Potensi algoritma berbasis neural network dan turunannya sebagai prediktor kadar PM2,5 dan PM10: Suatu telaah sistematis
DOI:
https://doi.org/10.25170/djm.v24i2.3314Keywords:
kecerdasan buatan, polusi udara, prediksiAbstract
Pendahuluan: Polusi udara merupakan penyebab utama Penyakit Tidak Menular (PTM) di Asia Tenggara dan telah diidentifikasi sebagai masalah kesehatan global terbesar kedua pada tahun 2016. Strategi prediksi yang efektif sangat penting untuk mengurangi dampak buruknya. Studi ini secara sistematis meninjau penerapan algoritma berbasis Artificial Neural Network (ANN) dalam memprediksi konsentrasi polutan udara, khususnya PM2.5 dan PM10.
Metode: Tinjauan sistematis ini mengikuti pedoman PRISMA. Pencarian sistematis dilakukan pada 4 basis data untuk menemukan studi yang menerapkan model ANN dalam prediksi polusi udara dengan menggunakan data meteorologi dan/atau geografis sebagai input. Ekstraksi data difokuskan pada struktur model, akurasi prediksi, serta perbandingan dengan algoritma kecerdasan buatan lainnya. Model ANN dievaluasi berdasarkan kemampuannya menangani interaksi variabel non-linear yang kompleks, fleksibilitas pada berbagai dataset, dan kinerja prediktif dibandingkan metode lain.
Hasil: Algoritma berbasis ANN secara konsisten menunjukkan performa lebih baik dibandingkan model alternatif dalam memprediksi kadar PM2.5 dan PM10. Kemampuan ANN dalam pembelajaran adaptif serta integrasi berbagai input meningkatkan akurasi prediksi. Beberapa studi melaporkan adanya peningkatan lebih lanjut ketika ANN dikombinasikan dengan metode turunannya.
Simpulan: ANN merupakan alat yang andal dan akurat untuk memprediksi polusi udara serta mendukung kebijakan berbasis bukti dalam pencegahan dan pengelolaan lingkungan. Peran ANN dalam ilmu lingkungan menyoroti inovasi dalam pemodelan prediktif dan membuka peluang integrasi dengan teknologi berkelanjutan.
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