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Abstract Kebakaran merupakan salah satu bencana yang dapat mengancam keselamatan jiwa dan harta benda, khususnya di lingkungan hunian padat seperti rumah kos. Kos Putri Kanaya Projo merupakan salah satu kos putri di Ungaran Timur, Kabupaten Semarang, yang berisiko tinggi mengalami kebakaran akibat kelalaian penghuni dalam penggunaan peralatan listrik maupun kompor gas. Penelitian ini bertujuan untuk merancang dan membangun sistem pendeteksi kebakaran dini berbasis mikrokontroler dengan dukungan teknologi Internet of Things (IoT).  Sistem dikembangkan menggunakan mikrokontroler ESP32 yang terhubung dengan sensor MQ-2 (asap/gas), sensor PIR (api), dan sensor DHT22 (suhu/kelembapan). Output sistem berupa notifikasi peringatan pada aplikasi Blynk, buzzer sebagai alarm suara, serta tampilan informasi melalui LCD. Metode penelitian yang digunakan adalah prototyping dengan tahapan perancangan, implementasi, pengujian, serta penyempurnaan sistem. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi asap, gas, suhu tinggi, dan api dengan akurasi di atas 90% serta memberikan notifikasi peringatan melalui aplikasi Blynk dengan waktu respon kurang dari 10 detik. Dengan demikian, sistem ini efektif sebagai solusi deteksi dini kebakaran pada lingkungan kos sehingga dapat meningkatkan keamanan dan meminimalisir risiko kerugian material maupun korban jiwa

The main problem with the water heating system on offshore platforms is the absence of water level monitoring and automatic overflow detection. This has the potential to cause hot water spills that endanger workplace safety and operational efficiency. This research designs and implements a water level monitoring system based on the Arduino Uno microcontroller with HC-SR04 ultrasonic sensors. The system is equipped with LED indicators, a buzzer alarm, and a 16x2 LCD to display water level status in real-time. Water levels are classified into three zones (low, medium, high), and overflow is detected if the water is within 3 cm of the sensor. Testing was conducted on a 5-liter simulation tank representing actual 500-liter tank conditions. Test results showed a reading accuracy of 96% and a quick system response to overflow conditions (<1 second). This system is economical, easy to develop, and highly applicable for offshore environments. In addition, this system can be integrated with IoT technology for remote monitoring.

Penelitian ini mengembangkan sistem pemantauan berbasis Internet of Things (IoT) untuk mengoptimalkan kinerja Mini PC dan pemeliharaan real-time di CV Permata Gemilang Jaya. Metodologi waterfall diterapkan menggunakanNodeMCU sebagai mikrokontroler utama, dilengkapi dengan sensor DHT22, DS18B20, dan INA219 untuk memantau parameter suhu, CPU, dan memori. Arsitektur sistem mengintegrasikan kerangka kerja Laravel dengan database MySQL, menghasilkan aplikasi web responsif dengan kontrol akses berbasisperan untuk Admin Pusat, Admin Regional, dan Teknisi Cabang. Infrastrukturserver cloud dengan konektivitas GSM cadangan memfasilitasi pemantauanterpusat di wilayah Ciayumajakuning. Desain sistem menggunakan Unified Modeling Language (UML) dengan diagram kasus penggunaan dan diagram aktivitas yang komprehensif. Penerapan sistem pemberitahuan otomatisdengan mekanisme peringatan berbasis ambang batas memungkinkan deteksidini anomali perangkat. Antarmuka yang dioptimalkan untuk selulermeningkatkan aksesibilitas teknisi untuk operasi lapangan. Validasi sistemmenunjukkan strategi pemeliharaan preventif yang sukses dalam mengurangiwaktu henti perangkat dan mengoptimalkan efisiensi operasional infrastrukturteknologi informasi.

