PENGEMBANGAN SISTEM EDGE COMPUTING BERBASIS RASPBERRY PI 3 UNTUK ANALISIS DATA ELEKTROKARDIOGRAM MENGGUNAKAN SENSOR AD8232

FADHIL, AHMAD NAUFAL (2026) PENGEMBANGAN SISTEM EDGE COMPUTING BERBASIS RASPBERRY PI 3 UNTUK ANALISIS DATA ELEKTROKARDIOGRAM MENGGUNAKAN SENSOR AD8232. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The increasing prevalence of cardiovascular diseases highlights the need for real-time, autonomous, and efficient electrocardiogram (EKG) monitoring systems that do not rely on centralized computing infrastructures. This study aims to design and implement an edge computing system based on Raspberry Pi 3 for real-time EKG signal acquisition and analysis using an AD8232 sensor and ESP32, as well as to evaluate the system’s performance in detecting abnormal EKG signals. The research employs a quantitative experimental approach with an engineeringbased system design methodology, encompassing hardware development, lightweight EKG classification model construction using TinyML, and local inference implementation via TensorFlow Lite. EKG signal acquisition is performed at a sampling rate of 250 Hz, while system performance is evaluated based on classification accuracy and inference latency on the edge device. The results show that the proposed system is capable of performing real-time EKG classification with an average accuracy exceeding 90% and an inference latency of less than 200 ms per data sample on the Raspberry Pi 3. These findings demonstrate that the edge computing approach is effective for EKG signal analysis on resourceconstrained devices. This research contributes to the development of edge-based healthcare monitoring systems and provides opportunities for future work involving dataset expansion, model optimization, and clinical validation to enhance system reliability and applicability. Keywords: Edge Computing, Electrocardiogram (EKG), Raspberry Pi 3, AD8232 Sensor, TinyML Peningkatan prevalensi penyakit kardiovaskular menuntut adanya sistem pemantauan elektrokardiogram (EKG) yang mampu bekerja secara real-time, mandiri, dan efisien tanpa ketergantungan pada infrastruktur komputasi terpusat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem edge computing berbasis Raspberry Pi 3 untuk akuisisi dan analisis sinyal EKG menggunakan sensor AD8232 dan ESP32, serta mengevaluasi kinerja sistem dalam mendeteksi sinyal EKG abnormal. Metode penelitian yang digunakan bersifat kuantitatif eksperimental dengan pendekatan perancangan alat, meliputi perancangan perangkat keras, pengembangan model klasifikasi EKG berbasis TinyML, serta implementasi inferensi lokal menggunakan TensorFlow Lite. Akuisisi sinyal EKG dilakukan dengan sampling rate 250 Hz, sementara evaluasi kinerja sistem dilakukan berdasarkan akurasi klasifikasi dan latensi inferensi pada perangkat edge. Hasil penelitian menunjukkan bahwa sistem mampu melakukan klasifikasi EKG secara real-time dengan akurasi rata-rata di atas 90% dan latensi inferensi di bawah 200 ms per data pada Raspberry Pi 3. Temuan ini membuktikan bahwa pendekatan edge computing efektif diterapkan untuk analisis sinyal EKG pada perangkat dengan keterbatasan sumber daya. Penelitian ini berkontribusi dalam pengembangan sistem monitoring kesehatan berbasis edge dan membuka peluang pengembangan lanjutan melalui peningkatan dataset, optimasi model, serta validasi klinis agar sistem dapat digunakan secara lebih luas. Kata Kunci: Edge Computing, Electrocardiogram (EKG), Raspberry Pi 3, Sensor AD8232, TinyM

Item Type:	Thesis (S1)
NIM/NIDN Creators:	41424110049
Uncontrolled Keywords:	Edge Computing, Electrocardiogram (EKG), Raspberry Pi 3, Sensor AD8232, TinyM
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan > 621.3 Electrical Engineering, Lighting, Superconductivity, Magnetic Engineering, Applied Optics, Paraphotic Technology, Electronics Communications Engineering, Computers/Teknik Elektro, Pencahayaan, Superkonduktivitas, Teknik Magnetik, Optik Terapan, Tekn
Divisions:	Fakultas Teknik > Teknik Elektro
Depositing User:	khalimah
Date Deposited:	27 Feb 2026 07:49
Last Modified:	27 Feb 2026 07:49
URI:	http://repository.mercubuana.ac.id/id/eprint/101237

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