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The old bridge structure, are subjects that must be monitored in bridge maintenance. Bridge detection supervision is very necessary to determine structural deformation caused by normal operations or environmental impacts such as temperature, humidity and heavy vehicle loads. Monitoring the structure as a whole also needs to be carried out after extreme conditions occur, such as an earthquake. Research on structural dynamics analysis of steel frame bridge conditions to determine the behavior of the structure. Vibration data is read using a wireless sensor network or accelerometer. The vibration mode signal obtained is processed using Fast Fourier Transform (FFT) analysis via MATLAB software, which will produce a graph of the relationship between the frequency domain and the time domain. Through this graph, the dynamic characteristics of the bridge structure can be analyzed, with several variations in the condition of the bridge structure.

Pengelolaan air yang efisien merupakan aspek penting dalam mendukung kegiatan operasional di SMK Diponegoro. Tandon air yang selama ini dikelola secara manual sering menimbulkan kendala, seperti tandon meluap atau kosong akibat keterlambatan pengisian. Hal ini menyebabkan pemborosan air dan listrik serta meningkatkan beban kerja staf pengelola. Untuk mengatasi permasalahan tersebut, penelitian ini merancang sistem pendeteksi ketinggian air otomatis berbasis sensor ultrasonik dan Programmable Logic Controller (PLC). Sistem ini menggunakan sensor water level control untuk mendeteksi batas bawah dan atas ketinggian air, yang kemudian diolah oleh PLC untuk mengontrol pompa secara otomatis. Selain itu, data ketinggian air dikirim ke database dan ditampilkan pada web secara real-time. Metode logika fuzzy Tsukamoto digunakan dalam sistem kontrol untuk mengakomodasi ketidakpastian pembacaan sensor, dengan aturan berbasis bahasa alami seperti “air rendah”, “air sedang”, dan “air tinggi”. Hasil implementasi sistem ini diharapkan dapat meningkatkan efisiensi penggunaan air, mengurangi pemborosan, dan meringankan beban pengelola di lingkungan SMK Diponegoro.

The growing demand for natural, sustainable personal care products has driven interest in perfumes formulated with plant-based essential oils and natural fixatives. This study aims to develop and evaluate base-note perfumes using essential oils extracted from three Indonesian aromatic leaves—lemongrass (Cymbopogon citratus), basil (Ocimum basilicum), and kaffir lime (Citrus hystrix)—combined with jasmine (Jasminum sambac) oil as a fixative. Essential oils were extracted by hydrodistillation, and three formulations were prepared, each containing a 30:70 blend of essential oil to ethanol. The perfumes underwent organoleptic testing by 15 trained panelists, along with evaluations of fragrance longevity (over 12 hours) and stain effects on paper substrates. Among the formulations, the blend of kaffir lime and jasmine (Formula 2) exhibited the highest average scores across sensory parameters, including fragrance intensity and freshness. It also retained a perceptible aroma for more than 12 hours and showed minimal staining. The results indicate that natural perfumes made with local essential oils and jasmine fixatives can meet consumer expectations for quality and performance. This study highlights the potential of Indonesian aromatic plants in sustainable perfumery and suggests avenues for future innovation in natural fragrance design.

The addition of butterfly pea (Clitoria ternatea) extract which contains antioxidants into pasteurized milk is expected to increase the intake of antioxidants in the body. This research was conducted to evaluate the effect on pH, color, organoleptic chracteristic, and antioxidant activity of pasteurized milk. This study used a completely randomized design (CRD) with 5 treatments and 4 replications. The results of this study showed that increasing concentration of extract decreased pH, brightness, redness and yellowness. P3 was the most preferred treatment according to sensory evaluation. The antioxidant activity increased proportionally with extract concentration, reflected in decreasing IC50 values from 116.54 ppm (P0) to 19.06 ppm (P4). These findings indicate that the enrichment of pasteurized milk with butterfly pea extract effectively enhances its antioxidant capability while maintaining favorable sensory properties, particularly at moderate extract concentrations.

The DC motor serves as the main drive of the vessel and is equipped with a rotary encoder that functions to regulate the movement of the sensor in measuring sediment levels. This rotary encoder is also used to monitor and represent the rotational speed of the DC motor. System testing was carried out by implementing a Fuzzy Logic Controller (FLC) algorithm to control the DC motor speed in moving the vessel, ensuring stable motion. This fuzzy logic–based approach is expected to improve accuracy and efficiency in sediment volume calculations, while also reducing potential errors that commonly occur in manual methods. Simulating motor speed control using the fuzzy logic algorithm in MATLAB, the best test results were achieved over several trials, with a rise time of 376.310 ms and an overshoot of 83.33%. Motor speed measurements using both a tachometer and Arduino produced consistent results, with an average relative error of 0.18%.