Carpal tunnel syndrome (CTS) is the most common chronic neuropathy of the upper limb, characterized by compression of the median nerve within the carpal tunnel. It typically results from repetitive hand movements or trauma and leads to pain, numbness, and weakness, making it a significant occupational health concern. Increased intracarpal pressure causes venous stasis, edema, and ischemic damage, which slow nerve conduction and are detectable through electrodiagnostic (EDX) studies. Recently, ultrasonographic measurement of the median nerve cross-sectional area (CSA) has been proposed as a noninvasive diagnostic option. This study aimed to evaluate the diagnostic accuracy of ultrasound-based CSA measurement compared with EDX findings in CTS patients. The research was conducted at Al-Imamain Al-Kadhimain Medical City in Baghdad from November 2024 to March 2025 and included 100 patients (200 hands). All individuals underwent both EDX and high-resolution ultrasonography using a 5–13 MHz linear probe, with CSA calculated by the direct tracing method. CTS was confirmed in 102 hands (51%). Affected hands demonstrated significantly prolonged distal motor and sensory latencies, reduced amplitudes, and lower conduction velocities (p < 0.001). Mean CSA was significantly larger in CTS hands (13.75 ± 3.95 mm²) than in non-CTS hands (10.15 ± 3.33 mm², p < 0.001). ROC analysis produced an AUC of 0.776 and an optimal cutoff of 11.5 mm² (72% sensitivity, 76% specificity). CSA also increased with CTS severity. Moderate accuracy was observed when differentiating mild from moderate CTS at a 12.5 mm² cutoff, and moderate from severe CTS. In conclusion, median nerve CSA measurement by ultrasound is a reliable, noninvasive, and rapid tool for diagnosing and grading CTS, complementing EDX assessment.  

The distinctive markings on the fingers are known as biometrics. The fingerprint attendance method uses biometrics. Without developing a usable fingerprint tool, research on fingerprint attendance is restricted to processing attendance data. As a result, an Arduino-based fingerprint approach was used to construct an attendance tool. The education sector is where SMK Negeri 1 Indralaya Utara. The school's student attendance system is still operated manually. In this study, the system uses fingerprints and camera recording to track students' locations. Using an Arduino Mega as a microcontroller, this tool will display student attendance information on an LCD, save it on an SD card module, and send the ESP32CAM-generated photographs over Telegram. The literature review is the first step in the research process, followed by hardware and software design. Following completion, the tool is put through testing to evaluate its functionality. Each of the ten participants scanned their thumb and index finger. The thumb and index finger have an average of five tests, with a 0.28% mistake rate, depending on the number of trials. Each component's error percentage is less than 1% while still falling within the 5% tolerance limit. The image test results indicate two conditions: 20 cm and greater. The ESP32CAM will activate when we take a picture at a distance of 20 cm.

This article explores the application of the Deep Learning approach in inclusive Islamic Religious Education (PAI) for children with special needs (ABK), with a focus on improving the understanding of abstract concepts such as monotheism and morals. The main problems faced by ABK in PAI learning include difficulties in understanding religious concepts due to cognitive, emotional, and sensory challenges. The purpose of this study is to analyze the urgency of Deep Learning in improving the spiritual understanding, motivation, and well-being of ABK through more personalized and reflective learning. The methods used include reflective strategies such as journals and discussions, adaptive media in the form of animation and audio, and collaborative learning with peers. The findings show that the application of Deep Learning improves ABK's in-depth understanding of religious values, as well as spiritual motivation and the ability to apply ethics in everyday life. The conclusion of this study is that Deep Learning can be an effective solution in inclusive PAI learning, supporting teachers in developing more humanistic learning designs and focusing on the character building of ABK.

