Mining activities significantly impact the environment, necessitating effective, continuous monitoring. Traditional surface monitoring methods are often costly and labor-intensive. This study proposes an automated workflow using the SpatioTemporal Asset Catalog (STAC) and Sentinel-2 satellite imagery to monitor mining surface changes. By calculating the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Modified Bare Soil Index (MBI), the workflow identifies land cover changes within mining concessions. The system was Implemented in Python environment using libraries such as PySTAC, PySTAC Client, Xarray, Rioxarray, Geopandas, Dask, and Numpy. The mining surface change was analyzed using the regression line gradient of each spectral index. Results show active mining sites exhibit an NDVI slope lower than -1, indicating rapid conversion of vegetation to non-vegetative land due to land clearing activities. Conversely, the positive NDWI trend indicates increased water coverage from land excavation, while the MBI trend is the weakest, suggesting limited sensitivity to surface changes in mining areas. To evaluate the accuracy of the results, manual verification was conducted. The analysis revealed that 3 out of 25 mining concessions were incorrectly classified, resulting in an overall accuracy of 88%.
In electric power distribution systems, the distance between the load bus and the generating unit significantly affects grid efficiency and reliability, with longer distances causing greater voltage drops. To mitigate this, Distributed Generation (DG) is increasingly being used, generating electricity closer to the point of consumption. Determining the optimal DG location requires advanced metaheuristic methods. This research proposes the Grey Wolf Optimizer Algorithm (GWOA) to determine optimal DG placement, tested on the IEEE 14-bus distribution grid. The method generated two scenarios: In the first scenario, power losses were reduced by 98.1465% for real power and 98.9538% for reactive power compared to the existing conditions, while voltage increased by an average of 0.0127 p.u. for all buses combined. The second scenario also showed a notable voltage increase of 0.0064 p.u. The GWOA method proves to be an efficient and effective solution for DG placement, enhancing system reliability and protecting household electronic devices.
The microstrip antenna is low-profile, meaning it has a narrow bandwidth. Additionally, the microstrip antenna is compact, making it highly suitable for implementation in small devices. Its low-profile nature is due to its thin thickness and flexibility, allowing it to be applied on curved surfaces. Technology 5G operating at the 3.5 GHz frequency requires a minimum bandwidth of 100 MHz to support high-speed data transmission and large network capacity, in accordance with the standards set by 3GPP and ITU. This study explores the combination of dual-feed and truncated methods to broaden the antenna's bandwidth. Truncation on the patch helps expand the bandwidth by altering the current distribution on the patch, which can affect the antenna's resonance. The antenna design is carried out using High-Frequency Structure Simulator (HFSS) software. This study shows the optimal bandwidth based on previous references, bandwidth alignment within the frequency range of 3.44 to 3.62 GHz with a simulated bandwidth of 180 MHz, and a measured bandwidth of 210 MHz within the frequency range of 3.42 GHz to 3.62 GHz.
Photodiode sensors are widely used in various applications such as light intensity measurement, optoelectronic devices, and automation. In improving the quality of measurement and automation systems, more sophisticated technology is needed such as photodiode sensor arrays, which allow more accurate data collection from multiple sensors simultaneously. This research aims to design a photodiode sensor array with high sensitivity. The system design consists of six photodiode sensors combined with a summing amplifier circuit and a non-inverting amplifier as a signal conditioner which is then processed by a microcontroller. After that, the linear regression function is determined through the calibration process and experiments carried out. Two linear regression functions are obtained and implemented in two operating modes: normal mode and sensitive mode. Experimental results yield two linear regression functions applied to a photodiode sensor array in normal and sensitive modes. Normal mode shows 82.50% accuracy with a 36.69% coefficient of variation, while sensitive mode boasts 94.05% accuracy and 49.81% coefficient of variation. Both modes cater to different light conditions, with sensitive mode excelling in detecting light intensity. Linear regression implementation proves precise and accurate for light detection.
