Pemodelan Sistem Monitoring Kualitas Udara Pintar Berbasis Internet of Things dengan Pendekatan Machine Learning
DOI:
https://doi.org/10.25126/jtiik.2025129195Kata Kunci:
air quality, Bandung City, LSTM, prediction, real-timeAbstrak
Penelitian ini bertujuan untuk merancang arsitektur model sistem pemantauan kualitas udara di Kota Bandung menggunakan empat parameter polutan utama: PM1.0, PM2.5, PM10, dan CO. Sistem ini dirancang dengan memanfaatkan algoritma Long Short-Term Memory (LSTM) untuk memprediksi kualitas udara harian berdasarkan data historis. Fokus penelitian meliputi perancangan desain arsitektur sistem, model data, dan metode prediksi, yang disusun berdasarkan analisis arsitektur sebelumnya serta kajian literatur. Salah satu elemen penting dan kebaruan dalam penelitian ini adalah penggunaan sensor ZH03B untuk pemantauan kualitas udara secara real-time yang memberikan solusi hemat biaya dan dapat diandalkan. Kombinasi antara sensor real-time dan algoritma LSTM menghasilkan tingkat akurasi prediksi kualitas udara sebesar 88%. Hasil evaluasi model menunjukkan nilai Root Mean Square Error (RMSE) sebesar 2,68 yang mencerminkan kinerja prediksi yang baik. Selain itu, pendekatan ini memberikan peningkatan signifikan dibandingkan metode konvensional yang sering kali kurang responsif terhadap perubahan kualitas udara secara dinamis. Penelitian ini memberikan dasar yang kuat untuk pengembangan sistem monitoring kualitas udara yang lebih akurat dan adaptif. Arsitektur yang diusulkan dapat menjadi acuan untuk pengembangan sistem monitoring kualitas udara di masa depan.
Abstract
This research aims to design the architecture of an air quality monitoring system model in Bandung City using four main pollutant parameters: PM1.0, PM2.5, PM10, and CO. The system is designed by utilising the Long Short-Term Memory (LSTM) algorithm to predict daily air quality based on historical data. The focus of the research includes the design of the system architecture, data model, and prediction method, which were developed based on previous architecture analysis and literature review. One important element and novelty in this research is the use of the ZH03B sensor for real-time air quality monitoring which provides a cost-effective and reliable solution. The combination of the real-time sensor and the LSTM algorithm resulted in an air quality prediction accuracy rate of 88%. The model evaluation results show a Root Mean Square Error (RMSE) value of 2.68 which reflects good prediction performance. In addition, this approach provides a significant improvement over conventional methods that are often less responsive to dynamic changes in air quality. This research provides a solid foundation for the development of a more accurate and adaptive air quality monitoring system. The proposed architecture can serve as a reference for the development of future air quality monitoring systems.
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Ansari, M. and Alam, M., 2024. An Intelligent IoT-Cloud-Based Air Pollution Forecasting Model Using Univariate Time-Series Analysis. Arabian Journal for Science and Engineering, [online] 49(3), pp.3135–3162. https://doi.org/10.1007/s13369-023-07876-9.
Bae, S. and Hong, Y.C., 2018. Health effects of particulate matter. Journal of the Korean Medical Association, 61(12), pp.749–755. https://doi.org/10.5124/jkma.2018.61.12.749.
Diskominfo, 2023. Kualitas Udara Kota Bandung di Ambang Batas Sedang. [online] Available at: <https://jabarprov.go.id/berita/kualitas-udara-kota-bandung-di-ambang-batas-sedang-10128>.
Geneva: World Health Organization, 2021. WHO global air quality guidelines. Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, pp.1–360.
Hapidin, D.A., Saputra, C., Maulana, D.S., Munir, M.M. and Khairurrijal, K., 2019. Aerosol chamber characterization for commercial particulate matter (PM) sensor evaluation. Aerosol and Air Quality Research, 19(1), pp.181–194. https://doi.org/10.4209/aaqr.2017.12.0611.
