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This study discusses the design and development of an automatic safety system for a wood cutting machine using Arduino Uno, a PIR (Passive Infrared) sensor, and a servo motor as the main components. The system is designed to automatically stop the movement of the wood cutting machine when human motion is detected around the cutting area, thereby minimizing the risk of work-related accidents. The research method includes hardware design, microcontroller programming, and system response testing using two types of test objects: the human body and a wooden block. The results show that the system operates according to the programmed logic. When the PIR sensor detects human motion, the servo motor stops and the red LED lights up as a danger indicator. In contrast, when no human motion is detected, the servo motor continues to move normally and the green LED remains on as a safe indicator. The system’s average response time is 0.6 seconds, indicating a fast and accurate performance. Therefore, the designed system is considered effective and can serve as a prototype of a simple safety tool to enhance operator safety in wood cutting machines.

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.

This research focuses on the synthesis and characterization of the double salt copper(II) ammonium sulfate hexahydrate, Cu(NH4)2(SO4)2·6H2O. The study aims to obtain the compound in crystalline form and evaluate its properties through yield calculation, solubility testing, and Fourier Transform Infrared (FTIR) spectroscopy. The synthesis involved reacting copper(II) sulfate pentahydrate (CuSO4·5H2O) with ammonium sulfate ((NH4)2SO4) under controlled conditions, followed by crystallization. The process produced 10.84 grams of crystalline Cu(NH4)2(SO4)2·6H2O with an 86.23% yield, indicating efficient synthesis. Solubility tests showed that the crystals were polar, soluble in polar solvents like water and hydrochloric acid (HCl), partially soluble in ammonium hydroxide (NH4OH), and insoluble in less polar solvents like ethanol and chloroform (CHCl3). FTIR analysis confirmed the presence of functional groups such as O–H, N–H, and S–O stretching vibrations, supporting the proposed molecular structure. The findings demonstrate that Cu(NH4)2(SO4)2·6H2O can be efficiently synthesized, and its physicochemical properties align with theoretical expectations. This study contributes to the understanding of double salt synthesis and characterization, relevant for inorganic chemistry, material science, and potential applications in catalysis and coordination chemistry.

Pneumonia ialah penyakit Infeksi Saluran Pernapasan Bawah yang bersifat akut pada parenkim paru meliputi alveolus dan jaringan interential yang dikarenakan mikroorganisme seperti jamur, virus dan bakteri. Mikroorganisme yang masuk ke saluran pernafasan bagian bawah dapat mengganggu proses pernapasan serta membuat saluran pernapasan tidak berfungsi secara optimal, sehingga proses keluar masuk oksigen juga terhambat dan mengakibatkan gangguan pada pola napas. Beberapa permasalahan lain yang ditimbulkan dari pneumonia seperti adanya penumpukan sputum, otot bantu pernapasan, penurunan ekspansi thoraks dan juga nyeri dada. Dalam perawatanya, tenaga medis berperan dalam pemberian antibiotik selama 8 jam setelah pasien mengalami perawatan serta fisioterapi berperan dalam pemberian intervensi penyinaran Infrared, Chest physiotherapy, dan Myofascial release. Penelitian ini bersifat studi kasus yang mengangkat satu kasus pasien serta mengumpulkan data melalui proses fisioterapi. Intervensi fisioterapi dilakukan sebanyak 4 kali pertemuan di Rumah Sakit dr. Ario Wirawan, Salatiga dengan menggunakan Infrared, Chest physiotherapy yang meliputi Postural Drainage, Clapping, Vibrasi, Latihan Batuk Efektif, Pursed Lip Breathing dan Myofascial release. Setelah menjalani 4 kali terapi, diperoleh hasil kondisi pasien yang mengalami penurunan nyeri dada, penurunan tingkat sesak napas, peningkatan ekspansi thoraks, berkurangnya spasme otot bantu pernapasan dan retensi sputum.

Methane gas detection is crucial in the oil and gas sector to enhance safety and operational efficiency. This study examines the impact of three types of gas detectors—catalytic, infrared, and ultrasonic sensors—on accuracy and response time. The research was conducted at PT PHM's onshore and offshore sites to evaluate sensor performance in operational environments. A quantitative approach with direct field observation was used. Data were collected by measuring methane gas concentrations indicated by detectors, which were then compared to standard gas concentrations. Response times were recorded when the detectors were exposed to methane concentrations of 2.5% LEL until the alarm triggered at 40% of full scale. Data analysis was performed using descriptive statistics, homogeneity test, normality test, ANOVA, and post hoc tests. The results show that the infrared detector had a response time of 2.87 seconds with an accuracy of 0.218%. The catalytic detector had a response time of 8.91 seconds and accuracy of 0.489% and the ultrasonic detector had a response time of 6.15 seconds and accuracy of 0.842%. Overall, the infrared detector demonstrated the best performance in response time and is recommended for use at PT PHM.

Predictive Maintenance is a maintenance activity that focuses on monitoring equipment conditions in real-time and analyzing data to predict potential failures before they occur, allowing repairs to be made in a timely manner before major damage occurs. One of the methods used in predictive maintenance is "Infrared Thermography” or use of technology thermal imaging technology. In the context of predictive maintenance, thermography can be used to identify problems that are not visible to the naked eye, such as poor electrical connections, excessive heat buildup, or damage to components that cause heat leakage The purpose of this study was to determine the implementation of Predictive Maintenance with Infrared Thermography method on electrical equipment at PT PHM. The method used in this research is the observation method with primary and secondary data collection. The results showed that the implementation of predictive maintenance with the Infrared Thermography method on electrical equipment and systems at PT PHM was effective in helping the company avoid unnecessary costs and improve operational efficiency. Predictive maintenance allows companies to perform maintenance to identify potential damage before it occurs and can take preventive action so as to reduce repair costs, and operational productivity.