Wastewater produced by healthcare facilities must meet the parameter criteria set by the Ministry of Environment and Forestry. These criteria ensure that the discharged wastewater does not harm the environment. The numerous healthcare facilities in an area and irregular monitoring can lead to inaccuracies in recording wastewater content. Technology that can be used for periodic monitoring of wastewater content in healthcare facilities is multi-sensor technology and the internet of things (IoT). This paper aims to develop the devices utilizing multi-sensor technology and IoT for monitoring wastewater parameter criteria in healthcare facilities. The developed device measures pH, turbidity, oxidation reduction potential (ORP), and temperature parameters in the wastewater. The sensor data test results showed an accuracy of 98.77% with a precision of 10.03 ± 0.13 for the pH parameter. The ORP parameter showed an average accuracy of 97.56% with a precision range of 241.20 ± 5.65. The turbidity parameter showed an average accuracy of 96.20% with a precision range of 101,67 ± 3,11. The temperature parameter showed an average accuracy of 97.80% with a precision range of 39.23 ± 0.73. Data transmission to the platform had an average delay of 88.02 ms with an average jitter 0.23 ms. Based on the performance measurement data for sensor and data transmission categories, the proposed device met the research aim in monitoring wastewater conditions in healthcare facilities. The device improve the documentation process in monitoring watewater parameter in healthcare facility. The monitoring results using the device showed that the three healthcare facilities met the criteria required by Indonesian Ministry of Environment and Forestry.
Monitoring the quality of shrimp pond water is crucial for shrimp growth, with salinity being one of the most significant parameters. Currently, salinity sensors for pond water are designed for momentary measurements, which are unsuitable for continuous monitoring. This study introduces a method for continuous salinity measurement using ultrasonic signals. The proposed approach utilizes a measuring chamber equipped with ultrasonic sensors to determine the Time-of-Flight (ToF). To ensure accuracy, four ToF methods were compared, with the cross-correlation method identified as the most accurate. This method was subsequently used to calculate the ToF, which was then applied to determine the acoustic speed. Since the acoustic speed in water is influenced by salinity, temperature, and pressure, changes in salinity cause detectable changes in the acoustic speed. The acoustic speed was further used as input for the modified Del Grosso equation to derive the salinity. Experimental results showed an average error of 4.83% for saline solutions and 1.81% for shrimp pond water. These findings demonstrate that the proposed method provides sufficient accuracy for water salinity measurement.
The increasing demand for electricity supply is evident across various modern industrial sectors. Electrification advancements in the transportation industry aim to address environmental and energy issues and continue to evolve. Charging electric vehicles is one of the most important aspects of electrification in the transportation sector. This study develops and simulates a three-phase IGBT-based rectifier using the Discontinuous Pulse Width Modulation 60-degree (DPWM1) method to optimize power loss reduction in a 200-kW charging system. The DPWM method has been shown to reduce power losses by up to 44% compared to the conventional Sinusoidal Pulse Width Modulation (SPWM) method under full load conditions, resulting in reduced heat and cooling requirements for the devices. Simulations show that with the appropriate use of filters, the total harmonic distortion of current (THDi) of the AC input side is reduced to 2.267%, minimizing the negative impacts on the grid. In addition, implementing feedforward control maintains DC voltage stability despite load variations, improving system efficiency and reliability.
Identity (ID) card forgery remains a significant issue in Indonesia, often leading to crimes such as identity theft and fraud. To address this challenge, this study proposes the development of an identity authentication system that integrates near field communication (NFC) and facial recognition based on K-nearest neighbors (KNN) algorithm. The primary objective of this system is to enhance the security of ID card (KTP) data and to ensure efficient and accurate access to services requiring identity verification. The system stores facial data and ID card information securely in Firebase, which serves both as a user authentication platform and a secure cloud-based storage solution. The application, developed using Flutter, incorporates facial recognition for biometric verification, while NFC is employed as an additional authentication layer to provide dual-factor verification and reinforce identity security. Experimental results demonstrate that the facial recognition based on KKN achieved an accuracy rate of 100% with a false acceptance rate (FAR) of 0%, indicating a highly reliable performance. These findings confirm that the integration of facial recognition and NFC technologies offers a robust and effective solution to combat ID card forgery, thereby improving the overall reliability and security of the population data authentication system in Indonesia.