Motorcycle theft remains a widespread issue that demands improved security solutions supported by modern technology.This research discusses the development and implementation of a motorcycle security system design using an ESP8266 microcontroller integrated incorporating the GPS Neo-7M positioning module, SW-420 vibration detection sensor, and an electronically controlled solenoid lock. The  implemented mechanism is monitored and controlled via the Telegram application, allowing the owner to receive real-time updates about the motorcycle’s status through a Telegram Bot. The development process involves hardware integration, ESP8266 programming, and functional testing. The SW-420 sensor detects vibrations as indicators of possible theft, triggering a buzzer alarm and sending instant notifications through Telegram. The GPS Neo-7M module provides location data in real time, automatically or upon user request, through a Google Maps link. Users can also lock or unlock the motorcycle remotely by sending the lock or /unlock commands via Telegram. Test results show that the system responds to vibrations in less than one second, delivers Telegram notifications within 3–5 seconds, and determines location with an accuracy of 3–10 meters. Overall, the proposed system offers an effective, practical, and low-cost solution to enhance motorcycle security.

This study reports the green synthesis of vanadium pentoxide (V₂O₅) using virgin olive oil as a natural and environmentally benign reducing agent. The approach aims to minimize the environmental impacts associated with conventional synthesis routes. Structural and physicochemical characterizations confirmed the successful formation of nanoscale V₂O₅. X‑ray diffraction (XRD) analysis indicated an average crystallite size of approximately 16.57 nm, evidencing high crystallinity. Fourier‑transform infrared spectroscopy (FTIR) revealed characteristic V=O and V–O–V vibrations with bands associated with physisorbed water, confirming the correct oxide framework. Field‑emission scanning electron microscopy (FE‑SEM) showed irregularly shaped nanoparticles with a representative particle diameter of ~32.62 nm. Brunauer–Emmett–Teller (BET) and Barrett–Joyner–Halenda (BJH) analyses yielded a specific surface area of 10.817 m²/g, a total pore volume of 0.024277 cm³/g, and a broad mesoporous distribution (20–90 nm). Energy‑dispersive X‑ray spectroscopy (EDX) confirmed the purity of V₂O₅ with weight fractions of V (69.40%) and O (30.60%), consistent with the stoichiometric composition. Overall, the results demonstrate the effectiveness of olive oil as a green reducing agent for preparing nanoscale V₂O₅, which is promising for catalysis, energy storage, sensors, and clean‑energy applications.

Coffe is one of Indonesia’s leading commodities, known for its diverse flavors and aromas. Traditionally, coffee quality assessment is conducted manually through cupping tests performed by expert panelists. However, this method is subjective and requires considerable time and cost. This study aims to implement an Artificial Neural Network (ANN) using the backpropagation algorithm to classify coffee types based on sensory parameters such as flavor, aroma, acidity level, and body. Simulated data were generated from five common Indonesian coffee varieties: Arabica Gayo, Robusta Lampung, Arabica Toraja, Liberica Jambi, and Excelsa. The results show that the ANN-based classification system with a 4-8-1 architecture achieved an accuracy rate of 93% after 500 training epochs, with a final error value of 0.07. The implementation of this method provides an efficient and objective technological alternative to assist the coffee industry in maintaining product quality and automatically identifying coffee types.    

This systematic literature review explores the application of the Stimulus-Organism-Response (SOR) model in understanding tourist behavior, focusing on empirical studies from 2019 to 2025. The tourism industry, a key economic driver globally, necessitates a deep understanding of tourist behavior to develop effective marketing strategies and enhance the tourist experience. The SOR model, which examines the impact of external stimuli on tourists' psychological and emotional responses, offers a comprehensive framework for analyzing tourist behavior. This review synthesizes findings from recent studies to highlight how external stimuli such as destination image, social media marketing, and sensory experiences affect tourists' perceptions, motivations, and behaviors, influencing their loyalty and intention to revisit destinations. It also discusses the role of internal factors, including satisfaction, trust, and tourist engagement, in mediating the response to external stimuli. The findings suggest that understanding these dynamics can help destination managers and tourism marketers design more targeted and sustainable marketing strategies. The review also identifies gaps in the current literature and provides recommendations for future research, particularly in the integration of digital technologies and the increasing importance of sustainability in tourism behavior.

