likes), and overall preference test 3.82 (likes). ). Fish nuggets made from tapioca flour and carrot flour can be a food that has high levels of crude fiber and beta-carotene.

Mocaf (Modified Cassava Flour) is one type of flour processed local cassava food that is expected to replace wheat flour. Legume flour can be used as a source of protein while enriching the nutritional value of gluten-free cakes. This study aims to make a cake formulation with mocaf flour ingredients fortificated with high-protein legume flour. The study used a two-factor complete randomized design (RAL) with two repeats. Factors used are the comparison of mocaf flour and legume flour (40:60, 50:50, 60:40) as well as variations in the type of legume flour (green beans, kidney beans, soybeans). The best formulation produced by sponge cake with the highest protein content is in the treatment ratio of 40:60 mocaf flour and composite flour with green bean flour types, namely water content 22.65%, ash content 1.38%, fat content 10.65%, protein content 7.28%, carbohydrate content 56.72%, and fiber content 8.32%. Organoleptic tests included 2.46 (dark green), mocaf flavor 2.55 (not too pronounced), legumous flavor 3.29 (slightly flavored), soft texture 2.53 (not too soft), soft texture 2.93 (slightly tender), aftertaste 2.80 (slightly concentrated), off flavor 2.62 (not too deviated), preferred value 2.85 (slightly preferred) and physical development volume expanded by 16.55%.

Nori is one of food in the  form of thin sheets. This study uses gotu kola leaves, and red seaweed as the basic ingredients for making nori. Red seaweed contains a large amount of amylopectin so that it has the potential to be used as nori, while gotu kola leaves have good fiber content. Except the fiber content, kola leaves (Centella asiatica .L) also content high antioxcidant as fenol, tanin and saponin. This study also uses drying with a cabinet dryer, room temperature and the sun. This study aims to determine the right formulation to produce gotu kola leaf nori which has high fiber content with variations in drying time. This research was conducted using a completely randomized design (CRD) method which consisted of 2 factors, namely the ratio of red seaweed and gotu kola leaves with variations in drying time. The first factor is the comparison of seaweed and gotu kola leaves (50:50, 40:60, and 30:70), while the second factor is the variation of nori drying time (cabinet drayer, room temperature and sun). The best chemical test results from fiber contant parameters were P3R3 with the results of 13.41% water content, 0.052 ash content, 21.18% crude fiber and 8.71 tensile strength.  The best sensory test results were P3R3 with color test results of 3.64, gotu kola falvor 3.58, crunchiness 3.27, elasticity 3.27 and overall preference 3.85.

Velva, a kind of frozen dessert, made from fruit that contains nutrients and help the body's metabolic processes as a source of energy. Red dragon fruit (Hylocereus polyrhizus) is used in the production of velva in order to get a good color and Moringa to increase antioxidant activity.  The purpose of this study was to determine the appropriate substitution ratio between velva, dragon fruit, moringa leaf extract, and stabilizer to produce dragon fruit velva with good characteristics, namely high antioxidant and preferred by consumers.  This study used a factorial completely randomized design (CRD).  The factors used were the concentration of Moringa leaves (8, 10, 12 g) and the concentration of the type of stabilizer (CMC 1,26%, cornstarch 1,26%, carrageenan 1,26%).  The best chemical analysis was treatment concentration of carrageenan stabilizer 1,26% with the addition of 8 g Moringa leaf extract with antioxidant activity 16,85%, crude fiber 0.63%, vitamin C 4,33%, reducing sugar 11,83%, total phenolic 13,93mg.GAE/ml and pH 3,5.  Physical analysis with melting power 47.00 minutes/hour, overrun 13,00%, TPT 4˚Brix and TAT 1.14%.  The results of the red dragon fruit velva sensory test with various types of stabilizers and the addition of Moringa leaves, as well as the combination of treatments between the two had no significant effect on color, texture, taste, aroma and overall preference.  The best organoleptic analysis was the P3K1 treatment (concentration of carrageenan stabilizer 1,26% with the addition of 8 g Moringa leaf extract) with a color value of 3.06, taste 2.57, aroma 2.78, texture 3.50 and overall preference 3,42.