Background: Hemorrhagic stroke is a critical condition caused by the rupture of cerebral blood vessels, which leads to intracranial bleeding, increased intracranial pressure (ICP), and decreased adaptive capacity of the brain. One of its main impacts is a significant disturbance of consciousness. Nurses have applied non-pharmacological approaches, such as bimodal sensory stimulation, as innovative interventions to stimulate neurological function and indirectly stabilize intracranial pressure. Objective: This study analyzed the implementation of bimodal sensory stimulation in improving intracranial adaptive capacity in patients with hemorrhagic stroke. Methods: This case study involved a patient with a hemorrhagic stroke and impaired consciousness in an intensive care unit. The intervention was conducted for three consecutive days by providing auditory stimuli (voices of family members and nurses) and gustatory stimuli (lemon) five times daily. Measurements included the Glasgow Coma Scale (GCS) and neurological response observations. Results: After receiving bimodal sensory stimulation therapy, the patient showed improved consciousness, as indicated by a consistent increase in GCS scores from 9 to 11 over the three days. The patient's responses to auditory and gustatory stimuli also improved, including mild motor movements, pupil reflexes, and simple interactions such as blinking or opening the eyes in response to sounds. Conclusion: Bimodal sensory stimulation effectively facilitated the recovery of consciousness and enhanced intracranial adaptive capacity in patients with hemorrhagic stroke. Nurses can integrate this intervention into evidence-based nursing practice in intensive care units with active family involvement.

Manual post-harvest paddy stirring requires significant time and labor and often results in uneven mixing, which can affect grain quality. To address this issue, this study designed and implemented a prototype of an Internet of Things (IoT)-based paddy stirring robot to simplify the process and improve efficiency. The system utilizes an ESP32 microcontroller as the main controller, DC motors as the stirring mechanism, and an IoT module for wireless connectivity to a mobile application. The research stages included hardware design, control system programming, IoT platform integration, and performance testing. Testing was conducted to evaluate response time, mixing uniformity, and power consumption. The results showed that the system could be operated remotely via a local Wi-Fi network with an average delay of less than 1 second, enabling real-time control. The prototype successfully stirred 0.3 kg of paddy with a mixing uniformity rate of 92% and an average power consumption of 12 watts. The application of IoT in the paddy stirring mechanism significantly improved time efficiency, reduced manual labor requirements, and maintained grain quality compared to traditional methods. These findings indicate the potential for further development into a large-scale automated paddy processing system with integrated humidity and temperature sensors for real-time quality monitoring, supporting the modernization of post-harvest processing through digital technology.

Waste management remains a major challenge, particularly due to the lack of public awareness and habits in sorting waste from its source. Innovative technology-based solutions are needed to support more effective waste management systems. This study aims to design and develop an Internet of Things (IoT)-based smart trash bin capable of automatically sorting metal and non-metal waste. The system utilizes an ESP32 microcontroller as the main controller, an LJ12A3-4-Z/BY sensor to detect metal materials, an E18 sensor to detect non-metal materials, and an ultrasonic sensor to monitor bin capacity. The waste separation mechanism is operated by a servo motor controlled by the system, while the Blynk application is integrated for real-time monitoring and full-capacity notifications sent directly to the user’s smartphone. The research stages included hardware design, microcontroller programming, IoT platform integration, and functionality testing to ensure system performance and reliability. The results showed that the smart trash bin operated as expected, successfully identifying and separating metal and non-metal waste automatically, while also sending real-time notifications to the user when the bin approached full capacity. The implementation of this technology has the potential to modernize waste management processes, improve the efficiency of waste sorting, and reduce dependency on manual labor. Furthermore, this research opens opportunities for future development by integrating additional sensors and cloud-based data management systems to support smart city initiatives and sustainable waste management practices.

