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Abstract: Detecting chili leaf diseases remains challenging due to the non-uniform manifestation of symptoms, local discoloration, small lesion regions, and visual similarity between disease patterns and natural leaf background variations. Although YOLO-based detectors provide favorable computational efficiency, lightweight variants often struggle to distinguish subtle lesion characteristics, while conventional attention mechanisms such as CBAM primarily rely on global feature aggregation and may overlook regional activation variability. To address these limitations, this study proposes a YOLOv9s-based detection framework integrated with a Region-Dispersion Channel Spatial Attention (RDCSA) module. The proposed module incorporates regional dispersion statistics, namely mean, standard deviation, and range, as channel descriptors to capture inter-region feature variability before applying spatial attention refinement. Experiments were conducted on the COLD dataset containing 532 original images from five chili leaf condition categories using a split-before-augmentation protocol to ensure objective evaluation. RDCSA was integrated at the P5 feature level and evaluated through attention placement analysis, component-wise ablation, sensitivity analysis, stability assessment, and comparison with modern attention mechanisms. The proposed YOLOv9s + RDCSA model achieved an mAP@50 of 0.894, mAP@50–95 of 0.773, precision of 0.858, recall of 0.861, and an F1-score of 0.859 with only a marginal increase in model parameters. The results suggest that regional dispersion-based attention improves feature discrimination while preserving computational efficiency, particularly for disease symptoms characterized by heterogeneous spatial patterns. Nevertheless, performance remains influenced by visually ambiguous symptom categories, indicating that further validation across multiple datasets and field conditions is required. Overall, the proposed RDCSA module enhances detection capability without substantially increasing computational overhead, making it a promising attention mechanism for lightweight plant disease detection systems.

The increasing demand for electrical energy, particularly in offices and commercial buildings, has made energy efficiency a critical aspect of sustainable development. Among various building components, lighting systems are recognized as one of the major consumers of energy. This study investigates the potential for energy savings through the adoption of a smart lighting system incorporating IoT-based sensors, motion detectors, and dimming controls. Employing a quantitative descriptive approach, the research was conducted at the workspace of Indie Light, comparing energy consumption before and after the implementation of the system. Data were collected using direct observation, light and power meters, and real-time monitoring devices to ensure accurate measurement. The results demonstrate that smart lighting systems can substantially reduce energy use without compromising lighting quality or comfort. By integrating intelligent sensors and adaptive control algorithms, the system not only optimizes energy efficiency but also aligns with national policies on energy conservation, supporting broader environmental sustainability efforts. These findings suggest that smart lighting solutions can play a significant role in promoting energy-efficient practices in commercial spaces while contributing to sustainable development goals.

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.

The research with the title Application of Fire and Gas Detection Systems takes place at the North Field Expansion Project (NFXP). The methods used are direct observation, interviews, simulations and literature studies. The objectives of the study were to determine the application of fire and gas detection systems, and  to determine the challenges in the application of fire and gas detection systems in the North Field Expansion Project (NFXP). The results obtained  show that the Fire and Gas Detection System implemented at NFXP is in compliance with international safety standards, and the system is able to detect fire threats and gas leaks with high efficiency through advanced detector technology, automatic response, and integration with other safety systems such as IPS and HVAC. The effectiveness of the system is reflected in the ability to detect threats in less than 5 seconds, with detection coverage that meets the target of 90% by one detector and 85% by two or more detectors. The main challenges in the implementation of fire and gas detection systems include extreme environmental conditions, such as corrosion due to exposure to seawater and high temperatures, and false alarms that reach around 15%. Another obstacle is the lack of technical training for personnel in operating and maintaining the system.

High Performance Liquid Chromatography (HPLC) is a vital analytical technique used for separation, identification, and quantification of compounds in complex mixtures. This review explores the comparative performance of four detectors employed in the Chemical Laboratory of PT FIP: UV-Visible, Refractive Index (RI), Conductivity, and Evaporative Light Scattering Detector (ELSD). Each detector operates on distinct principles and is selected based on analyte characteristics such as the presence of chromophores, ionic nature, or volatility. The UV-Vis detector remains the most widely used due to its precision and compatibility with chromophoric compounds. However RI, Conductivity, and ELSD detectors serve as essential alternatives for analytes that lack UV absorption, non-ionizing, or exhibit low volatility. This review summarizes the key principles, advantages, limitations, and applications of each detector. The comparative evaluation highlights the strengths and constraints of each system, facilitating method optimization in pharmaceutical analysis.

