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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Journal of Technology and Informatics (JoTI) Simpatik: Jurnal sistem Informasi dan Informatika Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Jurnal Pendidikan Sains dan Komputer Academic Journal of Computer Science Research (AJCSR) Archive: Jurnal Pengabdian Kepada Masyarakat
Firdaus, Diash
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Prediksi Konsentrasi PM2.5 dan PM10 Menggunakan Lstm dengan Optimasi Hyperparameter Melalui Hyperband Chazar, Chalifa; Putra Darmawan, Rafi; Firdaus, Diash
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

  Polusi udara merupakan permasalahan lingkungan yang berdampak serius terhadap kesehatan masyarakat, terutama karena kandungan particulate matter seperti PM2.5 dan PM10. Permasalahan ini semakin mengkhawatirkan seiring dengan meningkatnya aktivitas industri dan transportasi di kawasan perkotaan. Paparan PM2.5 dan PM10 diketahui berkontribusi terhadap peningkatan risiko penyakit Infeksi Saluran Pernapasan Akut (ISPA). Berbagai penelitian sebelumnya telah memanfaatkan model Long Short-Term Memory (LSTM) untuk memprediksi kualitas udara dan menunjukkan hasil yang cukup baik. Namun, penelitian penelitian tersebut juga menunjukan bahwa performa model dapat ditingkatkan melalui optimasi hyperparameter. Penelitian ini menggunakan algoritma Hyperband untuk mengoptimalkan hyperparameter model LSTM dalam memprediksi konsentrasi PM2.5 dan PM10. Konfigurasi hyperparameter terbaik yang diperoleh dari optimasi menggunakan Hyperband terdiri atas 1 layer, 128 unit LSTM, dropout rate sebesar 0,5, learning rate sebesar 0,01, dan batch size sebesar 16. Hasil kinerja model dievaluasi menggunakan metrik Root Mean Square Error (RMSE) dan mendapatkan hasil sebesar sebesar 5,5148 untuk PM2.5 dan 11,1197 untuk PM10. Hasil ini lebih baik jika dibandingkan dengan model LSTM tanpa optimasi Hyperband, yang memperoleh nilai RMSE sebesar 6,6061 untuk PM2.5 dan 13,0133 untuk PM10. Hasil ini menunjukkan bahwa penggunaan Hyperband dalam optimasi hyperparameter mampu menurunkan tingkat kesalahan prediksi pada PM2.5 dan PM10.   Abstract Air pollution is an environmental problem that has serious impacts on public health, particularly due to particulate matter such as PM2.5 and PM10. This issue is becoming increasingly concerning with the rise of industrial and transportation activities in urban areas. Exposure to PM2.5 and PM10 is known to contribute to a higher risk of Acute Respiratory Infections (ARI). Previous studies have utilized Long Short-Term Memory (LSTM) models to predict air quality and showed fairly good results, but also indicated that model performance can be improved through hyperparameter optimization. This study employs the Hyperband algorithm to optimize the hyperparameters of the LSTM model in predicting PM2.5 and PM10 concentrations. The best hyperparameter configuration obtained from Hyperband optimization consists of 1 layer, 128 LSTM units, a dropout rate of 0.5, a learning rate of 0.01, and a batch size of 16. Model performance was evaluated using the Root Mean Square Error (RMSE), achieving 5.5148 for PM2.5 and 11.1197 for PM10, which is better compared to the LSTM model without Hyperband optimization that obtained RMSE values of 6.6061 for PM2.5 and 13.0133 for PM10. These results indicate that the use of Hyperband in hyperparameter optimization can reduce prediction errors for PM2.5 and PM10.
Co-Authors Adiningrum, Nur Tri Ramadhanti Ady P., Alfalery Al-Hakim, Dany Prastya Ali G. M., Rangga Alwan, Haikal H. Azka, Fadhlan M. Binsar, Ray Chazar, Chalifa Daffa Ulhaq, Muhammad Rifqi Dafy, Muhammad Zufar Erlangga, Muhammad Khairurrafa Fahira, Fahira Fauzan, Paris Achmad Hassan Ifrayim, Mikail Heryani, Rena Idi Sumardi, Idi Krisna Yudha, Muhammad Kulsum, Yuni Maulana, Azka Muhammad Ichwan Nurrizziqie, Ahmad Mufie PPA , M Davin Putra Darmawan, Rafi Putra, Bob Byan Handoko Ramadhan, Bachtiar Rianti, Resa Rizky Herdiansyah, Muhammad Suryana, Agung Zaidan, Muammar Alfien