Adolescent girls are the most vulnerable group to iron deficiency during growth and menstruation. An innovative snack product gyoza substituted with tuna and added moringa leaves can be developed to increase protein and iron content. This study aimed to determine the effect of tuna substitution and moringa leaf addition on sensory evaluation of gyoza’s color, aroma, texture, and taste as a snack for adolescent girls with iron deficiency anemia. The research is a pure experimental research with a completely randomized design. The sensory test data collection technique was carried out on 35 untrained panelists. The 4 formulations developed, namely F1 (25g tuna substitution with the addition of 10g moringa leaves), F2 (50g tuna substitution with the addition of 10g moringa leaves), F3 (75g tuna substitution with the addition of 10g moringa leaves), and F4 (100g tuna substitution with the addition of 10g moringa leaves). Then it was analyzed using the Kruskal Wallis test and the Mann-Whitney test as a further test. The results of the four formulations showed that there was an effect of tuna substitution with the addition of moringa leaves on the texture and taste sensory test however, there was no effect of tuna substitution with the addition of moringa leaves on the color and aroma sensory test and the best product was obtained in F3. The results of the gyoza nutritional content test that was substituted with tuna and the addition of the best moringa leaves or F3 formula based on laboratory testing had a nutritional content of 11,25% protein and 0,022% iron per 100 grams of gyoza. It is hoped that further research will modify the amount of moringa leaves added to improve the organoleptic test of gyoza products by panelists and examine gyoza formulations for other nutritional content that has not been tested and economic value.

Android continues to innovate, and biometric systems particularly fingerprint sensors must operate with high efficiency. However, the main challenge lies in the communication between hardware and software, which often causes high latency, excessive power consumption, and protocol incompatibility between components. As a result, authentication speed decreases and system stability is compromised, especially on mid- to low-range devices. This study explores various implementations of Android-based fingerprint systems, focusing on how sensor modules interact with the microcontroller or Trusted Execution Environment (TEE) and the operating system through a hardware-software co-design approach to evaluate integration efficiency across all layers. The analysis reveals that conventional protocols such as Bluetooth or serial connections still cause delays, while improvements in drivers and the Hardware Abstraction Layer (HAL) can significantly reduce latency. As a solution, the researchers propose a co-design optimization approach that utilizes data flow normalization within the HAL and adopts lightweight communication protocols to accelerate the verification process. Based on the test results, this approach successfully improves efficiency—authentication time is reduced by up to 35% and power consumption decreases by approximately 15%. Therefore, the efficiency of communication between hardware and software becomes a key factor in enhancing the performance and reliability of fingerprint systems on Android devices.

In the modern industrial world, robots like mechanical arms in automotive factories or packaging lines must move quickly and safely. For this, a real-time operating sistem (RTOS) is needed—think of it as a "super-fast brain" that ensures the robot reacts instantly to commands, without delays or errors. This paper analyzes popular RTOS like FreeRTOS and VxWorks for controlling industrial robots, focusing on evaluating performance (speed of operation) and stability (long-term reliability). We conducted tests in a lab using a simple robot that moves its arm to pick up objects. Performance was measured by response time (how quickly the robot stops when encountering an obstacle, ideally under 10 milliseconds) and throughput (how many tasks it can complete per second). Stability was checked through simulations of disruptions, like heavy loads or interfered sensor signals, using metrics such as error rate and time variation (jitter). Results show that FreeRTOS is more efficient for small, affordable robots, with high performance (average response time of 4 ms) but moderate stability (5-10% errors during overload). In contrast, VxWorks excels in stability (errors <2%, stable for up to 95% of tasks on time) for large factory robots, though it requires stronger hardware. Our analysis uses simple models like graphs and repeated tests, without complex formulas, to prove that the right RTOS can boost production efficiency by up to 25% and reduce accident risks.

Unmonitored traffic conditions often hinder decision-making processes in traffic management, particularly on secondary roads. Jalan Raden Inten II in East Jakarta is one of the connecting routes with heavy traffic activity at certain times, yet no integrated data-based monitoring system is currently available. This study proposes an Internet of Things (IoT)-based traffic condition classification system to identify Clear, Normal, or Congested states based on vehicle counts and speed categorization. The system is designed using an ESP32 microcontroller, an HB100 sensor to detect vehicle speed, and two AJ-SR04M ultrasonic sensors to detect vehicle presence. Data on vehicle counts and the percentage of slow-moving vehicles are periodically transmitted to the ThingSpeak platform and processed using the Threshold-Based Classification method. The classification results are visualized on a dashboard-based website equipped with charts, traffic condition status, and notifications when consecutive congestion is detected. Testing was conducted using simulation data over a specific period. Qualitative validation was carried out by comparing the classification results with traffic indicators from Google Maps. The results show that the system can classify traffic conditions with a good degree of agreement with external references, although discrepancies occurred at certain times due to the limitations of simulated data. This research demonstrates that a simple IoT approach can provide an affordable and effective solution for monitoring and classifying traffic conditions, with potential for real-world implementation in future studies.

