Research on Instrumentation
Aim and Scope Research on Instrumentation is a scientific journal that aims to provide a comprehensive platform for the dissemination of research and advancements in the field of instrumentation. Its focus is on analog and digital circuit design, measurement systems, control systems, antennas and wave propagation, electromagnetic, and other relevant areas. The journal welcomes original research articles, review papers, and technical notes that contribute to the development and application of instrumentation in various engineering and scientific disciplines. Scope: Analog and Digital Circuit Design: Research on the design, optimization, and application of analog and digital circuits in instrumentation. This includes, but is not limited to, analog-to-digital and digital-to-analog converters, signal conditioning circuits, and mixed-signal integrated circuits. Measurement Systems: Advances in the development of systems and methodologies for precise and accurate measurement in various environments. Topics may include sensor technology, data acquisition systems, signal processing techniques, and calibration methods. Control Systems: Innovations in control system design, including feedback and feedforward control, adaptive and robust control, and applications of control theory in instrumentation. This also covers real-time control systems and embedded systems design. Sensors and Actuators: The design, development, and application of sensors and actuators in instrumentation. This includes studies on sensor materials, sensor networks, MEMS-based sensors, and their integration into complex systems. Signal Processing: Research on advanced signal processing techniques for instrumentation systems, including noise reduction, filtering, data compression, and pattern recognition. Embedded Systems: Studies on the integration of embedded systems in instrumentation, focusing on hardware-software co-design, real-time computing, and the development of low-power and high-performance systems. Test and Calibration Methods: Development of innovative testing and calibration techniques for instrumentation systems, ensuring accuracy, reliability, and repeatability in measurements. Applications of Instrumentation: Papers exploring the application of advanced instrumentation in fields such as industrial automation, medical devices, environmental monitoring, telecommunications, and aerospace engineering. Electromagnetic, Antenna and Wave Propagation: Antennas—covering their analysis, design, development, measurement, and testing—as well as radiation, propagation, and how electromagnetic waves interact with both discrete and continuous media. Additionally, the journal addresses applications and systems related to antennas, propagation, and sensing. These include applied optics, millimeter- and sub-millimeter-wave techniques, antenna signal processing and control, radio astronomy, and the propagation and radiation aspects of terrestrial and space-based communication. The Research on Instrumentation is dedicated to advancing the field by publishing high-quality research that drives innovation and facilitates the application of cutting-edge instrumentation techniques across various industries. Contributions that explore interdisciplinary approaches and emerging technologies are highly encouraged.
Articles
27 Documents
<![CDATA[An IoT-Enabled Tomato Sorting System with Dual Opposing Ultrasonic Sensors and Multispectral Color Detection for Five-Class ]]>
<![CDATA[Nurahmadani]]>;
<![CDATA[Asrizal]]>;
<![CDATA[Fatni Mufit]]>
<![CDATA[ Research on Instrumentation Vol. 3 No. 1 (2026): Research on Instrumentation ]]>
Publisher : <![CDATA[RESSTECH ]]>
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DOI: 10.66926/rins.2026.1.31
<![CDATA[Tomato sorting plays an important role in maintaining crop quality, especially in terms of ripeness and size, which affect market value. Tomato sorting is generally still done manually, which is time-consuming, labor-intensive, and often results in misclassification. To address this issue, this study aims to design an automatic tomato sorting device using a TCS34725 color sensor and an ultrasonic sensor integrated with the Internet of Things (IoT) using ESP32 as a remote monitoring system. The research method used is research engineering, which includes hardware and software design. The hardware consists of a DC motor to drive the conveyor, a PCA9685 module to control four servos, a TCS34725 sensor to detect RGB values, and two opposing ultrasonic sensors to measure diameter. The software was built using Arduino IDE, with Blynk IoT integration as a medium for monitoring RGB values, diameter, category, and number of sorting results. The system was developed to classify tomatoes into five categories, namely large ripe, small ripe, large semi-ripe, small semi-ripe, and mixed unripe. Test results show that the sorting tool is capable of classifying ripeness levels based on RGB values and diameter with an average accuracy of 90% to 99% and an average error of less than 10%. Sorting data can be monitored remotely via Blynk, and the system can still operate offline without an internet connection. Thus, this tomato sorting tool is expected to facilitate the community, especially farmers, in the process of sorting tomatoes more accurately and efficiently.]]>
<![CDATA[A Smart Health Device to Measure Stress Levels Based on the Internet of Things Using the K-Nearest Neighbor Algorithm ]]>
<![CDATA[Tiara Ayunda]]>;
<![CDATA[Asrizal]]>;
<![CDATA[Leni Aziyus Fitri]]>