Pressure injury or pressure ulcers could occur due to continuous pressure on the bony prominence of the skin and tissue. Geriatric patients, especially those with limited mobility and several comorbidities, are more susceptible to pressure injury. Stage classification of pressure injury is currently carried out qualitatively and requires clear communication with the patient. This is often not possible in elderly patients due to lack perception of pain causing late detection of pressure injury until they have reached a severe level and can endanger the patient's life. This study proposes a non-contact device in the form of a camera integrated with a convolutional neural network (CNN) model with MobileNet architecture to classify the level of pressure injury. Testing showed a classification accuracy of 83.3% with an average classification duration of 2.24 s. This aiding device is considered to have great potential to improve faster and more accurate pressure injury assessment in clinical settings.
This paper focuses on the development of a multi-hop LoRa (Long-Range) communication network for real-time monitoring of urban drainage Internet of Things (IoT), specifically simulating the flood-prone area along the drainage channel of Jalan Jawa, Surabaya City. The novelty of this research lies in the selection of the optimal communication environment through path loss and shadowing analysis prior to implementing a multi-node, multi-hop, sensor medium access control (S-MAC) method. The selected environment at the first location demonstrated a lower path loss exponent of 1.55, typical of "in-building line-of-sight," compared to the second location with a loss exponent of 2.82, which resembled "urban area cellular radio." Applying the multi-hop technique successfully extended the data transmission range up to 750 meters with nodes placed at 250 meter intervals while maintaining a high data transfer rate. The experiments showed that increasing distance significantly reduced the received signal strength indicator (RSSI), with values dropping from -52.75 dBm at 150 meters to -98.25 dBm at 750 meters. This paper demonstrates the feasibility of using multi-hop communication rather than the conventional multi-node technique to ensure reliable data transmission and wider range, offering a solid foundation for building a robust communication network in urban drainage monitoring systems.
A rectifier is an electronic circuit that converts Alternating Current (AC) into Direct Current (DC). This circuit is very important in many electronic applications, especially in power supplies, battery chargers, and other equipment requiring a DC power supply. Conventional rectifiers use diodes or thyristors as rectifying components. Although a rectifier that uses a diode is generally effective and easy to apply in converting AC to DC, the diode or thyristor component has several drawbacks when used as a rectifier, namely the low power factor on the supply side. To overcome these deficiencies, this paper presents the topic of front-end converter which uses Insulated Gate Bipolar Transistor (IGBT) components as a substitute for diode and thyristor components. IGBT is an active component so a SPWM signal is needed to regulate this IGBT so that it can work. In this front-end converter, to achieve unity power factor results on the supply side, SPWM is used so that it can adjust the current waveform on the supply side to be in phase with the voltage waveform on the source side. The front-end converter presented in this paper has also added the load on the AC side to prove the work of the Front-End Converter.
The increasing complexity of industrial automation technology today requires industries to upgrade their existing systems, such as IoT-based SCADA, which allows for real-time production monitoring from anywhere. However, to achieve this, a system is needed that does not affect or halt the production process and does not require replacing the existing system during the upgrade process. Therefore, a system that can adapt to the existing one is necessary. This research aims to implement an IoT-based SCADA system in an existing plant by integrating the system with ctrlX Automation to address this issue. The implementation is carried out in several stages. The first stage involves integrating the plant controlled by the Omron CP1L PLC with ctrlX Automation. The second stage is creating tags for the SCADA system using tools installed on ctrlX Automation. The third stage involves developing the user interface. The fourth stage is the remote access communication process for the IoT system using the OpenVPN Cloud tool. The test results show that the integration between the Omron CP1L PLC and ctrlX Automation was successful, with a 100% tag retrieval rate from the Omron CP1L PLC, and the data could be visualized. The user interface can be accessed, and remote access can be performed on different networks and locations using the OpenVPN Cloud tool. During testing, the average data transfer write-read delay with one address/tag during remote access was 61.3ms, and for the write-read test with three addresses simultaneously, the average was 78.1ms. Based on these results, the data transfer process using ctrlX Automation integrated with the Omron CP1L PLC can be categorized as very good.