Unmanned Surface Vessels (USV) have become a major focus in modern maritime technology for mine search and handling. Unmanned vessels are equipped with advanced sensors such as sonar and lidar to detect and map mines below the water's surface. In addition, image processing systems and artificial intelligence are used to analyze sensor data and identify mine locations with high accuracy. Once a mine is detected, the unmanned vessel is equipped with a mine handling system that can safely destroy or deactivate the mine. Thus, unmanned vessels are an effective and efficient solution in searching and handling mines in potentially dangerous waters. This study discusses the selection of unmanned vessels as a solution to search for and neutralize mines. This study was conducted quantitatively using the Analytical Hierarchy Process (AHP) method to determine recommendations for Unmanned Vessels with Mine Countermeasure capabilities to the Indonesian Navy. The results of the quantitative study showed the priority of the criteria that would later narrow down to the priority of selecting an Unmanned Vessel from several alternative choices analyzed by the researcher. Theoretically, all previous theories and studies can be used in this study so that they can be utilized in subsequent studies. While practically this study provides recommendations for the Indonesian Navy regarding the characteristics of the Unmanned Axe and various supporting equipment that can be used to carry out Mining Operations and Mine Countermeasures Operations. The calculation results in this study indicate that the priority of the alternative Unmanned Vessel with the highest Mine Countermeasures capability is obtained by Inspector 125 from France with a weight of 0.37450145.

Persons with disabilities represent a diverse segment of society, facing unique challenges in accessing public services such as education, healthcare, and employment. In response, Indonesia ratified the Convention on the Rights of Persons with Disabilities (CRPD) through Law No. 19 of 2011, which serves to promote, protect, and ensure equal rights and fundamental freedoms for individuals with disabilities. This commitment led to the enactment of Law No. 8 of 2016, replacing an earlier, more compassionate framework with one emphasizing equality and the protection of rights for persons with disabilities. The law defines individuals with disabilities as those facing long-term physical, intellectual, mental, or sensory limitations and emphasizes the necessity of special protections to uphold their rights and prevent discrimination. The method used in this study is a normative legal research method. This study evaluates the effectiveness of disability rights protection policies in Indonesia, revealing significant challenges despite the legal framework established by Law No. 19 of 2011. Access to education, healthcare, and employment remains limited due to inadequate facilities, discriminatory practices, and social stigma. Effective solutions require strengthening the legal framework, increasing awareness, and involving the disability community in policy planning. Furthermore, robust monitoring and enforcement mechanisms are essential for safeguarding rights. To enhance protections, improved coordination among ministries, ongoing training for service providers, inclusive education practices, and recognition of employers who hire individuals with disabilities are critical. These measures are vital for fostering a more equitable society and ensuring the rights of persons with disabilities are upheld sustainably.

This study investigates the effects of adding various natural extracts: carrot, red ginger, mango, and tamarind on the physical and sensory properties of honey candy. Specifically, the research examines the pH value, yield, and organoleptic qualities (taste, aroma, texture, and color) of honey candy enhanced with these extracts. A laboratory experiment with a completely randomized design (CRD) was conducted, and each treatment was replicated four times. Results showed highly significant differences in the all  treatments. The highest pH was recorded with red ginger extract (5.41), while the lowest was with tamarind extract (4.02). Carrot extract yielded the highest production efficiency (61.5%), followed closely by tamarind (58%), whereas red ginger and mango showed lower yields. Organoleptic testing revealed that tamarind extract was most preferred in terms of taste and color, while mango and red ginger improved texture. Red ginger, however, received lower scores for taste and aroma, likely due to its strong spicy profile. Overall, the addition of natural extracts significantly impacted the quality of honey candy, with tamarind and carrot extracts demonstrating the most favorable effects on yield and sensory attributes, making them promising choices for enhancing functional candy products.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.

This research aims to design and developing a hydrofluorocarbon (HFC) leak monitoring system in the cold storage cooling system on the Anchor Handling Tug Supply (AHTS) ship by utilizing Internet of Things (IoT) technology. HFC leaks in cooling systems can cause environmental hazards and operational inefficiencies. The proposed system integrates sensors and IoT devices to continuously monitor HFC levels, detect leaks in real-time, and provide instant alerts to crew and management. The system architecture includes sensors for HFC detection, a microcontroller for data processing, and a communication module for data transmission to a central monitoring platform. Real-time data can be accessed through a user-friendly interface, enabling timely intervention and maintenance. Initial testing shows that the system is effective in identifying and reporting HFC leaks, potentially reducing environmental impacts and improving the safety and efficiency of cold storage operations on AHTS vessels. Further development and extensive field testing are planned to refine the system and validate its performance across a wide range of operational conditions.