<![CDATA[ Research on Instrumentation Vol. 3 No. 1 (2026): Research on Instrumentation ]]>
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DOI: 10.66926/rins.2026.1.32
<![CDATA[Mental health plays an important role in daily life. However, many factors can affect mental health, one of which is stress. Students are one group that is prone to stress. Academic stress is common among students. Currently, physiological stress screening devices are available, but most of them still work separately and are quite expensive. Therefore, this study aims to design an IoT-based stress detection device with a KNN algorithm that can measure physiological symptoms to detect stress levels in a practical and economical manner. This research is a type of engineering research, which involves the process of designing hardware and software for the system in an IoT-based stress level detection tool. The tool is designed to measure three physiological parameters, namely skin conductance, heart rate, and body temperature. Sensor data is processed using the KNN algorithm to classify stress levels into four categories, namely normal, mild, moderate, and severe. The results are displayed on an OLED and ThingSpeak platform so that they can be accessed remotely through IoT integration. Testing was carried out by collecting stress condition data from several subjects. The test results show that the device has an average accuracy and precision value of 83.33% to 99.82%. In addition, the average prediction computation time produced by the system was only 0.44 seconds, this computation time falls within an acceptable range for real-time applications. Thus, this stress level detection tool is expected to be an alternative solution and facilitate remote condition monitoring through the IoT feature.]]>
<![CDATA[Design and Implementation of a Hybrid Voice- and IoT-Based Smart Home Control System Using Local Speech Processing to Reduce Cloud Dependency ]]>
<![CDATA[Sukma Maksum]]>;
<![CDATA[Yulkifli]]>;
<![CDATA[Mona Berlian Sari]]>
<![CDATA[ Research on Instrumentation Vol. 3 No. 1 (2026): Research on Instrumentation ]]>
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DOI: 10.66926/rins.2026.1.49
<![CDATA[Electrical devices are an important part of modern life. However, forgetting to turn off electrical appliances is still one of the causes of house fires in many areas. This research aims to determine the performance and design specifications of a home electrical appliance control system using a voice recognition sensor based on the Internet of Things (IoT). The research method used is an engineering method with several steps, including problem identification, conceptual design, system design, detailed design, prototype building, and system testing. The system is built using an Arduino Uno microcontroller, ESP32 module, voice recognition sensor, and relay module, which are connected to the Blynk application as a user interface for real-time communication. The system has two modes: voice control mode and manual control mode. In voice control mode, devices work based on voice commands from the user. In manual mode, users can control and monitor devices remotely through the Blynk application using a switch. The test results show that the system achieved an accuracy of 90% and demonstrated good consistency under controlled testing conditions. Furthermore, the system operates effectively and has demonstrated a high level of functional success during testing. These results show that the IoT-based control system using a voice recognition sensor works well and is reliable for controlling home electrical appliances.]]>
<![CDATA[ESP32 Based Battery Management System with Passive Balancing and Smartphone Monitoring ]]>
<![CDATA[Raisa Analista]]>
<![CDATA[ Research on Instrumentation Vol. 3 No. 1 (2026): Research on Instrumentation ]]>
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DOI: 10.66926/rins.2026.1.53
<![CDATA[A Battery Management System (BMS) is an electronic system designed to protect batteries from potentially harmfull operating conditions. However, conventional BMS still have limitations in cell balancing, protection, voltage monitoring, and State of Charge (SOC) estimation, which may reduce battery performance and increase the risk of battery damage. This study aims to design and implement an ESP32-based Battery Management System equipped with voltage and SOC monitoring, overcharge and undervoltage protection, and passive balancing, enabling real-time battery monitoring through a smartphone application. The proposed system was developed using an ESP32 microcontroller and DC voltage divider sensor to measure the voltage of series-connected battery cells. The measured data were processed using the Arduino IDE and displayed on the Blynk application, including battery voltage, SOC, and protection status indicators. The system performance was evaluated through sensor characterization, measurement error analysis, and assessments of the passive balancing and monitoring functions. The experimental results demonstrated that the proposed system achieved a measurement error of less than 2% a relative accuracy of 99%, and a precision of up to 99.8%. Furthermore, the passive balancing function successfully equalized the voltage of each battery cell, resulting in a total battery voltage of 12 V and an SOC of 100% after the charging process. These findings indicate that the proposed system operates effectively in accordance with its intended functions. Therefore, the developed Battery Management System has the potential to provide an effective solution for improving battery safety, extending battery lifespan, and optimizing battery utilization.]]>
