Prediksi Konsentrasi CO(GT) Menggunakan Long Short-Term Memory pada Data Sensor Kualitas Udara IoT
DOI:
https://doi.org/10.47065/bulletincsr.v6i4.1162Keywords:
Air Quality; Internet of Things; Long Short-Term Memory; Prediction; Air Quality UCIAbstract
Air quality deterioration has become a major challenge for public health and environmental management in urban areas. Internet of Things (IoT)-based monitoring systems continuously generate sensor data that can be exploited for air quality prediction; however, these datasets commonly contain missing values, noise, and temporal dependencies that may reduce prediction accuracy. This study proposes a Long Short-Term Memory (LSTM)-based model to predict carbon monoxide (CO(GT)) concentrations using the Air Quality UCI dataset, which consists of 9,357 observations and 15 attributes. During preprocessing, -200 values were identified as missing-value indicators, followed by invalid-data handling, Min-Max normalization, and sequence generation using a sliding-window approach with a window size of four. The processed data were divided into training and testing sets using an 80:20 ratio. The prediction model employs a single LSTM layer with 50 hidden units and a Dense output layer and is trained using the Adam optimizer for 50 epochs. Experimental results achieved a Mean Absolute Error (MAE) of 0.0389 and a Root Mean Squared Error (RMSE) of 0.0567, indicating that the proposed model effectively captures temporal patterns in air quality observations with relatively low prediction errors. These findings are consistent with previous studies reporting the effectiveness of LSTM for air quality forecasting and demonstrate its potential to support continuous IoT-based environmental monitoring systems. Future work may incorporate hyperparameter optimization and comparative evaluations with alternative deep learning architectures to further improve predictive performance.
Downloads
References
B. Karimi and S. Samadi, “Long-term exposure to air pollution on cardio-respiratory, and lung cancer mortality: a systematic review and meta-analysis,” J. Environ. Health Sci. Eng., vol. 22, no. 1, pp. 75–95, May 2024, doi: 10.1007/s40201-024-00900-6.
M. S. Tavares, C. T. Rodrigues, S. L. S. de Menezes, and A. M. Moreno, “Health at Risk: Air Pollution and Urban Vulnerability—Perspectives in Light of the 2030 Agenda,” Green Health, vol. 1, no. 3, p. 21, Nov. 2025, doi: 10.3390/greenhealth1030021.
S. Chadalavada et al., “Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review,” Environmental Modelling & Software, vol. 185, p. 106312, Feb. 2025, doi: 10.1016/j.envsoft.2024.106312.
A. Garcia, Y. Saez, I. Harris, X. Huang, and E. Collado, “Advancements in air quality monitoring: a systematic review of IoT-based air quality monitoring and AI technologies,” Artif. Intell. Rev., vol. 58, no. 9, p. 275, Jun. 2025, doi: 10.1007/s10462-025-11277-9.
D. N. Azizah, S. Heranurweni, and L. O. M. Idris, “Internet of Things Based Air Quality Monitoring System with Automatic Notification,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 3, pp. 776–787, Jun. 2025, doi: 10.57152/malcom.v5i3.1945.
D. N. Azizah, S. Heranurweni, and L. O. M. Idris, “Internet of Things Based Air Quality Monitoring System with Automatic Notification,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 3, pp. 776–787, Jun. 2025, doi: 10.57152/malcom.v5i3.1945.
C. Liu et al., “Characteristics and mechanism of a persistent ozone pollution event in Pearl River Delta induced by typhoon and subtropical high,” Atmos. Environ., vol. 310, p. 119964, Oct. 2023, doi: 10.1016/j.atmosenv.2023.119964.
A. E. Edje, M. S. Abd Latiff, and W. H. Chan, “IoT data analytic algorithms on edge-cloud infrastructure: A review,” Digital Communications and Networks, vol. 9, no. 6, pp. 1486–1515, Dec. 2023, doi: 10.1016/j.dcan.2023.10.002.
F. Ares-Robledo, H. Rifà-Pous, and R. Clarisó, “Graph neural networks for anomaly detection: a systematic review of dynamic temporal approaches,” Artif. Intell. Rev., vol. 59, no. 5, p. 129, Mar. 2026, doi: 10.1007/s10462-026-11532-7.
D. C. Joshi et al., “Fine particulate matter (PM2.5, PM10): A silent catalyst for chronic lung diseases in India; a comprehensive review,” Environmental Challenges, vol. 20, p. 101215, Sep. 2025, doi: 10.1016/j.envc.2025.101215.