Khumaidi, A., Raafi’udin, R. and Solihin, I.P., 2020. Pengujian Algoritma Long Short-Term Memory untuk Prediksi Kualitas Udara dan Suhu Kota Bandung. Jurnal Telematika, 15(1), pp.13–18.
Kusnandar, M., 2020. Permen LHK Nomor 14 Tahun 2020. Permen LHK Nomor 14 Tahun 2020 Tentang Indeks Standar Pencemar Udara (ISPU), pp.1–16.
Li, Z., Zhang, Y. and Wang, Y., 2023. Prediction and Early Warning of Air Quality based on the LSTM-ARIMA Model. Frontiers in Sustainable Development, 3(11), pp.29–38. https://doi.org/10.54691/fsd.v3i11.5722.
Liang, Y.C., Maimury, Y., Chen, A.H.L. and Juarez, J.R.C., 2020. Machine learning-based prediction of air quality. Applied Sciences (Switzerland), 10(24), pp.1–17. https://doi.org/10.3390/app10249151.
Montalvo, L., Fosca, D., Paredes, D., Abarca, M., Saito, C. and Villanueva, E., 2022. An Air Quality Monitoring and Forecasting System for Lima City With Low-Cost Sensors and Artificial Intelligence Models. Frontiers in Sustainable Cities, 4. https://doi.org/10.3389/frsc.2022.849762.
Oktaviani, D.A. and Prasasti, C.I., 2016. The Physical and Chemical Air Quality, Worker’s Characteristics, and Respiratory Symptoms Among Printing Workers in Surabaya. Jurnal Kesehatan Lingkungan, 8(2), p.195. https://doi.org/10.20473/jkl.v8i2.2016.195-205.
Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., Jiang, L. and Cheng, Z., 2020. Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, [online] 186(November 2019), p.106682. https://doi.org/10.1016/j.petrol.2019.106682.
Suriano, D. and Prato, M., 2023. An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring. Sensors, 23(8). https://doi.org/10.3390/s23083976.
Suryantoro, H. and Kusriyanto, M., 2023. Sistem Monitoring Partikel (PM2.5) Air Purifier untuk Mengetahui Kualitas Udara Berbasis Sensor PMS5003 dan Arduino. 4887, pp.88–96.
Tran, H.D., Huang, H.Y., Yu, J.Y. and Wang, S.H., 2023. Forecasting hourly PM2.5 concentration with an optimized LSTM model. Atmospheric Environment, [online] 315(August), p.120161. https://doi.org/10.1016/j.atmosenv.2023.120161.
Truong, T.P., Nguyen, D.T. and Truong, P.V., 2021. Design and deployment of an IoT-based air quality monitoring system. International Journal of Environmental Science and Development, 12(3), pp.139–145. https://doi.org/10.18178/IJESD.2021.12.5.1331.
Utamy, A.R., Siswanto and Sutarti, 2023. Prototype Wireless Sensor Network Sistem Pengukuran Debu Dan Suhu Udara Berbasis Mqtt Server. PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer, 10(2), pp.152–164. https://doi.org/10.30656/prosisko.v10i2.7158.
Wang, C., Bingchun Liu, J.C. and Jiali Chen, X.Y., 2023. Air Quality Index Prediction Based on a Long Short-Term Memory Artificial Neural Network Model. 電腦學刊, 34(2), pp.069–079. https://doi.org/10.53106/199115992023043402006.
Wibawa, I.M.S. and Putra, I.K., 2021. Design and manufacture of air quality measurements based on arduino ATmega 2560 using dust ZH03A laser sensor. International journal of physical sciences and engineering, 5(1), pp.8–15. https://doi.org/10.29332/ijpse.v5n1.800.
Xayasouk, T., Lee, H.M. and Lee, G., 2020. Air pollution prediction using long short-term memory (LSTM) and deep autoencoder (DAE) models. Sustainability (Switzerland), 12(6). https://doi.org/10.3390/su12062570.
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