Human activity recognition (HAR) has become an important field of study because of its wide range of applications in healthcare, security, and smart living systems. Radio frequency (RF)-based HAR offers a non-invasive and privacy-preserving alternative to traditional vision-based systems. This study proposes a hybrid deep learning model combining long short-term memory (LSTM) networks with Random Forest classifiers for RF-based HAR, aiming to improve recognition accuracy across diverse environments. The model was evaluated using channel state information (CSI) and received signal strength indicator (RSSI) features under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. synthetic minority over-sampling technique (SMOTE) was integrated to balance the dataset and K-fold cross-validation was employed to assess robustness. The dataset included data from 8 subjects performing 10 different activities. The model achieved high classification accuracy, with 99.40% in Environment 1 (LOS), 97.58% in Environment 2 (LOS), and 98.30% in Environment 3 (NLOS), demonstrating the model’s adaptability and effectiveness. The results highlight the potential of the hybrid LSTM with random forest approach for scalable and reliable RF-based HAR systems that can be integrated into real-world Internet-of-Things (IoT) applications.
As air traffic becomes more complicated, more effective monitoring systems are required to assure aviation safety and security. Automatic Dependent Surveillance-Broadcast (ADS-B) technology has become the international standard for real-time airplane tracking, however typical ADS-B receivers used in airports are expensive and frequently unavailable. This study seeks to assess the dependability and efficiency of Software Defined Radio (RTL-SDR) as a low-cost option for receiving ADS-B signals. The study focuses on the development of a 1090 MHz PCB antenna coupled to an RTL-SDR device, with data processed using RTL1090 software and visualized using Virtual Radar Server. Testing took place at two airports: Yogyakarta International Airport (YIA) and Wirasaba Airport. The results show that the system can identify aircraft within a range of up to 400 km at YIA and 250 km at Wirasaba, with the received data providing precise information on aircraft position, altitude, and speed. The system spotted four airplanes in Wirasaba and nine at YIA, indicating that the latter location has broader coverage. These findings show that RTL-SDR is a dependable and cost-effective option for ADS-B signal reception, with the potential to replace more expensive conventional receivers used in airports.
This research aims to develop an integration device combining Multi-Modal Sensors and Robot Arm Vision (MMS-RAV) for monitoring activities and assisting in healthcare services for the elderly at home. The method used to develop this device involves integrating MMS, which consists of PIR sensors for detecting the presence of the elderly, LDR sensors for detecting home light conditions, fire sensors for detecting flames, and DHT11 sensors for measuring temperature and humidity. Additionally, the RAV component assists and supports the activities of the elderly and includes a camera for vision-based object detection, ultrasonic sensors for robot navigation, Raspberry Pi as the data processing center, an arm for object retrieval and camera movement, LCD for displaying messages, omni-wheels for robot navigation, and buzzer for early warnings in case of anomalous conditions with the elderly. In this research, MMS functions to monitor elderly activities, while RAV supports healthcare services for the elderly, particularly in medication intake using image processing techniques. The software used to control the entire MMSRAV system is the robot operating system. The results of this study indicate that the developed MMS-RAV device is effective for monitoring elderly activities and assisting in providing healthcare services for medication intake.
Technology in robotics has developed rapidly in the last few decades, as evidenced by the increasing number of robots created, such as humanoid robots and mobile robots. In this study, a wheeled humanoid robot is designed to move from one place to another using a swerve drive model, a holonomic type of drive wheel. This model uses a combination of DC motors and gears to ensure smooth movement of the humanoid robot. The swerve drive allows the robot to move freely in all directions. Therefore, the humanoid robot requires a control system to manage and automatically regulate the state of the system. The fuzzy logic control system can perform mathematical calculations based on human knowledge, serving as a controller without requiring a mathematical model of the controlled process. The results obtained from this study demonstrate the robot’s ability to move stably and accurately, based on the response to the rules provided by the fuzzy logic control system. The more membership functions used, the more stable and accurate the results will be, while using fewer membership functions will result in faster response times to reach the setpoint.
