Peramalan Kadar PM10 Menggunakan Algoritma Long Short-Term Memory (LSTM) Sebagai Acuan Ketersediaan Ruang Terbuka Hijau Di Kota Jambi

Ulfa  Khaira; Mutia Fadhila  Putri; Shally  Yanova

doi:10.51454/decode.v5i1.866

Authors

Ulfa Khaira Informatika Universitas Jambi
Mutia Fadhila Putri Informatika Universitas Jambi
Shally Yanova Teknik Lingkungan Universitas Jambi

DOI:

https://doi.org/10.51454/decode.v5i1.866

Keywords:

Long Short-Term Memory, Peramalan, PM10, Polutan

Abstract

Pada tahun 2023 jumlah kendaraan bermotor di Kota Jambi hampir mencapai 960.814 unit. Peningkatan volume lalu lintas akan menyebabkan peningkatan emisi polusi udara, yang dapat mengurangi kualitas udara. Salah satu polutannya adalah PM₁₀, yang berkontribusi terhadap polusi udara. Particulate matter 10 (PM₁₀) adalah partikel materi yang berukuran kurang dari 10 mikrometer. PM₁₀ dapat berdampak negatif pada sistem pernapasan, seperti serangan asma, penurunan fungsi paru-paru, dan bahkan kematian. Salah satu solusi untuk mengatasi masalah polusi udara di Jambi adalah melalui pengembangan prediksi temporal kualitas udara menggunakan data historis. Dengan membangun model prediksi berdasarkan indeks polutan, kita dapat memproyeksikan kualitas udara secara harian. Penelitian ini bertujuan untuk membuat model prediksi konsentrasi PM₁₀ di Kota Jambi dengan metode Long Short Term Memory. Data yang digunakan adalah data konsentrasi PM₁₀ pada bulan Januari sampai Juni 2024 menggunakan algoritma jaringan saraf tiruan Long Short-Term Memory (LSTM). Penelitian ini dilakukan melalui beberapa tahapan yaitu pengumpulan data, praproses data, pembagian data, pembuatan model prediksi konsentrasi PM₁₀, dan MAPE. Penelitian ini menghasilkan model prediksi konsentrasi PM₁₀ dengan nilai RMSE sebesar 0,021 dan MAPE 0,11%, yang berarti model peramalan memiliki peramalan yang sangat akurat. Dengan kemampuannya memprediksi kadar PM₁₀ di masa depan berdasarkan pola historis. Informasi ini krusial dalam menentukan prioritas pengembangan RTH.

References

Abdoli, G., MehrAra, M., & Ebrahim Ardalani, M. (2020). Comparing The Prediction Accuracy Of Lstm And Arima Models For Time-Series With Permanent Fluctuation. Gênero & Direito, 9(2). https://doi.org/10.22478/ufpb.2179-7137.2020v9n2.50782

Aji Tritamtama, K., Sembiring, F. E. S., Choiruddin, A., & Patria, H. (2023). Analysis of Air Pollution (SO2) at Some Point of Congestion in DKI Jakarta. Disease Prevention and Public Health Journal, 17(1), 82–92. https://doi.org/10.12928/dpphj.v17i1.6147

Alasadi, S. A., & Bhaya, W. S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences. https://doi.org/10.3923/jeasci.2017.4102.4107

Chiam, Z., Song, X. P., Lai, H. R., & Tan, H. T. W. (2019). Particulate matter mitigation via plants: Understanding complex relationships with leaf traits. Science of The Total Environment, 688, 398–408. https://doi.org/10.1016/j.scitotenv.2019.06.263

Gupta, A., Moniruzzaman, M., Hande, A., Rousta, I., Olafsson, H., & Mondal, K. K. (2020). Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh. SN Applied Sciences. https://doi.org/10.1007/s42452-020-03829-1

Henderi, H. (2021). Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. IJIIS: International Journal of Informatics and Information Systems, 4(1), 13–20. https://doi.org/10.47738/ijiis.v4i1.73

Huang, R., Wei, C., Wang, B., Yang, J., Xu, X., Wu, S., & Huang, S. (2022). Well performance prediction based on Long Short-Term Memory (LSTM) neural network. Journal of Petroleum Science and Engineering, 208, 109686. https://doi.org/10.1016/j.petrol.2021.109686

Joshi, N., Joshi, A., & Bist, B. (2020). Phytomonitoring and Mitigation of Air Pollution by Plants. In Sustainable Agriculture in the Era of Climate Change (pp. 113–142). Springer International Publishing. https://doi.org/10.1007/978-3-030-45669-6_5

Kumar, J. Y., & Kumar, B. S. (2011). Min max normalization based data perturbation method for privacy protection. International Journal of Computer & Communication Technology.

