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PERANCANGAN SISTEM CERDAS DETEKSI DAN PERINGATAN KELELAHAN BERBASIS SENSOR MAX30102 DENGAN INTEGRASI AKTUATOR VISUAL DAN AUDIO PADA PLATFORM INTERNET OF THINGS (IOT)

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Fatigue is a physiological condition that has a high potential and can cause accidents in the workplace and in traffic internationally. This study aims to design and create a smart system that can detect fatigue using the MAX30102 sensor connected to the Internet of Things (IoT) platform. This system utilizes an ESP32 microcontroller that measures physiological parameters such as heart rate (BPM) and blood oxygen levels (SpO2) non-invasively through photoplethysmography (PPG) techniques. Classification of user conditions is carried out using a rule-based approach consisting of five categories: “normal”, “tired/tachy”, “danger (O2)”, “danger (BPM)”, and “no finger”. This system is equipped with visual actuators such as an SSD1306 OLED display and audio actuators in the form of a buzzer as an automatic warning system, and is connected to the Blynk IoT platform and Google Sheets for direct monitoring and data recording. The methods used include hardware design, rule-based firmware development, sensor calibration, and function and accuracy testing. The results of functional testing indicate that all system scenarios operate according to the established plan. Accuracy testing conducted on ten subjects resulted in a Mean Absolute Error (MAE) of 3.5 BPM and an MAE SpO2 of 2.4%, both within the specified tolerance limits. Historical monitoring data across 13 measurement sessions indicated 92.3% were in “normal” condition and 7.7% were in TIRED/TACHY condition, demonstrating the system's ability to detect changes in conditions quickly. Overall, the system has successfully functioned as a real-time fatigue monitoring tool, although improvements to the SpO2 calibration algorithm and additional local data storage space are still needed for future development.

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Detail Information

Series Title

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Call Number

T2017842026

Publisher

Palembang : Prodi Diploma III Teknik Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xvi, 110 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.678 07

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Internet of Things (IoT)
Prodi Diploma III Teknik Komputer

Specific Detail Info

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Statement of Responsibility

MI

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