ANALISIS IMPLEMENTASI SUPPORT VECTOR MACHINE DAN RANDOM FOREST UNTUK PREDIKSI KATEGORI INDEKS KUALITAS UDARA JAKARTA
DOI:
https://doi.org/10.24252/instek.v10i1.56477Keywords:
Analisis Korelasi, Cross-Validation, Indeks Kualitas udara, Machine learning, Support Vector Machine, Random ForestAbstract
Kualitas udara yang buruk di Jakarta berdampak signifikan terhadap kesehatan masyarakat dan lingkungan. Oleh karena itu, diperlukan metode prediksi untuk membantu pengambilan kebijakan mitigasi polusi udara. Penelitian ini memprediksi kategori indeks kualitas udara dengan metode Support Vector Machine (SVM) dan Random Forest menggunakan data polutan (PM10, PM2.5, SO₂, CO, O₃, NO₂) dari Kaggle tahun 2021, meliputi PM10, PM2.5, SO2, CO, O3, dan NO2. Analisis korelasi menunjukkan bahwa PM10 dan PM2.5 memiliki hubungan yang sangat kuat (r = 0.96), menandakan keterkaitan erat dalam menentukan tingkat polusi udara. SVM dan Random Forest disimulasikan dengan berbagai rasio pembagian data latih dan uji (10:90, 15:85, 20:80, 25:75, dan 30:70), serta menggunakan stratified k-fold cross-validation untuk meningkatkan validitas hasil dan mengurangi potensi overfitting. Hasil evaluasi menunjukkan bahwa kedua model memberikan performa yang sangat baik dengan akurasi lebih dari 97% pada seluruh skenario pembagian data. Random Forest mencapai akurasi maksimum 100% pada rasio 15:85, sementara SVM mencatatkan akurasi tertinggi 98,9% pada rasio 25:75. Hasil cross-validation menunjukkan bahwa Random Forest mencapai akurasi 100% pada simulasi menggunakan 5-folds, dengan nilai presisi, recall, dan F1-score yang juga 100%. Di sisi lain SVM menunjukkan akurasi sedikit lebih rendah yaitu 97,30% namun lebih konsisten dengan standar deviasi 2,50%.
Downloads
References
[1] T. Xayasouk and H. Lee, “AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING,” Jun. 2018, pp. 71–79. doi: 10.2495/AIR180071.
[2] S. Khan et al., “Modelling the Impact of Road Dust on Air Pollution: A Sustainable System Dynamics Approach,” E3S Web of Conferences, vol. 430, p. 01176, Oct. 2023, doi: 10.1051/e3sconf/202343001176.
[3] A. C. T. Silva, P. T. B. S. Branco, and S. I. V. Sousa, “Impact of COVID-19 Pandemic on Air Quality: A Systematic Review,” Int J Environ Res Public Health, vol. 19, no. 4, p. 1950, Feb. 2022, doi: 10.3390/ijerph19041950.
[4] A. Amalia, A. Zaidiah, and I. N. Isnainiyah, “Prediksi Kualitas Udara Menggunakan Algoritma K-Nearest Neighbor,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 7, no. 2, pp. 496–507, May 2022, doi: 10.29100/jipi.v7i2.2843.
[5] N. F. Prih Waryatno, N. P. Kinanti, and Taryono, “Kondisi Pencemaran Udara pada Saat Periode Lebaran 2022 di Wilayah Jakarta,” Buletin GAW Bariri, vol. 3, no. 2, Dec. 2022, doi: 10.31172/bgb.v3i2.68.
[6] C. Harjono, L. Gianto, R. Sidik, and D. L. Widaningrum, “FORECASTING AIR POLLUTION DRIVEN BY VEHICLE GROWTH, PUBLIC TRANSPORT, INDUSTRY, AND HOUSEHOLD WASTE,” Journal of Environmental Science and Sustainable Development, vol. 7, no. 2, Dec. 2024, doi: 10.7454/jessd.v7i2.1272.
[7] P. Lestari, M. K. Arrohman, S. Damayanti, and Z. Klimont, “Emissions Inventory of Air Pollutants from Anthropogenic Sources in Jakarta,” May 15, 2023. doi: 10.5194/egusphere-egu23-6686.
[8] A. H. R. Inaku and C. Novianus, “Pengaruh Pencemaran Udara PM 2,5 dan PM 10 Terhadap Keluhan Pernapasan Anak di Ruang Terbuka Anak di DKI Jakarta,” ARKESMAS (Arsip Kesehatan Masyarakat), vol. 5, no. 2, pp. 9–16, Dec. 2020, doi: 10.22236/arkesmas.v5i2.4990.
[9] S. Jia, “Effect of combined strategy on mitigating air pollution in China,” Clean Technol Environ Policy, vol. 23, no. 3, pp. 1027–1043, Apr. 2021, doi: 10.1007/s10098-020-02013-8.
