| dc.contributor.advisor | Widodo, Bayu | |
| dc.contributor.author | HANIF, MUHAMMAD DAFFA | |
| dc.date.accessioned | 2026-08-03T03:33:20Z | |
| dc.date.available | 2026-08-03T03:33:20Z | |
| dc.date.issued | 2026 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/176838 | |
| dc.description.abstract | Kenyamanan ruang kerja merupakan salah satu faktor yang memengaruhi kesehatan dan produktivitas sehingga diperlukan sistem pemantauan kondisi lingkungan secara berkelanjutan. Penelitian ini bertujuan membuat sistem monitoring kenyamanan ruang kerja berbasis Internet of Things (IoT) menggunakan mikrokontroler ESP32, sensor BME680, dan sensor MAX9814, serta menganalisis karakteristik data lingkungan yang dihasilkan. Sistem mengukur suhu, kelembapan, kualitas udara (Indoor Air Quality/IAQ), dan tingkat kebisingan, kemudian mengirimkan data melalui protokol HTTP ke platform ThingsBoard untuk visualisasi real-time. Evaluasi dilakukan melalui pengujian akurasi sensor, Packet Delivery Ratio (PDR), analisis statistik deskriptif, dan analisis korelasi Pearson. Hasil penelitian menunjukkan akurasi pengukuran suhu sebesar 97,22%, kelembapan 96,24%, tingkat kebisingan 95,78%, serta nilai PDR 97,17%. Analisis statistik menunjukkan bahwa data hasil pemantauan mampu menggambarkan karakteristik kondisi lingkungan serta hubungan antarparameter dengan korelasi terkuat antara kelembapan dan IAQ. Sistem yang dikembangkan mampu menyediakan informasi objektif sebagai pendukung pemantauan dan pengelolaan kenyamanan ruang kerja. | |
| dc.description.abstract | Workspace comfort is an important factor for health and productivity. This study developed an Internet of Things (IoT)-based workspace comfort monitoring system using an ESP32 microcontroller with BME680 and MAX9814 sensors. The system measures temperature, relative humidity, Indoor Air Quality (IAQ), and noise level, then sends the data to the ThingsBoard platform through the HTTP protocol for real-time visualization. System performance was evaluated using sensor accuracy testing, Packet Delivery Ratio (PDR) measurement, descriptive statistics, and Pearson correlation analysis. The system achieved measurement accuracies of 97,22% for temperature, 96,24% for humidity, 95,78% for noise level, and a 97,17% PDR. Statistical analysis described workspace environmental characteristics and relationships among the monitored parameters, with the strongest correlation found between humidity and IAQ. The developed system provides objective environmental information to support workspace comfort monitoring and management. | |
| dc.description.sponsorship | | |
| dc.language.iso | id | |
| dc.publisher | IPB University | id |
| dc.title | Implementasi Sistem Monitoring Kenyamanan Ruang Kerja Berbasis IoT Menggunakan Sensor BME680 dan MAX9814 | id |
| dc.title.alternative | | |
| dc.type | Tugas Akhir | |
| dc.subject.keyword | Statistical analysis | id |
| dc.subject.keyword | Internet of Things | id |
| dc.subject.keyword | Workspace comfort | id |
| dc.subject.keyword | Monitoring Real Time | id |
| dc.subject.keyword | ThingsBoard | id |
| dc.subtype | Undergraduate Theses | |