The braking system is a crucial component in a vehicle, where its performance is highly influenced by the wheel's rotational speed and the geometry of the brake pad. This study aims to analyze the impact of wheel rotational speed and variations in pad geometry on the temperature of the brake pad. The braking process generates heat due to friction, which, if not properly managed, can reduce braking performance and accelerate brake pad wear. The experiment was conducted at four levels of wheel rotational speed: 1000 RPM, 1500 RPM, 2000 RPM, and 2500 RPM. The testing system was designed using a braking system simulator equipped with a speed sensor (LM393) and a temperature sensor (K-type thermocouple), which were connected to an Arduino microcontroller and displayed in real-time through a Graphical User Interface (GUI) in MATLAB. The test results indicated that both the geometric shape of the brake pads and the wheel rotation speed significantly affected the resulting temperature. Standard brake pads produced the highest temperature at a speed of 2500 RPM, reaching 63.33°C. In contrast, brake pads with holes offered the best performance by maintaining a lower temperature of only 43.00°C. Furthermore, an increase in wheel rotation speed led to a noticeable rise in temperature; for standard pads, the temperature increased from 36.67°C at 1000 RPM to 63.33°C at 2500 RPM. This demonstrates that RPM is a major factor in heat generation due to friction. The MATLAB GUI effectively visualized the relationship between RPM and temperature, facilitating the analysis and evaluation of the data.

Bread is a food product made from wheat flour, water, yeast, and salt processed by mixing them into one dough then fermented and baked in the oven until cooked. Roti has several types, one of which is burger buns. Burger patties are white bread that is round or oval, made from high protein wheat flour, yeast, sugar, bread improver, salt, butter, milk powder, water, and usually with the addition of sesame seeds. The purpose of this study was to determine the formulation of burger patties high in dietary fiber and preferred by consumers. This study used a two-factor Complete Randomized Design (RAL), with a ratio of wheat flour and mocaf 70:30, 60:40, 50:50 with the addition of the percentage of beet tubers 30,40,50. The results of burger buns with high-fiber formulations were obtained from the ratio of wheat flour and mocaf flour at 50%:50% with a percentage of beet puree at 50%, this treatment has a fiber content of 11.47%, the water content of 26.10%, ash content 1.58%, fat content 4.33%, protein content 6.53%, carbohydrate content 61.45%, calories 310.89%, specific development volume 4.42%. Of flavor 3,38, color sensory test 3.73, chewiness 3.81, tenderness 3.11. Burger buns with raw materials of mocaf flour and the addition of beet puree as a source of fiber can be one of the practical foods that have good nutritional value for health.

Technological advancements in manufacturing have greatly improved production efficiency, particularly through the use of advanced machinery. As key components of industrial operations, machines must operate optimally to meet production targets, often requiring continuous use. To maintain performance and reliability, regular maintenance in accordance with Standard Operating Procedures (SOPs) is essential. This study analyzes the maintenance management system at PT. XYZ, focusing on the Komatsu OBS-25T Power Press machine. The research evaluates the Preventive Maintenance strategy using Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR) as key performance indicators. The results show that the monthly maintenance schedule is effective. Between January 1 and April 1, 2024, several component failures occurred, including lamp, roll feeder, hand sensor, oil flow, memory slide, alarm No. 11, conveyor belt, and uncoiler malfunctions. The total MTBF was 171.387 minutes (average 13.183 minutes), and the total MTTR was 1.950 minutes (average 150 minutes). The machine of availability rate reached 99%, demonstrating high operational efficiency and effective maintenance management.