Biosensors are analytical devices that combine biological sensing elements with physicochemical detectors to provide rapid, accurate, and cost-effective solutions for medical diagnostics. Globally, these devices have revolutionized the detection and monitoring of diseases, significantly impacting patient outcomes. In Indonesia, the adoption and development of biosensor technology have accelerated over the past decade, particularly in response to the growing healthcare needs and the government's push for technological innovation. This study aims to review and analyze the development and application of biosensors in Indonesia medical diagnostics sector between 2014 and 2024. The research focuses on identifying key technological advancements, the integration of biosensors into healthcare, the challenges faced in their development and deployment, and the prospects for future growth. The study employs a comprehensive literature review and analysis of scientific publications, industry reports, and government documents related to biosensor technology in Indonesia. The review covers the evolution of biosensing materials, point-of-care testing applications, integration with digital health technologies, and regulatory frameworks. Data were synthesized to provide a detailed overview of the current state of biosensor technology and its impact on medical diagnostics in Indonesia. The findings indicate significant advancements in biosensor technology, particularly in the development of novel nanomaterials, the integration of biosensors with mobile health (mHealth) platforms, and the expansion of point-of-care testing (POCT) for infectious diseases. However, the sector faces challenges, including technological limitations, regulatory hurdles, and economic constraints, which have slowed the widespread adoption of biosensors in clinical settings. Despite these challenges, the future of biosensors in Indonesia appears promising, with continued investment and innovation expected to drive further advancements in this field.

In this final project the author discusses the problem entitled "Design of Smoke Detector and Room Temperature of SAP ERP Server at PT Apac Inti Corpora". This design aims to support the improvement of the security system in the server room with smoke and temperature detectors and microcontroller-based automatic countermeasures. Against the background of problems with sensors and countermeasures systems in the server room and lack of insight into sensor installation. This tool uses a gas sensor MQ-2 as a smoke detector, a DHT-11 sensor as a temperature detector, Arduino as a microcontroller that controls I/O, buzzer and LED as an alarm, a fan as a temperature controller, and an LCD to display conditions in the room. The output of the MQ-2 and DHT-11 sensors will be processed in a programmed microcontroller so that it can display conditions on the LCD. This tool will turn on the alarm and fan if smoke and high temperature are detected at the specified conditions.

This Research is generally aimed at designing a system that can solve problems at Hotel Oyo Jakarta Selatan. In addition to the use of bathroom tools, there is a need for a gas controller in the bathroom space, especially ammonia gas and other gas in the bathroom. The bathroom control system at Hotel Oyo Jakarta Selatan is currently ineffective, in the use of the bathroom tools to hotel guests and hotel staff. The reseach method used by researcher is The research step begins with research and information gathering, then planning and developing the initial form of the product or system, and field testing the feasibility of the system or product, if there is a revision, it can be developed further before conducting the main field test. . Based on the research results, monitoring ammonia gas in rooms based on a microcontroller can be a solution for the Oyo South Jakarta hotel in monitoring rooms, both from guests or hotel employees more effectively and efficiently , and to facilitate the management of existing bathroom rooms in the Hotel Oyo Jakarta Selatan

The infrared detector (or IR detector) is an optoelectronic component and represents the core element of gas analyzers, flame sensors, devices of spectral analysis, as well as non-contact temperature measurement. pyroelectric infrared detectors work with  stabilization time  in a wide operating temperature range (-55 ... 85) °C, capable of measuring the slightest amounts of infrared radiation (as little as a fraction of a nW) with wavelengths between 1 µm and > 25 µm. The difference of the good pyroelectric or No good (NG) devices shown by graph of voltage offset operating that can check by manual using Digital Multi Meter or semi auto by using microcontroller One of importance measuring of IRD sensors  is Offset Voltage, stabilization of offset voltage indicate the sensor is stable with certain level value. Stabilization value measurement during measuring time means the average value not so much different with fluctuation value.  10 bit ADC on Atmega 328 that main microcontroller on Arduino Uno is capable to measure the Offset Voltage of IRD sensor and check the measurement values is within specification of IRD sensor.