The industrial internet-of-things (IIoT) has recently become an important requirement in the process industry. The factories must be able to integrate process automation devices such as programmable logic controllers and industrial computers with mobile devices, especially to support their maintenance and operations. Connectivity with mobile devices has the consequence that cellular networks must be specified to the needs of the industry itself. Comparative studies on using cellular networks in process automation systems are urgently needed. The research that has been conducted is a comparative study between the use of 4G and 5G cellular networks in IIoT process automation systems. It can be seen in the result that the 4G cellular network is sufficient to be used for industries that require mobile devices for monitoring functions, as seen from the results showing the latency obtained is 17.03 ms, jitter is 9.5 ms, packet loss is 6.67 %, and throughput is 192.73 Kbps. However, for the industry that needs to perform real-time control, mobile connectivity has to use a 5G network with better performance metrics with a latency of 15.21 ms, jitter of 5.43 ms, packet loss of 2.67 %, and throughput of 217.19 Kbps. The research results are needed by the process industry in Indonesia, which is widely spread on the island as an archipelago with quite varied cellular network connectivity quality.
This research aims to develop an Eye-to-Text Morse Code (ETT-MC) device as an assistive communication tool for individuals with speech disabilities, based on artificial intelligence image processing. The method used to detect speech codes is based on eye blink input, which is converted into Morse code and then translated into letters by utilizing a thresholding image processing technique that compares the pixel values when the eyes are open and closed. In Morse code, there are two main symbols combined to form a letter: the dash and the dot. The object of this research is the eye, where if the system detects the eye in an open state, it is converted into a dot code or value 1, while when the system detects the eye as closed, it is converted into a dash code or value 0. The results and hypotheses of this research show that the developed ETT-MC device can assist individuals with speech disabilities in communicating by utilizing eye blinks as an input medium to convey messages to others with an accuracy rate of up to 80%. This occurs because the accuracy of eye image detection processed by the system is significantly influenced by light intensity, the quality of the image detected by the camera, and the length of the translated text.
Wood is an incredibly valuable resource, particularly for everyday living. To fully harness the advantages of wood, it must focus on two key considerations. Firstly, it is imperative to consistently utilize wood sourced from sustainably managed forests. Secondly, we must explore techniques that maximize the utilization of every part of the tree. One technique for meeting these considerations is to create a wood identification system. This system can be used for quickly inspecting wood species. In wood identification, it is essential to consider specific characteristics and physical properties of wood. Manual identification will depend on the examination of wood anatomists’ eye and will require a significant amount of time. In accordance with these situations, a computer vision-based system can address this condition. Therefore, feature extraction is necessary to extract the features of wood characteristics from the wood image. This research aims to propose a method for wood species identification based on Gray Level Co-occurrence Matrix (GLCM) features to extract important information about wood characteristics from macroscopic wood images. For the classifier, the Random Forest algorithm is proposed for the identification of the machine learning model. Five wood species images will be used in this research, with each wood sample being presented as a macroscopic image. The total dataset used was 750 images, with each wood species having 150 images. The result showed that the Model C (90/10) training data ratio demonstrates good performance in classifying wood species from the macroscopic images. The model achieved a peak accuracy of 0.81 and correctly predicted all test images. This study indicates that the Random Forest model can be an effective classifier for wood species identification.
This research develops an Internet-of-Things (IoT)-based system for real-time monitoring and automatic control of water quality in koi fish farming, addressing the lack of knowledge regarding optimal water conditions. The system integrates sensors for pH, ammonia, temperature, total dissolved solids (TDS), and turbidity, along with controllers such as filters, coolers, and heaters, all managed through a mobile application called “AquaKoi.” System testing is divided into IoT device testing and mobile application testing. IoT device testing ensures proper sensor and controller functionality, with sensor data verified against water quality standards. Application testing includes black box testing, quality-of-service (QoS) measurement, user acceptance test (UAT), and notification warning testing, showing a user satisfaction rate of 92%. The test results indicate that the system functions well and meets specifications, despite challenges like overheating of the ESP32 microcontroller, which was mitigated with a temporary fan solution. Overall, the AquaKoi system demonstrates significant potential in enhancing the efficiency of koi fish farming. However, further development is recommended to address technical constraints, improve the user interface, and expand the system’s capabilities to meet more diverse user needs.