<![CDATA[Analysis of the Accuracy and Precision of Load Cell Sensors in IoT-Based Water Mass Measurement: English ]]>
<![CDATA[Vina Rahma Yeni]]>
<![CDATA[ Research on Instrumentation Vol. 3 No. 1 (2026): Research on Instrumentation ]]>
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<![CDATA[Accurate and precise mass measurement is an important aspect of sensor based measurement systems. Load cell sensor are widely used for mass measurement because they can convert applied load into measurable electrical signal and can be integrated with Internet of Things (IoT) system. However, the measurement performance of a load cell needs to be evaluated based on its characterization, accuracy and precision. This study aims to analyze the characterization, accuracy and precision of a load cell sensor in measuring water mass using an IoT-based measurement system. The research employed an engineering research approach through the design, assembly, calibration, and testing ig the measurement system. Characterization results indicate a linear relationship between the applied load and the ADC output, with a coefficient of determination (R2) of approximately 0.9997. Accuracy testing was conducted using 12 variations of water mass, while precision testing was performed through 15 repeated measurement. Accuracy was determined by comparing the measures mass with the reference mass, while precision was evaluated based on the consistency of repeated measurements. The accuracy test resulted get an average of 98.11%. the precision test produced average measures=d mass of 58.90 gram and an average precision of 98.81%. these result indicate that the load cell provides relatively accurate and consistent measurement of water mass within the tested measurement range. Therefore, the load cell demonstrates good performance and is suitable for water mass measurement in an IoT-based instrumentation system.]]>
<![CDATA[Travel Time Measurement for Uniformly Accelerated Rectilinear Motion Based on Infrared Sensor ]]>
<![CDATA[Avri Lyana Syafitri]]>
<![CDATA[ Research on Instrumentation Vol. 3 No. 1 (2026): Research on Instrumentation ]]>
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DOI: 10.66926/rins.2026.1.57
<![CDATA[This study evaluates the accuracy and precision of an infrared (IR) sensor system applied to measure uniformly accelerated linear motion (GLBB) using an Atwood machine. The system utilizes IR sensors to detect travel time. The measured travel times are compared against values obtained via stopwatch and those derived from the theoretical formulation of Newton's Second Law. Measurements were conducted across seven distance variations, ranging from 0.20 m to 0.50 m at 0.05 m intervals. The results demonstrate an average accuracy of 99.31% when comparing sensor measurements to theoretical values, with a mean percentage error of 0.69%. Comparisons with stopwatch measurements—using the stopwatch as the reference value—show an average accuracy of 96.52%; this indicates that sensor-based measurements are more accurate than manual stopwatch timing, which is susceptible to human error. Repeated measurements at distances of 0.20 m and 0.30 m yielded stable readings, with a relative error of 0.17%. These findings demonstrate that the IR sensor-based timing system is capable of measuring travel time accurately and consistently, while also mitigating timing errors associated with manual stopwatch usage.]]>
<![CDATA[IoT-Based Oscillation Counter Using a Tracker Sensor ]]>
<![CDATA[Vika Okrian Safitri]]>
<![CDATA[ Research on Instrumentation Vol. 3 No. 1 (2026): Research on Instrumentation ]]>
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DOI: 10.66926/rins.2026.1.59
<![CDATA[The development of Internet of Things (IoT) technology has enabled the implementation of measurement and monitoring systems that can be accessed remotely through the internet. In oscillation experiments, the number of oscillations is generally determined through direct observation, which may require continuous monitoring and can be affected by human observation errors. This research aimed to develop an IoT-based oscillation counter using a tracker sensor and to evaluate the accuracy and precision of oscillation counting based on sensor detection results. The system consisted of a tracker sensor, Arduino Uno as the main controller, an Ethernet module for data communication, and a web interface for remote monitoring. The research stages included system design, hardware and software implementation, sensor characterization, and performance testing. The tracker sensor operated based on infrared light reflection using active-high logic, producing a HIGH output when an object was detected and a LOW output when no object was detected. The sensor characterization results showed average output voltages of 3.276 V when an object was detected and 0.001 V when no object was detected. The accuracy test of oscillation counting achieved 100% accuracy with 0% error for (n = 2, 4, 6, 8, 10). Furthermore, the precision test using 10 repeated measurements at (n = 10) achieved 100% precision, indicating consistent oscillation counting results. The detected oscillation data were processed by the Arduino Uno and transmitted to the server for real-time display on the web interface. The results indicated that the developed system was capable of accurately and consistently counting oscillations and supporting remote monitoring through an IoT-based web interface.]]>