Latif, N., Selvam, J. D., Kapse, M., Sharma, V., & Mahajan, V. (2023). Comparative Performance of LSTM and ARIMA for the Short-Term Prediction of Bitcoin Prices. Australasian Accounting, Business and Finance Journal, 17(1), 256–276. https://doi.org/10.14453/aabfj.v17i1.15

Lin, W.-C., & Tsai, C.-F. (2020). Missing value imputation: a review and analysis of the literature (2006–2017). Artificial Intelligence Review, 53(2), 1487–1509. https://doi.org/10.1007/s10462-019-09709-4

Maduna, K., & Tomašić, V. (2017). Air pollution engineering. Physical Sciences Reviews, 2(12). https://doi.org/10.1515/psr-2016-0122

Malsa, N., Vyas, V., & Gautam, J. (2021). RMSE calculation of LSTM models for predicting prices of different cryptocurrencies. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-021-01431-1

Mangones, S. C., Jaramillo, P., Rojas, N. Y., & Fischbeck, P. (2020). Air pollution emission effects of changes in transport supply: the case of Bogotá, Colombia. Environmental Science and Pollution Research, 27(29), 35971–35978. https://doi.org/10.1007/s11356-020-08481-1

Meo, S. A., Almutairi, F. J., Abukhalaf, A. A., & Usmani, A. M. (2021). Effect of Green Space Environment on Air Pollutants PM2.5, PM10, CO, O3, and Incidence and Mortality of SARS-CoV-2 in Highly Green and Less-Green Countries. International Journal of Environmental Research and Public Health, 18(24), 13151. https://doi.org/10.3390/ijerph182413151

Mohd Pauzi, N. A., Wah, Y. B., Deni, S. M., Abdul Rahim, S. K. N., & Suhartono. (2021). Comparison of Single and MICE Imputation Methods for Missing Values: A Simulation Study. Pertanika Journal of Science and Technology, 29(2). https://doi.org/10.47836/pjst.29.2.15

Prasetyowati, S. A. D., Ismail, M., Budisusila, E. N., Setiadi, D. R. I. M., & Purnomo, M. H. (2022). Dataset Feasibility Analysis Method based on Enhanced Adaptive LMS method with Min-max Normalization and Fuzzy Intuitive Sets. International Journal on Electrical Engineering and Informatics, 14(1), 55–75. https://doi.org/10.15676/ijeei.2022.14.1.4

Prasher, S., Nelson, L., & Jagdish, M. (2023). Potato leaf disease prediction using RMSProp, Adam and SGD optimizers. 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), 343–347. https://doi.org/10.1109/InCACCT57535.2023.10141714

Priyankara, S., Senarathna, M., Jayaratne, R., Morawska, L., Abeysundara, S., Weerasooriya, R., Knibbs, L. D., Dharmage, S. C., Yasaratne, D., & Bowatte, G. (2021). Ambient PM2.5 and PM10 Exposure and Respiratory Disease Hospitalization in Kandy, Sri Lanka. International Journal of Environmental Research and Public Health, 18(18). https://doi.org/10.3390/ijerph18189617

Ren, L., Wang, T., Sekhari Seklouli, A., Zhang, H., & Bouras, A. (2023). A review on missing values for main challenges and methods. In Information Systems. https://doi.org/10.1016/j.is.2023.102268

Saputra, R., Handika, R. A., & Lestari, R. A. (2019). Analisis Sebaran Polutan Particulate Matter (PM10) Pada Harian Musim Kemarau di Kota Jambi. Jurnal Engineering, 1(1). https://doi.org/10.22437/jurnalengineering.v1i1.6276

Shao, S., McAleer, S., Yan, R., & Baldi, P. (2019). Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2018.2864759

Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2019). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018. https://doi.org/10.1109/ICMLA.2018.00227

Song, X., Liu, Y., Xue, L., Wang, J., Zhang, J., Wang, J., Jiang, L., & Cheng, Z. (2020). Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 186, 106682. https://doi.org/10.1016/j.petrol.2019.106682

Vlasov, D., Ramírez, O., & Luhar, A. (2022). Road Dust in Urban and Industrial Environments: Sources, Pollutants, Impacts, and Management. In Atmosphere. https://doi.org/10.3390/atmos13040607

Wang, Y., Yuan, Y., Wang, Q., Liu, C. G., Zhi, Q., & Cao, J. (2020). Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2020.139133

Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7), 1235–1270. https://doi.org/10.1162/neco_a_01199