Power transformer theft, a pervasive issue disrupting critical infrastructure, necessitates the development of cost-effective and energy-autonomous security solutions. This paper presents the design and implementation of a detection-focused anti-theft framework that integrates a Raspberry Pi Zero W, camera module, and passive infrared (PIR) motion sensors powered by a solar system for continuous monitoring. The system is designed for remote, off-grid deployment, utilizing a headless Raspberry Pi powered by a 5V solar panel and power bank to ensure energy autonomy. Upon motion detection, captured images are processed on the edge device using OpenCV’s Haar Cascade classifier, optimized for upper-body detection to minimize false positives and verify human presence. Captured images are processed locally on the edge device using OpenCV’s Haar Cascade classifier to confirm human presence before an alert is sent to the mobile application, emphasizing real-time operation and low latency. Once an intrusion is confirmed, the images are saved locally and uploaded via the Secure File Transfer Protocol to a custom-developed Android application. The app provides a dedicated remote monitoring interface, enabling secure file transfer and system access, while providing users with immediate notifications and image management capabilities. The system emphasizes low power consumption, real-time operation, and low deployment cost. Tests over 200 triggered events under varied environmental conditions achieved 90% detection accuracy with an average latency of 4.5 s. Solar autonomy was maintained for approximately 24 h under normal operation. It is concluded that the integration of solar power, edge computing of images, and mobile monitoring provides a feasible, scalable, and financially viable framework for securing transformers, especially in resource-constrained environments.

Fantasy eyelashes are eyelashes that are used to add an accent of beauty to fantasy eye makeup. Currently, fantasy eyelashes are rarely sold on the market, and most of them are relatively expensive with simple designs. The scarcity of fantasy eyelashes at relatively high prices has led the author to innovate by utilizing mother-in-law's tongue leaf fibers as the basic material for making fantasy eyelashes. The research aims to simplify the search for fantasy eyelashes needed by the public at an affordable price while using natural materials that minimize the risk of irritation, thereby presenting an entrepreneurial opportunity. The natural material used is the mother-in-law's tongue plant. This plant is an environmentally friendly ornamental plant that is easily found in the author's surroundings, highly beneficial for health as it filters toxins and air pollution, and has high economic value. This research employs observation, literature review, experimentation, documentation, and questionnaires. The author uses descriptive qualitative analysis, presenting research results in narrative form using words and sentences. The author offers a price of Rp. 35,000 per pair, which is competitive with market prices for fantasy eyelashes. Product validation was conducted through sensory testing with three expert validators, covering aspects such as texture, shape, neatness, and a rating of 15 in the “very suitable” category. The level of preference was assessed through a preference test with 60 respondents, covering statements 1 to 8, with results ranging from 241 to 300 indicating “very liked.” This study concludes that fantasy eyelashes made from mother-in-law's tongue leaves can compete and are favored by makeup artists and practitioners. 

Purpose –This article aims to examine Territorial Defense Management (TDM) as an adaptive defense strategy in the contemporary era, with a focus on the synergy of UAV–sensor fusion–C2 technologies, whole-of-defense cyber orchestration and strengthening community resilience in facing multidimensional threats. Design/methodology/approach –This study employed a Systematic Literature Review (SRB) approach, using scientific literature sources from 2021–2025 from Scopus, IEEE, Springer, ScienceDirect, Garuda, and ResearchGate. Thirty-one primary articles were selected through rigorous screening and then analyzed using thematic coding and data triangulation to ensure the validity of the findings. Findings –The study results show that: (1) UAVs with sensor fusion increase the effectiveness of regional surveillance, although they require a counter-UAV system and good spectrum governance; (2) whole-of-defense cyber is proven crucial with the support of threat intelligence, territorial cyber operations centers, and AI for anomaly detection and automated response; (3) community resilience strengthens defense through emergency communications, early warning systems, and community participation in joint training with authorities. Practical implications –The effectiveness of MPT can be enhanced through standardization of IoMT architecture across the military services, counter-UAV and cyber exercises at the regional level, and the development of quantitative metrics to measure community resilience linked to military-level readiness. Originality/value –This article contributes by integrating technology, cyber, and community perspectives into a single adaptive MPT framework, thereby strengthening deterrence, rapid response, and socio-technological resilience. This approach is relevant for strengthening Indonesia's modern defense doctrine.

