Aplikasi Monitor dan Prediksi Tingkat Polusi Udara Berbasis Web dengan Streamlit

Muhammad Ikmal Tawakal; Dony Marulitua Simanungkalit; Yohanes Septian Hildegardis; Reno Nurhadi; Lisnawanty Lisnawanty

Authors

Muhammad Ikmal Tawakal, Universitas Bina Sarana Informatika, Indonesia
Dony Marulitua Simanungkalit, Universitas Bina Sarana Informatika, Indonesia
Yohanes Septian Hildegardis, Universitas Bina Sarana Informatika, Indonesia
Reno Nurhadi, Universitas Bina Sarana Informatika, Indonesia
Lisnawanty Lisnawanty, Universitas Bina Sarana Informatika, Indonesia

Keywords:

Kualitas udara, Benzena (C6H6), Random Forest, XGBoost, Streamlit

Abstract

Penelitian ini mengembangkan model machine learning untuk memprediksi konsentrasi Benzena C6H6(GT) dan merancang aplikasi web berbasis Streamlit untuk monitoring serta prediksi kualitas udara secara real-time. Dataset Air Quality dari UCI (9.357 rekaman per jam, Maret 2004–Februari 2005) digunakan dengan pembersihan nilai hilang, penghapusan NMHC(GT) yang dominan hilang, interpolasi berbasis waktu, serta rekayasa fitur waktu dan meteorologi. Pemodelan regresi membandingkan Linear Regression, Random Forest, dan XGBoost menggunakan pembagian data 80/20, standardisasi fitur, dan evaluasi dengan MAE, MSE, serta R-squared. Hasil menunjukkan Random Forest sebagai model terbaik dengan MAE 1.1444, MSE 3.5262, dan R² 0.8900, mengungguli XGBoost dan Linear Regression. Model terbaik diintegrasikan ke aplikasi Streamlit sehingga pengguna dapat memasukkan parameter seperti suhu dan kelembapan dan memperoleh prediksi konsentrasi Benzena secara langsung melalui antarmuka yang ramah pengguna. Temuan ini menegaskan bahwa kombinasi fitur waktu dan cuaca efektif untuk memodelkan polutan dan implementasi web memungkinkan pemanfaatan praktis untuk pemantauan serta pengambilan keputusan. Secara keseluruhan, solusi ini valid secara prediktif dan layak diterapkan sebagai alat bantu fungsional untuk memonitor tingkat polusi udara.

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Aplikasi Monitor dan Prediksi Tingkat Polusi Udara Berbasis Web dengan Streamlit

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