Conventional attendance systems often face various problems such as inefficiency, inaccuracies in attendance logging, and limitations in recapitulation processes. Manual systems are prone to human error and time-consuming, while fingerprint-based systems may fail when the sensor is affected by dirty, wet, or damaged fingers. This study aims to develop an attendance system based on Artificial Intelligence (AI) by utilizing the face_recognition function in Python and implementing a microservice architecture to improve efficiency and accuracy in attendance recording. The system is developed using the Agile Feature-Driven Development (FDD) method, which focuses on building system features based on prioritized business values. This method is applied within the Software Development Life Cycle (SDLC) to ensure a structured, iterative, and user-oriented development process. Facial recognition is performed by comparing the encoding of the captured face image with the data stored in the database. The results show that the system is capable of recording attendance automatically, accurately, and in real-time. Furthermore, the recapitulation process becomes more efficient as the system manages and presents the data systematically.

Initial survey results show that consumers in the Makale region have specific preferences for tempeh packaging and quality, but business actors have not incorporated these preferences into their production and marketing strategies. This gap calls for an intervention to develop a strategy based on a systematic, contextual mapping of consumer preferences. Using a mixed methods approach with an explanatory sequential design, the study was conducted in two stages: first, quantitative data collection and analysis, followed by qualitative data collection and analysis to clarify and deepen the results. The study found that consumer preferences for Jeslyn tempeh are primarily influenced by sensory qualities (taste, texture, aroma), cleanliness, affordability, hygienic packaging, distribution ease, and promotion. The Likert scale analysis showed that most indicators had an average value above 4.20 (percentage index > 84%), indicating high to very high consumer satisfaction. Taste (mean 4.52; index 90.4%) and texture (mean 4.44; index 88.8%) were the most influential factors, followed by price and cleanliness. Consumer loyalty is built through consistent quality and emotional attachment to local products. Producers should maintain product quality, improve hygiene, introduce simple innovations, and use social media for cost-effective promotion.

This study aims to investigate the impact of packaging type and storage temperature on the physical, chemical, and sensory properties of chili blocks during storage. Additionally, the study aims to identify the optimal packaging and storage temperature combination, as well as to characterize the quality attributes of chili blocks. The experimental design employed was a Completely Randomized Design (CRD) in a factorial arrangement with two factors: packaging type and storage temperature. In this study, using aluminum foil as primary packaging, PP plastic packaging, and paper as secondary packaging, and 3 treatment storage temperatures, each repeated as many as 3 trials, so that it has 18 experimental units. Data analysis using ANOVA with Duncan's Multiple Range Test (DMRT)at a significant level of 5% using the SPSS18 program. The results showed that secondary packaging types and storage temperatures did not significantly affect physical properties (moisture content) and organoleptic properties (color, aroma, texture), but significantly affected the chemical properties (pH, vitamin C, ash content) and secondary packaging types the good for chili block is alufo + plastic packaging and refrigerator storage temperature (10oC), and good chili block characteristics are: Refrigerator storage (temperature 10oC) with alufo + plastic packaging: lowest physical properties (moisture content) 27.29, chemical properties (lowest pH 5.23, Vitamin C 12.91 and ash content 6.35).

This study investigates the influence of coffee brewing device branding on the sensory perception of coffee flavor at TEFA Javanica, Politeknik Negeri Banyuwangi. Focusing on three V60 dripper brands—Hario (Japanese brand), Indotara (local generic brand), and One Two Cup (local brand with English naming)—the research employs an experimental quantitative approach combining blind and branded taste tests. The blind test eliminates brand influence, allowing assessment of the inherent coffee taste, while the branded test examines how brand expectations affect flavor experience and consumer perception. Sensory evaluation follows the Specialty Coffee Association's standardized cupping protocol, assessing aroma, taste, aftertaste, balance, and overall impression. Data from 45 purposively selected experienced coffee tasters were analyzed using the McNemar test to statistically compare preferences between blind and branded conditions. Results show significant brand effects on consumer perception for two of the three brands tested, highlighting branding’s role in shaping sensory experience. These findings offer valuable insights for coffee producers and marketers to optimize brewing device selection and branding strategies, enhancing customer satisfaction and competitive positioning. Academically, the study enriches literature on sensory branding in coffee, emphasizing the importance of equipment branding in specialty coffee markets. This research also lays groundwork for future studies exploring marketing and consumer psychology aspects within the specialty coffee industry.