This study aims to examine the organoleptic storage life of kecicang chili sauce using vacuum storage. The research design is experimental, involving 3 trained panelists, all of whom are lecturers from the Culinary Arts Vocational Education Study Program. The data collection method used in this study was observation through organoleptic testing using a 5-point Likert scale, namely 5) Very Good, 4) Good, 3) Fairly Good, 2) Not Very Good, 1) Very Poor. Data analysis used quantitative descriptive methods. The results of this study indicate that: 1) Processing affects the quality of kecicang chili sauce, using a technique that involves grinding the ingredients using a chopper, cooking at a temperature of ±70°C, and then vacuum packing. The organoleptic test results showed that vacuum storage was able to maintain the sensory properties of the sambal, as assessed from the aspects of colour, distinctive kecicang aroma, kecicang texture, solid dough texture, and distinctive kecicang taste, which were rated “fairly good” based on colour, aroma, texture, and taste. Vacuum-sealed sambal maintained its best quality until day 3rd, with no signs of leaking. The most significant changes occurred in the flavor and aroma, rendering it unfit for consumption. The sambal packaging, which showed no signs of leaking, maintained its sensory quality until day 5.

The purpose of this study is to analyse the comparison of sensory characteristics between original putu ayu cake and a variant with a 25% substitution of kale leaf flour. The substitution of kale leaf flour is expected to serve as a natural alternative for coloring while also improving the cake's nutritional value. This research utilized a quantitative experimental approach, involving organoleptic testing with 62 panelists from the Brangsong Baru Housing. The panelists evaluated four sensory aspects: aroma, color, taste, and texture. The results from the organoleptic tests showed that substituting kale leaf flour positively influenced the aroma of the putu ayu cake without compromising its texture. However, the color and taste still require improvements to enhance the overall consumer acceptance of the cake. These findings highlight the potential of kale leaf flour as a functional ingredient in traditional cakes, offering a healthier alternative while maintaining the sensory qualities that consumers expect.

Mental health has become a major global issue, particularly after the COVID-19 pandemic, which significantly increased the prevalence of psychological disorders. Early detection of stress and other mental health problems remains a major challenge, as traditional methods are generally subjective and unable to provide real-time results. This study aims to design and test a wearable sensor based on Heart Rate Variability (HRV) as a physiological indicator for detecting stress levels. The research employed an experimental approach through the development of a wearable sensor prototype equipped with a stress detection algorithm based on HRV analysis, including both time-domain and frequency-domain parameters. The prototype was tested on 100 respondents with varying stress levels under controlled conditions. Instruments used in this study included the HRV sensor prototype, psychological questionnaires, and standard validation devices. Data were analyzed by comparing the sensor detection results with respondents’ psychological data and calculating prediction accuracy. The findings showed that the wearable sensor was able to predict stress conditions with an accuracy rate of 80%. The distribution of sensor detection results was generally consistent with psychological data, especially in the low-stress category, although slight deviations were observed in moderate and high-stress categories. These results demonstrate that an HRV-based wearable sensor can serve as a practical and non-invasive tool to monitor mental conditions in real time. The implications of this research highlight the potential of wearable technology as an innovative solution for mental health monitoring, both for individual use and as support for healthcare systems. Therefore, this study contributes to the development of adaptive and responsive health technologies in addressing global mental health challenges.

The development of digital technology and the Internet of Things (IoT) has transformed the paradigm of urban development towards the concept of a smart city based on data and connectivity. This study analyzes the nature of smart cities through three dimensions of the philosophy of science: ontology, epistemology, and axiology. From an ontological perspective, a smart city is understood as a complex entity consisting of physical, digital, social, and ecological systems that interact with each other to create efficient and adaptive urban governance. Epistemologically, knowledge in a smart city is obtained through the process of collecting, processing, and analyzing data from various IoT devices, sensors, and citizen participation, thereby producing new insights that support evidence-based decision-making (data-driven governance). From an axiological perspective, a smart city has ethical values and goals to improve the quality of life of its citizens, strengthen government transparency, maintain environmental sustainability, and promote inclusive social participation. Thus, this study asserts that the development of a smart city is not only technological but also has a philosophical foundation oriented towards a balance between efficiency, humanity, and sustainability.