Knowledge refers to information or facts collected and resulted from people learning through sensory perception which lead and shape their action. It is necessary for students both male and female to understand menstruation knowledge. Although the boys do not have experience of having menstrual cycle, they need to understand in order to behave appropriately towards their female friends, such as showing respect, emphaty and support for their peers. The study aims to investigate the correlation between students’ mestruation knowledge towards their attitudes in grade fifth and sixth of Integrated Islamic Primary School of Insan Madani Bongas.The study uses quantitative approach to examine relationship between the knowledge and attitude. To collect data, the researcher distributes the questionnaires to students. To describe the correlation between the variables, for data analysis the researcher uses classical assumption test and simple linear regression. This study shows that the knowledge and attitudes of sixth-grade students towards menstruation are better than those of fifth-grade students. And, the knowledge and attitudes of female students regarding menstruation are better than those of male students since females have personal experience with menstruation, while males do not. Finally, in conclusion, there is a correlation between students’ menstruation knowledge and their attitudes in fifth and sixth grade of SDIT Insan Madani Bongas.

The tool that detects human movement has been designed with a passive infrared sensor based on Arduino ESP32 which can send messages via SMS to the ship's security system. This is the background to the need to improve the security system on ships. The aim of this research is to find out the movement in the ship's engine room. This system can find movements. This tool can detect a maximum distance of 6 m. This system consists of software and hardware that allows sensors to detect human actions. The software uses the C+ language program and is used on the Arduino ESP32. Press the switch to activate this device. A message will be sent to the owner's smartphone if there is movement. Methods and tool system design for applied scientific work that will be carried out using prototype research methods. Prototype research methods are used to produce certain products, and test their effectiveness. The results of the research in this applied scientific work are the level of accuracy of the PIR sensor which detects human movement at a maximum distance of 6 meters and sim800L as a message sender to Android. Beyond that the data is unreadable and invalid.

Fire is an event that often occurs due to human negligence. Losses from fire include damage to goods, business delays and can even cause loss of life. For early handling of fires, a "Fire Fighting Control Design Using MQ-2 Sensor and Fuzzy Logic Based 5 Channel Flame Sensor" has been created. This tool uses the MQ-2 sensor to detect gases related to fires, such as hydrocarbon gas. A 5 channel flame sensor is used to detect flames. The predetermined fuzzy logic rules will be used to control the fire extinguisher and implemented using the Arduino Atmega 2560 microcontroller as the brain of the system. Next, the microcontroller will send a control signal to the 12V DC pump output as a water spray system to deal with fires. The test results of this system show that the tool can detect early fire hotspots and respond well to fire extinguishing according to the fuzzy rule decisions carried out by testing the tool with 30 fire spots at different distances.

In order for life to exist on Earth, water is required and is particularly important on ships. Concerns about water scarcity arise because water resources cannot always meet demand. pH has an impact on the quality of drinking water, with an ideal range between 6.5-8.5. Alkaline water with a pH of 8.8 has health benefits, including helping to lower stomach acid and improve body oxygenation. The pH of drinking water recommended by the EPA is between 6.5-8.5 to ensure safe consumption. pH is a measure of the acidity or basicity of water, on a scale of 0-14, where 7 is considered neutral. The research plans to make a drinking water pH control system using an ESP 32 microcontroller. This research aims to control also monitor the pH of drinking water efficiently using ESP 32 microcontroller. This research uses the Research and Development (R&D) method to design pH control in drinking water solutions. The phases of the research include using a pH sensor to detect pH values, using an ESP 32 microcontroller to control pH, and validating and improving the product model. Overall system testing in the design of a drinking water pH controller system from water treatment is an important stage to ensure successful integration between all components and desired functionality. This testing process includes several main stages, Hardware Integration Testing: All hardware is physically connected and integrated, including ESP32, relays, solenoid valves, and pH sensors. This test is taken to ensure that all components are properly installed and interact with each other correctly. Software Integration Testing: This type of testing verifies that all features and functions of the system function as intended by the software program responsible for it. This includes testing Wi-Fi connections, integration with the Blynk platform, and programming logic as an automatic water controller. Testing System Functionality: to ensure that the intended features can be run properly, the system is tested in various usage scenarios. This includes testing the water controller automatically. the designed system can function normally and adjust appropriately to the designed controller system. From the data obtained regarding the performance results of the system built, it was found that the pH controller system can be run automatically based on the water that has been set as the set point of the system created. The monitoring system with IoT is also able to run well. The time needed will be more to control the pH in the range of 6.5-8.5.

