Electronic Theses and Dissertation

Penyakit Paru Obstruktif Kronik (Chronic Obstructive Pulmonary Disease / COPD) merupakan salah satu penyebab kematian tertinggi di dunia dengan 3,5 juta kematian per tahun menurut World Health Organization (WHO) (2021), dengan prevalensi di Indonesia mencapai 5,6% atau sekitar 4,8 juta penderita. Penelitian ini mengembangkan prototipe sistem monitoring senyawa VOCs terkait PPOK berbasis sensor gas Volatile Organic Compounds (VOCs) menggunakan mikrokontroler ESP32 yang terintegrasi dengan platform Internet of Things (IoT). Sistem menggunakan empat sensor gas, yaitu MQ-7 untuk karbon monoksida (Carbon Monoxide / CO), MQ-3 untuk etanol (Ethanol), MQ-135 untuk VOCs kompleks seperti amonia (Ammonia) dan benzena (Benzene), serta MG-811 untuk karbon dioksida (Carbon Dioxide / CO₂). Nilai pembacaan sensor dikonversi ke dalam satuan persentase berdasarkan resolusi Analog-to-Digital Converter (ADC) 12-bit, kemudian dikirimkan secara real-time melalui protokol Message Queuing Telemetry Transport (MQTT) ke backend berbasis Amazon Web Services (AWS) dan ditampilkan pada web monitoring interface serta layar OLED prototipe. Pengujian sensitivitas terhadap 31 individu (18 individu non PPOK dan 13 pasien PPOK) menunjukkan perbedaan signifikan pada seluruh sensor: MQ-7 (1,49% vs 5,89%), MQ-3 (4,78% vs 12,0%), MQ-135 (1,43% vs 3,38%), dan MG-811 (6,77% vs 39,22%). Pengujian komunikasi IoT pada 50 sesi transmisi menunjukkan success rate dan integritas data 100%, latency rata-rata kurang dari 1 detik, serta response time sistem dari akuisisi hingga tampil di layar OLED kurang dari 2 detik. Hasil penelitian menunjukkan bahwa prototipe ini berhasil membedakan pola VOCs antara individu non PPOK dan pasien PPOK secara noninvasive, real-time, dan portabel, sehingga berpotensi digunakan sebagai alat monitoring awal VOCs terkait PPOK di fasilitas kesehatan maupun lapangan.

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of death worldwide, with 3.5 million deaths per year according to the World Health Organization (WHO) (2021), and a prevalence in Indonesia reaching 5.6% or approximately 4.8 million patients. This study develops a prototype system for monitoring VOCs associated with COPD based on gas sensors using an ESP32 microcontroller integrated with an Internet of Things (IoT) platform. The system utilizes four gas sensors: MQ-7 for carbon monoxide (CO), MQ-3 for ethanol, MQ-135 for complex VOCs such as ammonia and benzene, and MG-811 for carbon dioxide (CO₂). Sensor readings are converted into percentage values based on a 12-bit Analog-to-Digital Converter (ADC) resolution, then transmitted in real time via the Message Queuing Telemetry Transport (MQTT) protocol to an Amazon Web Services (AWS)-based backend and displayed on a web monitoring interface as well as an OLED screen on the prototype. Sensitivity testing on 31 individuals (18 non-COPD individuals and 13 COPD patients) shows significant differences across all sensors: MQ-7 (1.49% vs 5.89%), MQ-3 (4.78% vs 12.0%), MQ-135 (1.43% vs 3.38%), and MG-811 (6.77% vs 39.22%). IoT communication testing over 50 transmission sessions demonstrates a 100% success rate and data integrity, with an average latency of less than 1 second and a system response time from acquisition to OLED display of under 2 seconds. The results indicate that the proposed prototype is capable of distinguishing VOC patterns between non-COPD individuals and COPD patients in a non-invasive, real-time, and portable manner, making it a potential tool for early VOC-based monitoring of COPD in healthcare facilities and field applications.