Penerapan Logika Fuzzy Mamdani dan Sensor MAX30100 untuk Penilaian Kondisi Pasien Kardiovaskular
Penelitian
Keywords:
Mamdani Fuzzy Logic, Patient Monitoring, MAX30100, Cardiovascular Patient, Case StudyAbstract
Monitoring cardiovascular patients at home is often constrained by the interpretation of raw sensor data. This service aimed to implement a portable decision support system to convert raw heart rate (BPM) and oxygen saturation (SpO2) data into meaningful health condition information. The method used was designing a prototype based on NodeMCU ESP8266 and the MAX30100 sensor for data acquisition , and applying Mamdani Fuzzy Logic for analysis. A Fuzzy Inference System (FIS) in MATLAB used 9 rule-bases to classify 2 inputs (BPM and SpO2) into 3 outputs ('Healthy', 'Caution', 'Emergency'). The case study object was a patient with a history of hypertensive heart disease and aortic aneurysm. The results showed the prototype had high accuracy, reaching 95.98% for heart rate and 95.31% for oxygen saturation. The fuzzy logic system successfully classified 35 test datasets, with 34 detected as "Healthy" and 1 as "Caution," proving its sensitivity in monitoring. Oxygen saturation was proven to be the dominant factor for 'Emergency' conditions, while heart rate more influenced the transition from "Healthy" to "Caution". This system provides a practical solution for families to monitor patient conditions more effectively.
References
Cahyadi, W., Chaidir, A. R., & Anda, M. F. (2021). Penerapan Logika Fuzzy sebagai Alat Deteksi Hipotermia dan Hipertermia Pada Manusia Berbasis Internet Of Thing (Iot). Jurnal Rekayasa Elektrika, 17(2), 94–99. https://doi.org/10.17529/jre.v17i2.15670
Ghamari, M. (2018). A review on wearable photoplethysmography sensors and their potential future applications in health care. International Journal of Biosensors & Bioelectronics, 4(4). https://doi.org/10.15406/IJBSBE.2018.04.00125,
Hakim, F., & Nurwarsito, H. (2019). Sistem Pemantauan Detak Jantung dan Suhu Tubuh menggunakan Protokol Komunikasi MQTT. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(11), 10705–10711.
Isa, I. G. T., Ammarullah, M. I., Efendi, A., Nugroho, Y. S., Nasrullah, H., & Sari, M. P. (2024). Constructing an elderly health monitoring system using fuzzy rules and Internet of Things. AIP Advances, 14(5). https://doi.org/10.1063/5.0195107
Ismail, S., Akram, U., & Siddiqi, I. (2021). Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review. Eurasip Journal on Advances in Signal Processing, 2021(1). https://doi.org/10.1186/s13634-020-00714-2
Majumder, S., Mondal, T., & Deen, M. J. (2017). Wearable Sensors for Remote Health Monitoring. Sensors 2017, Vol. 17, Page 130, 17(1), 130. https://doi.org/10.3390/S17010130
Mostafa, B. S., Miry, A. H., & Salman, T. M. (2022). Smart health monitoring and self-analysis system based on internet of things with fuzzy controller. AIP Conference Proceedings, 2386(January). https://doi.org/10.1063/5.0067991
Sapra, A., Malik, A., & Bhandari, P. (2023). Vital Sign Assessment. StatPearls. https://www.ncbi.nlm.nih.gov/books/NBK553213/
Vidyastari, I. R., Rohma, N. M., & Mohsin, M. (2023). Alat Monitoring Detak Jantung dan Suhu Tubuh Berbasis Arduino Sebagai Upaya Menjaga Kesehatan Pasca Pandemi. CYCLOTRON : Jurnal Teknik Elektro, 6(02), 74–76.
Zuccotti, G., Agnelli, P. O., Labati, L., Cordaro, E., Braghieri, D., Balconi, S., Mandalari, D., Fiorina, P., Bertoli, S., Berra, C. C. F., Croci, M., Losurdo, F., Foppiani, A., Bucciarelli, L., Xodo, M., De Pasquale, S., & Calcaterra, V. (2025). Accuracy of heart rate, pulse oxygen saturation, and blood pressure using a non-contact photoplethysmography-based mobile application: A potential tool for wellness monitoring. Digital Health, 11, 1–9. https://doi.org/10.1177/20552076251351841
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