J. W. Sitanggang, E. Sunarsih, and H. Hasyim, “Literature Review: Analysis of Exposure of Vehicle Emission Gases (Co, No2, So2, Pm2.5, and Pm10) to Public Health Risks,” Journal of Social Research, vol. 2, no. 7, pp. 2278–2287, Jun. 2023, doi: 10.55324/josr.v2i7.1142.
D. P. S. Sudibya, D. A. A. Afaaf, L. Satria, N. U. P. Prasna, and U. Kamal, “Implementasi Kebijakan Baku Mutu Udara Sebagai Bentuk Penanggulanan Pemanasan Global,” Jurnal Ilmu Sosial dan Humaniora, vol. 3, no. 4, pp. 571–598, Jun. 2025, doi: 10.57248/jishum.v3i4.593.
W. M. S. Bandara and W. A. R. De Mel, “Forecasting Nonlinear Time Series with ARIMA, ANN, and Hybrid Models: A Case Study on Inflation Rate in Sri Lanka,” Indonesian Journal of Statistics and Its Applications, vol. 9, no. 1, pp. 145–156, Jun. 2025, doi: 10.29244/ijsa.v9i1p145-156.
M. Karmoude et al., “Machine learning for air quality prediction and data analysis: Review on recent advancements, challenges, and outlooks,” Science of The Total Environment, vol. 1002, p. 180593, Nov. 2025, doi: 10.1016/j.scitotenv.2025.180593.
R. Espinosa, J. Palma, F. Jiménez, J. Kami?ska, G. Sciavicco, and E. Lucena-Sánchez, “A time series forecasting based multi-criteria methodology for air quality prediction,” Appl. Soft Comput., vol. 113, p. 107850, Dec. 2021, doi: 10.1016/j.asoc.2021.107850.
M. Rajesh, R. G. Babu, U. Moorthy, and S. V. Easwaramoorthy, “Machine learning-driven framework for realtime air quality assessment and predictive environmental health risk mapping,” Sci. Rep., vol. 15, no. 1, p. 28801, Aug. 2025, doi: 10.1038/s41598-025-14214-6.
Vella Puspitasari Wijayanti and Adie Wahyudi Oktavia Gama, “Analisis Model Long Short-Term Memory (LSTM) untuk Prediksi Kadar Particulate Matter 2.5,” Jurnal ilmiah Sistem Informasi dan Ilmu Komputer, vol. 6, no. 1, pp. 616–629, Mar. 2026, doi: 10.55606/juisik.v6i1.2228.
C. W. Chen Wang, B. L. Chen Wang, J. C. Bingchun Liu, and X. Y. Jiali Chen, “Air Quality Index Prediction Based on a Long Short-Term Memory Artificial Neural Network Model,” J. Comput. (Taipei)., vol. 34, no. 2, pp. 069–079, Apr. 2023, doi: 10.53106/199115992023043402006.
S. Raheja and S. Malik, “Prediction of Air Quality Using LSTM Recurrent Neural Network,” International Journal of Software Innovation, vol. 10, no. 1, pp. 1–16, Apr. 2022, doi: 10.4018/IJSI.297982.
Y. Sudriani, I. Ridwansyah, and H. A Rustini, “Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia,” IOP Conf. Ser. Earth Environ. Sci., vol. 299, p. 012037, Jul. 2019, doi: 10.1088/1755-1315/299/1/012037.
R. Sudha, A. Damodaran, and G. Manohar, “Enhanced air quality prediction using adaptive residual Bi-LSTM with pyramid dilation and optimal weighted feature selection,” Sci. Rep., vol. 15, no. 1, p. 30428, Aug. 2025, doi: 10.1038/s41598-025-14668-8.
N. S. Muruganandam and U. Arumugam, “Seminal Stacked Long Short-Term Memory (SS-LSTM) Model for Forecasting Particulate Matter (PM2.5 and PM10),” Atmosphere (Basel)., vol. 13, no. 10, p. 1726, Oct. 2022, doi: 10.3390/atmos13101726.
Dani Rofianto et al., “IoT-Based Smart Irrigation and Fertilization System with Realtime Cloud Integration,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 15, no. 1, pp. 34–43, Feb. 2026, doi: 10.22146/jnteti.v15i1.23124.
M. Ansari and M. Alam, “An Intelligent IoT-Cloud-Based Air Pollution Forecasting Model Using Univariate Time-Series Analysis,” Arab. J. Sci. Eng., vol. 49, no. 3, pp. 3135–3162, Mar. 2024, doi: 10.1007/s13369-023-07876-9.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Prediksi Konsentrasi CO(GT) Menggunakan Long Short-Term Memory pada Data Sensor Kualitas Udara IoT
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Asep Arwan Sulaeman, Candra Naya, Ahmad Turmudi Zy, Riyadi Riyadi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
