Analisis Struktur Terbaik Neural Network dengan Algoritma Backpropagation dalam Memprediksi Indeks Kandungan Sulfida (SO2) di Ibu Kota Jakarta
Abstract
Polusi udara merupakan masalah lingkungan yang umum terjadi di kota-kota besar di tanah air, tidak terkecuali Ibu Kota Jakarta. Tingginya jumlah penduduk di Jakarta menyebabkan konsentrasi polusi udara semakin tinggi karena peningkatan jumlah kendaraan. Kondisi ini diperburuk dengan banyaknya limbah yang dihasilkan oleh pabrik dari berbagai industri. Sulfida (SO2) merupakan salah satu polutan dengan konsentrasi tertinggi di Jakarta dengan total beban emisi sebesar 19.7 kton. Oleh karena itu, prediksi indeks kandungan SO2, merupakan isu penelitian yang penting karena zat SO2 dapat berdampak terhadap berbagai faktor, seperti lingkungan, pertanian, dan kesehatan. Tujuan dari penelitian ini adalah menemukan model terbaik dalam melakukan prediksi SO2 di Jakarta menggunakan Artificial Neural Network (ANN). Jenis algoritma ANN yang digunakan adalah backpropagation. Lebih lanjut, model prediksi dibangun dan dibandingkan berdasarkan tiga fungsi aktivasi dan skema pembagian data yang berbeda untuk memperoleh struktur atau arsitektur model ideal dengan tujuan mengoptimalkan hasil prediksi. Model dievaluasi menggunakan nilai Mean Absolute Percentage Error (MAPE) terkecil. Hasil penelitian menunjukkan bahwa model terbaik untuk prediksi indeks kandungan SO2 adalah model yang menerapkan fungsi aktivasi Tanh dengan skema pembagian data 90% data pelatihan dan 10% data pengujian. Model tersebut memperoleh nilai MAPE sebesar 15.87412%, dan akurasi sebesar 84.12588%. Hal ini mengindikasikan bahwa model memiliki tingkat akurasi yang cukup baik dalam memprediksi indeks kandungan SO2 di Ibu Kota Jakarta dan dapat dijadikan sebagai pendekatan alternatif dalam meningkatkan efektivitas pengendalian SO2.
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DOI: https://doi.org/10.26418/justin.v12i2.76166
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