This research aims to design a HFO Settling Tank monitoring simulation tool using an ultrasonic sensor that displays the fuel level in real time and accurately. The research that will be carried out is by utilizing Arduino Uno microcontroller technology and utilizing ultrasonic waves from ultrasonic sensors to determine a height in a tube container. These sensors as inputs will be received by Arduino Uno and the data results from the sensor will be displayed on the LCD and notification from the buzzer. In this study the authors used a research method in the form of the Rnd (Research and Development) method, which begins by analyzing and conducting in-depth research on an object or tool. Then the author designs a tool design which will then validate the design design, and revise the product if the design is deemed insufficient. For the last stage, testing the product, if the tool does not work according to the expected function, a redesign will be carried out. The result of this research is that the ultrasonic sensor is able to detect the height of the simulated container. The results of the detection are processed through Arduino and displayed on the LCD so that all parties can know the water level in the tube container.

Rainfall monitoring is an important aspect of meteorology and water resources management. The development of a microcontroller-based rainfall monitoring device that can transmit data in real-time through the Telegram application offers an effective and efficient solution to overcome the constraints of manual monitoring. This research aims to design and build a rainfall monitoring system that uses a microcontroller as the main controller and the Twilio platform as a data transmission service via Telegram. This tool is designed to measure rainfall intensity using a hall effect sensor, which is then processed by a microcontroller. The rainfall data obtained will be sent to the user's device via Telegram message. This system is expected to provide accurate and timely information to users, so that it can be used in various applications such as agriculture, flood monitoring, and water resource management. The test results show that this rainfall monitoring tool is able to send data quickly, this tool is expected to increase efficiency and effectiveness in rainfall monitoring and make a significant contribution in the field of meteorology and environmental management.

Child development problems are also related to learning and education problems. In the learning process, a combination of psychosocioemotional, psychomotor skills, medical, and language relationships is needed. These four things are the main keys or gateways to a bright future, especially for children who have learning problems. This study discusses learning media used to train sensory and motor skills of children with special needs using the socialization and demonstration method at St. Anna Tomohon Special School. The phenomenon found at St. Anna Tomohon Special School is that they have problems with sensory, motoric, and learning processes. This results in disruption of the child's physical development. The purpose of creating a sensory playmat is to train good coordination between the five senses and movement to the stimulation received. Located at St. Anna Tomohon Special School, with 21 participants in the activity. Various reactions were found when children demonstrated which were tested by imitating and following games such as acceptance and rejection

The importance of innovation in waste management is increasingly felt in this modern era. This research introduces an automated waste handling system utilizing Arduino Uno-based sensors. Arduino Uno, as a versatile microcontroller, is employed to control the automation process in waste bins. Integrated ultrasonic and load cell sensors assist in identifying the presence of individuals disposing of trash and monitoring the weight of the waste, triggering the automatic disposal mechanism. This implementation not only enhances the efficiency of waste management but also provides convenience for sanitation workers. The study details the design, implementation, and performance evaluation of the automated waste bin system, showcasing the potential utilization of Arduino Uno technology in creating intelligent and sustainable solutions for waste management.

Main engine is the main driving engine. This research develops a ship's main engine temperature monitoring system using the Blynk application. Thermocouple sensors measure engine temperature and IR Sensor measure RPM, the data is processed by ESP32, and sent to the Blynk cloud server, this research aims to determine the design of a prototype intelligent monitoring system to detect the temperature of a ship's main engine using a Thermocouple sensor, NodeMCU ESP32 which operates at 3.3V power is used as the processing core data. The system design in this research integrates Internet of Things (IoT) technology. Test factors for thermocouple temperature measuring devices and RPM sensors (IR Sensors). The test procedure uses a dynamo as a simulation of the main engine, tested for 5-10 minutes under normal conditions and 5 minutes when the dynamo feels warm to hot.