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Fitri Fitri
State Polytechnic of Malang

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Watch winder based on the internet of things Leonardo Kamajaya; Fitri Fitri; Mocharief Azzmi Santoso
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp721-729

Abstract

Watches are wearable devices that support human activities as timepieces. There are two types of watches based on their source driving energy: the quartz type, which uses a battery, and the automatic mechanical type, which uses a mechanical mechanism to convert the kinetic energy obtained when the clock is used as the energy to drive the hands. Automatic mechanical type watches have a time limit of 48 hours or two days until the energy turns off and the time stop. Therefore, a watch winder is needed to make movements to keep the automatic watch running while not in use. So, users can use their watch again without the need to adjust the time, date and other functions. The watch winder produced in this study employs a stepper motor that is controlled by an ESP32 microcontroller via a ULN2003 motor driver integrated circuit (IC). This type of stepper motor is used because it has a high rotor rotation precision to prevent the clock from over-winding problems. This watch winder device also has a watch condition monitoring feature through application-based temperature and humidity parameters. Users can monitor the condition of their watch and control the dial of the watch winder’s modules through the integrated application.
Intelligent decision-making in healthcare telemonitoring via forward-backward chaining and IoT Agwin Fahmi Fahanani; Novita Titis Harbiyanti; Nurvandy Nurvandy; Fitri Fitri; Ari Murtono; Leonardo Kamajaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1436-1447

Abstract

Healthcare telemonitoring has emerged as a promising approach to remotely monitor patients remotely, enabling timely intervention and personalized care. Internet of things (IoT) device-generated patient data necessitates innovative solutions for intelligent healthcare decision-making, as current methods struggle to provide timely, context-aware, and data-driven recommendations, resulting in suboptimal patient care. This study aims to develop an intelligent decision-making framework for healthcare telemonitoring by leveraging forward-backward chaining and IoT technology. The research focuses on a system using forward-backward chaining algorithms to analyze real-time patient data from IoT devices. It utilizes machine learning models to adapt to changing conditions and refine decision-making, demonstrating its ability to provide real-time context-aware recommendations. Temperature, blood pressure, oxygen level, and heart rate measurement errors are 2.01%, 1.74 to 2.13%, 0.61%, and 1.45%, respectively. The success rate of early disease diagnosis using an expert system is 81%, with an average application interface responsiveness time of 4.978 s. The integration of IoT data with intelligent decision-making algorithms in healthcare telemonitoring has the potential to revolutionize patient care. However, future work should focus on scalability and interoperability for diverse healthcare settings.
Integrated electronic system for FET biosensor assessment based on current-voltage curve tracing Achmad Arif Bryantono; Leonardo Kamajaya; Fitri Fitri; Sungkono Sungkono; Herwandi Herwandi; Agwin Fahmi Fahanani
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1463-1471

Abstract

Field-effect transistor (FET) biosensors are pivotal in diverse applications, from environmental monitoring to healthcare diagnostics. Current-voltage (I-V) curve tracing is a powerful method for evaluating FET biosensor behavior, enabling comprehensive analysis of their FET biosensor characteristics. Traditional I-V curve tracing methods often require complex and expensive equipment, limiting their accessibility and practicality for routine sensor assessment. This study aims to develop and demonstrate an integrated electronic system for assessing FET biosensors using I-V curve tracing. The integrated electronic system uses readily available components, including microcontrollers, analog circuitry, and user-friendly software. We developed a compact, low-cost device that generates I-V curves for the FET biosensor. The integrated electronic system successfully generated I-V curves for various FET biosensors. The system demonstrated consistent, reliable performance, portability, and ease of use, making it a practical solution for routine sensor assessment. The average error in measurements using bipolar junction transistors (BJT) and metal-oxide-semiconductor field-effect transistors (MOSFETs) results in 2.62%, and measurements at different pH levels have a sensitivity of 21.6 mV/pH and a linearity of 0.9892. This innovation contributes to the advancement of FET biosensor technology. In the future, the developments should focus on ensuring their accuracy and reliability in various sensor fields.
Co-Authors Achmad Arif Bryantono Agwin Fahmi Fahanani Agwin Fahmi Fahanani Ari Murtono Herwandi Herwandi Leonardo Kamajaya Mocharief Azzmi Santoso Novita Titis Harbiyanti Nurvandy Nurvandy Sungkono Sungkono