Bell's Palsy is a sudden onset of peripheral facial nerve paralysis that is generally unilateral. This condition is characterized by facial muscle weakness, asymmetry, difficulty closing the eyes, and facial expression disorders. Associated risk factors include exposure to cold temperatures and the habit of chewing ice cubes, which can trigger vasospasm of the blood vessels and inflammation of the facial nerve.  Physiotherapy management is important to accelerate recovery, prevent complications such as synkinesis and contractures, and improve the patient's quality of life. This study aims to describe the implementation of physiotherapy in cases of Bell's Palsy at PKU Muhammadiyah Selogiri Hospital and evaluate the effectiveness of physiotherapy intervention on the recovery of facial function in patients. A case report was conducted on a 41-year-old female patient diagnosed with right-sided Bell's Palsy. The examination included anamnesis, static and dynamic inspection, palpation, basic movement examination, sensory examination, and assessment using the House-Brackmann Facial Grading System (HBGS). Physiotherapy interventions were given three times a week, including Infra Red (IR), Transcutaneous Electrical Nerve Stimulation (TENS), light massage, facial exercises (raising eyebrows, closing eyes, blowing, smiling), and education on preventing exposure to cold. Evaluation was performed using Manual Muscle Testing (MMT) and Ugo Fisch. The implementation of physiotherapy with a combination of IR, TENS, massage, and facial exercises is effective in gradually improving the facial function of patients with Bell's Palsy. Physiotherapy also plays a role in educating patients about the prevention of recurrence related to environmental risk factors and lifestyle.

The braking system was a crucial component in ensuring the safety of motor vehicles on the road. Brake failures, such as brake fade or total malfunction, could result in fatal accidents. Therefore, an accurate and realistic testing method was necessary to measure the effectiveness of a vehicle’s braking system. This study aimed to develop a deceleration measuring device based on GPS, which could be used in road test methods. The device was designed using an ESP32 microcontroller combined with a BN-220 GPS module, RTC real-time clock sensor, 16x2 LCD, thermal printer, and microSD storage media. Testing was conducted on a Mitsubishi L300 vehicle at the Depok Beach runway in Yogyakarta, with initial speed variations ranging from 10 to 80 km/h. Calibration results showed that the device had an accuracy of 99.64% with an average error of 0.36%. Meanwhile, the test results recorded deceleration values ranging from 0.74 to 3.26 m/s², depending on the initial speed and braking conditions. Based on these findings, the developed device proved to be accurate and feasible as an alternative method for field testing of braking systems.

Physiotherapy plays an important role in promotive and preventive efforts through education for the community, including children’s health posts (posyandu). Children aged 1–5 years often experience eating difficulties that reduce appetite, increasing the risk of malnutrition and growth retardation. Low appetite is often related to oral-motor function disorders, such as difficulty chewing or swallowing. This activity aimed to increase mothers’ knowledge about oral-motor-sensory stimulation as an effort to improve children’s appetite. The community service program was conducted at Posyandu RW 02, Kota Lama Subdistrict, Malang City, involving 15 mothers or caregivers of toddlers. The method included physiotherapy counseling using interactive discussions and poster media, with knowledge evaluation through pre-test and post-test. The results showed a significant improvement in participants’ knowledge, from 0% in the pre-test to 85% in the post-test. This counseling effectively enhanced mothers’ understanding and practical skills in performing oral-motor-sensory stimulation independently at home. With regular implementation, this stimulation is expected to increase children’s appetite, improve nutritional status, and support optimal growth and development. Moreover, it reinforces the role of physiotherapy in preventing nutritional problems and improving the quality of life of children from an early age.

Work safety is always a priority, especially in the mining world which has a high potential for danger. Therefore, there are many regulations that must be obeyed, including the prohibition of smoking in the cabin of the Dump Truck unit. This study aims to design a cigarette smoke detector in the cab of a dump truck as a form of warning to drivers who violate the ban on smoking in the cab. The method used in this research is a quantitative method by conducting experiments. The research conducted is to analyze the effect of smoke thickness levels (20%, 40%, and 60%) on different sensor types (MQ-4 and MQ-7). The expected result is the effect of smoke thickness variation on the response time of different sensors.