ANALISIS PREDIKSI KUALITAS UDARA BERBASIS INDEKS MENGGUNAKAN INTERNET OF THINGS DAN ALGORITMA MACHINE LEARNING

VIANA, VERA RAHMA and Ubaya, Huda (2025) ANALISIS PREDIKSI KUALITAS UDARA BERBASIS INDEKS MENGGUNAKAN INTERNET OF THINGS DAN ALGORITMA MACHINE LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Kualitas udara merupakan faktor krusial yang memengaruhi kesehatan manusia dan keberlangsungan lingkungan. Peningkatan polusi akibat aktivitas manusia seperti industri dan transportasi mendorong perlunya pemantauan kualitas udara yang efektif dan real-time. Penelitian ini bertujuan untuk mengembangkan model prediksi Indeks Kualitas Udara (AQI) berbasis algoritma Random Forest menggunakan data konsentrasi enam polutan utama, yaitu PM2.5, PM10, CO, NO2, SO2, dan O3. Data yang digunakan diperoleh dari sensor berbasis Internet of Things (IoT) serta dataset publik dengan total lebih dari 155.000 data pengukuran. Proses penelitian meliputi tahapan preprocessing, pelatihan model, evaluasi dengan metrik RMSE dan R², serta validasi eksternal. Hasil analisis menunjukkan bahwa model mampu memprediksi nilai AQI dengan tingkat akurasi yang baik. Selain itu, fitur PM2.5 teridentifikasi sebagai faktor paling berpengaruh dalam penentuan kualitas udara. Dengan integrasi teknologi IoT dan algoritma Machine Learning, sistem ini dapat digunakan sebagai alat bantu dalam mendukung kebijakan mitigasi pencemaran udara dan perlindungan kesehatan masyarakat secara berkelanjutan.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Kualitas Udara, AQI, Internet Of Things, Machine Learning, Random Forest, PM2.5, Smart City
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA158.7 Computer network resources Including the Internet
Divisions: 09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Vera Rahma Viana
Date Deposited: 24 Jul 2025 02:18
Last Modified: 24 Jul 2025 02:18
URI: http://repository.unsri.ac.id/id/eprint/180171

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