PREDIKSI KUALITAS UDARA DENGAN PEMANFAATAN DATA IOT DAN METODE RNN DENGAN ALGORITMA LSTM, BIDIRECTIONAL LSTM, GRU

OKTAVIADI, BAGUS (2025) PREDIKSI KUALITAS UDARA DENGAN PEMANFAATAN DATA IOT DAN METODE RNN DENGAN ALGORITMA LSTM, BIDIRECTIONAL LSTM, GRU. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Air pollution is one of the major challenges affecting public health and environmental quality. Therefore, accurate air quality prediction is crucial to support data-driven decision-making. This study aims to develop an air quality prediction model utilizing data from Internet of Things (IoT) devices and Recurrent Neural Network (RNN) methods, specifically Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) algorithms. The data includes PM2.5 and PM10 concentration (μg/m³), NO2 (ppb), CO (ppm), O3 (ppb), SO2 (ppb), temperature (°C), and relative humidity (%). These parameters are collected through IoT sensor networks to provide real-time environmental conditions. The research process involves data normalization, correlation analysis between variables, and model training using the three algorithms. Model performance is evaluated based on prediction accuracy, convergence speed, and computational efficiency. This study is expected to identify the most effective algorithm for air quality prediction, enabling its implementation in early warning systems and technologydriven environmental management. Thus, this research contributes significantly to mitigating the impacts of air pollution. Kata kunci: Air quality prediction, IoT, PM2.5, NO2, CO, O3, SO2, temperature, humidity, RNN, LSTM, Bidirectional LSTM, GRU. Polusi udara menjadi salah satu tantangan utama yang memengaruhi kesehatan masyarakat dan kualitas lingkungan. Oleh karena itu, prediksi kualitas udara yang akurat sangat penting untuk mendukung pengambilan keputusan berbasis data. Penelitian ini bertujuan untuk mengembangkan model prediksi kualitas udara menggunakan data dari perangkat Internet of Things (IoT) dan metode Recurrent Neural Network (RNN), khususnya algoritma Long Short-Term Memory (LSTM), Bidirectional LSTM, dan Gated Recurrent Unit (GRU). Data yang digunakan meliputi konsentrasi PM2.5 dan PM10 (μg/m³), NO2 (ppb), CO (ppm), O3 (ppb), SO2 (ppb), suhu (°C), dan kelembaban relatif (%). Data ini dikumpulkan menggunakan jaringan sensor IoT untuk memberikan representasi kondisi lingkungan secara real-time. Proses penelitian melibatkan normalisasi data, analisis korelasi antarvariabel, dan pelatihan model menggunakan ketiga algoritma tersebut. Evaluasi performa model dilakukan berdasarkan akurasi prediksi, kecepatan konvergensi, dan efisiensi komputasi. Hasil penelitian ini diharapkan dapat menunjukkan algoritma yang paling efektif untuk memprediksi kualitas udara, sehingga dapat diterapkan pada sistem peringatan dini dan pengelolaan lingkungan berbasis teknologi. Dengan demikian, penelitian ini diharapkan memberikan kontribusi signifikan dalam upaya mitigasi dampak polusi udara. Kata kunci: Prediksi kualitas udara, IoT, PM2.5, NO2, CO, O3, SO2, suhu, kelembaban, RNN, LSTM, Bidirectional LSTM, GRU

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 140
NIM/NIDN Creators:	41521110008
Uncontrolled Keywords:	Prediksi kualitas udara, IoT, PM2.5, NO2, CO, O3, SO2, suhu, kelembaban, RNN, LSTM, Bidirectional LSTM, GRU
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika > 004.6 Interfacing and Communications/Tampilan Antar Muka (Interface) dan Jaringan Komunikasi Komputer > 004.67 Wide Area Network (WAN)/Wide Area Network > 004.678 Internet (World Wide Web)/Internet 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.32 Neural Nets (Neural Network)/Jaringan Saraf Buatan 600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 658 General Management/Manajemen Umum > 658.6 Quality Management/Manajemen Kualitas
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	11 Aug 2025 01:57
Last Modified:	11 Aug 2025 01:57
URI:	http://repository.mercubuana.ac.id/id/eprint/96733

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