Electronic Theses and Dissertation

Abstrak- Permasalahan emisi gas berbau dari sampah organik memerlukan sistem pemantauan yang mampu bekerja secara real-time, mengingat akumulasi gas tersebut dapat menimbulkan dampak buruk bagi kesehatan serta ketidaknyamanan bagi masyarakat di sekitar lingkungan Tempat Pembuangan Sampah (TPS). Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem monitoring kualitas udara berbasis Internet of Things (IoT) menggunakan sensor SHT31-D untuk mendeteksi suhu dan kelembapan, dan sensor TGS2602 digunakan sebagai indikator keberadaan gas berbau pada lingkungan sampah organik, diantaranya yaitu gas amonia (NH₃), hidrogen sulfida (H₂S), dan senyawa organik volatil (Volatile Organic Compounds/VOCs) dengan pengolahan data oleh ESP32 dan visualisasi melalui website monitoring yang dapat diakses secara real-time. Metode penelitian meliputi perancangan sistem, pengambilan data pada dua lokasi berbeda yaitu TPS Pasar Aceh dan TPS Rukoh, serta pengujian berdasarkan variasi jarak dan kondisi lingkungan. Hasil pengujian menunjukkan bahwa sensor SHT31-D memiliki tingkat galat sebesar 5,94% untuk suhu dengan rentang toleransi 2,90%–4,73%, serta tingkat galat sebesar 1,68% untuk kelembapan dengan rentang toleransi 0,65%–1,26%. Pada pengujian sensor TGS2602, diperoleh bahwa konsentrasi gas memiliki hubungan terhadap suhu dengan persamaan regresi y = 0,3497x - 6,5177 dan nilai koefisien determinasi (R²) sebesar 0,3092, serta terhadap kelembapan dengan persamaan regresi y = -0,0983x + 11,052 dan nilai koefisien determinasi (R²) sebesar 0,1357. Hasil pengukuran menunjukkan bahwa rata-rata konsentrasi gas di TPS Rukoh sebesar 3,328 ppm, lebih tinggi dibandingkan TPS Pasar Aceh sebesar 2,401 ppm. Pengujian berdasarkan variasi jarak menunjukkan bahwa rata-rata konsentrasi gas pada jarak 30 cm sebesar 2,858 ppm, lebih tinggi dibandingkan pada jarak 2 meter sebesar 1,475 ppm. Sistem mampu mengirimkan data setiap 1 detik tanpa kehilangan data dan melakukan pemulihan koneksi otomatis dengan rata-rata waktu 9,2 detik, sehingga mendukung monitoring secara real-time dan historis.

Abstract- Odorous gas emissions from organic waste require a monitoring system capable of operating in real time, as the accumulation of such gases can adversely affect human health and reduce the comfort of communities surrounding waste disposal areas. This study aims to design and implement an Internet of Things (IoT)-based air quality monitoring system using an SHT31-D sensor for temperature and humidity measurement and a TGS2602 sensor as an indicator of odor-related gases commonly found in organic waste environments, including ammonia (NH₃), hydrogen sulfide (H₂S), and volatile organic compounds (VOCs). The system employs an ESP32 microcontroller for data processing and a web-based monitoring platform for real-time visualization. The research methodology includes system design, data collection at two different locations, namely TPS Pasar Aceh and TPS Rukoh, and performance evaluation under varying distance and environmental conditions. The results show that the SHT31-D sensor achieved an error rate of 5.94% for temperature, with a tolerance range of 2.90%–4.73%, and 1.68% for humidity, with a tolerance range of 0.65%–1.26%. Analysis of the TGS2602 sensor data revealed a relationship between gas concentration and temperature, represented by the regression equation y = 0.3497x − 6.5177 with a coefficient of determination (R²) of 0.3092, and between gas concentration and humidity, represented by the regression equation y = −0.0983x + 11.052 with an R² value of 0.1357. The average gas concentration measured at TPS Rukoh was 3.328 ppm, which was higher than that measured at TPS Pasar Aceh (2.401 ppm). Distance-based testing showed that the average gas concentration at 30 cm was 2.858 ppm, compared with 1.475 ppm at 2 m. The system successfully transmitted data at one-second intervals without data loss and automatically restored network connectivity with an average recovery time of 9.2 s, thereby supporting both real-time and historical monitoring