[10] A. Gupta, H. K. Mall, and S. Janarthanan., “Rainfall Prediction Using Machine Learning,” in 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR), IEEE, Mar. 2022, pp. 1–5. doi: 10.1109/ICAITPR51569.2022.9844203.
[11] K. Kumar and B. P. Pande, “Air pollution prediction with machine learning: a case study of Indian cities,” International Journal of Environmental Science and Technology, vol. 20, no. 5, pp. 5333–5348, May 2023, doi: 10.1007/s13762-022-04241-5.
[12] A. Toha, P. Purwono, and W. Gata, “Model Prediksi Kualitas Udara dengan Support Vector Machines dengan Optimasi Hyperparameter GridSearch CV,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 4, no. 1, pp. 12–21, May 2022, doi: 10.12928/biste.v4i1.6079.
[13] Anisa Ma’u Luthfi and Fatkhurokhman Fauzi, “Perbandingan Klasifikasi Random Forest, Support Vector Machines, dan LGBM Pada Klasifikasi Kualitas Udara di Jakarta,” JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia), vol. 9, no. 2, pp. 99–108, Aug. 2024, doi: 10.32528/justindo.v9i2.1912.
[14] A. S. Handayani, S. Soim, T. E. Agusdi, and N. L. Husni, “Air Quality Classification Using Support Vector Machine,” Computer Engineering and Applications Journal, vol. 10, no. 1, pp. 55–69, Feb. 2021, doi: 10.18495/comengapp.v10i1.350.
[15] S. Ketu and P. K. Mishra, “Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare,” Complex & Intelligent Systems, vol. 7, no. 5, pp. 2597–2615, Oct. 2021, doi: 10.1007/s40747-021-00435-5.
[16] Z. Almheiri, M. Meguid, and T. Zayed, “Failure modeling of water distribution pipelines using meta-learning algorithms,” Water Res, vol. 205, p. 117680, Oct. 2021, doi: 10.1016/j.watres.2021.117680.
[17] S. Gocheva-Ilieva, A. Ivanov, and M. Stoimenova-Minova, “Prediction of Daily Mean PM10 Concentrations Using Random Forest, CART Ensemble and Bagging Stacked by MARS,” Sustainability, vol. 14, no. 2, p. 798, Jan. 2022, doi: 10.3390/su14020798.
[18] H. Sunaryanto, M. A. Hasan, and G. Guntoro, “Classification Analysis of Unilak Informatics Engineering Students Using Support Vector Machine (SVM), Iterative Dichotomiser 3 (ID3), Random Forest and K-Nearest Neighbors (KNN),” IT Journal Research and Development, vol. 7, no. 1, pp. 36–42, Aug. 2022, doi: 10.25299/itjrd.2022.8912.
[19] D. Mustafa Abdullah and A. Mohsin Abdulazeez, “Machine Learning Applications based on SVM Classification A Review,” Qubahan Academic Journal, vol. 1, no. 2, pp. 81–90, Apr. 2021, doi: 10.48161/qaj.v1n2a50.
[20] A. Louati, H. Louati, E. Kariri, F. Alaskar, and A. Alotaibi, “Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine,” Applied Sciences, vol. 13, no. 23, p. 12539, Nov. 2023, doi: 10.3390/app132312539.
[21] J. Wang and J. Hu, “A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model,” Energy, vol. 93, pp. 41–56, Dec. 2015, doi: 10.1016/j.energy.2015.08.045.
[22] S. M. Simon, P. Glaum, and F. S. Valdovinos, “Interpreting random forest analysis of ecological models to move from prediction to explanation,” Sci Rep, vol. 13, no. 1, p. 3881, Mar. 2023, doi: 10.1038/s41598-023-30313-8.
[23] X. Xie, M.-J. Yuan, X. Bai, W. Gao, and Z.-H. Zhou, “On the Gini-impurity Preservation For Privacy Random Forests,” in Advances in Neural Information Processing Systems, A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., Curran Associates, Inc., 2023, pp. 45055–45082. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/file/8d6b1d775014eff18256abeb207202ad-Paper-Conference.pdf
[24] N. Syahira and D. B. Arianto, “PREDIKSI TINGKAT KUALITAS UDARA DENGAN PENDEKATAN ALGORITMA K-NEAREST NEIGHBOR,” Jurnal Ilmiah Informatika Komputer, vol. 29, no. 1, pp. 45–59, Apr. 2024, doi: 10.35760/ik.2024.v29i1.10069.
[25] D. Fitrianah, S. Dwiasnati, H. H. H, and K. A. Baihaqi, “Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes,” Faktor Exacta, vol. 14, no. 2, p. 92, Aug. 2021, doi: 10.30998/faktorexacta.v14i2.9297.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Evander Banjarnahor, Ronald Belferik, Wiputra Cendana, Yohanes Adi Saputra Abraham
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
