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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) SWARNA
Michel, Michel
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Advancing precision in air quality forecasting through machine learning integration Komarudin, Muhamad; Ratna Sulistiyanti, Sri; Suharso, Suharso; Irsyad, Muhammad; Dian Septama, Hery; Yulianti, Titin; Sophian, Ali; Michel, Michel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2113-2122

Abstract

In an era where environmental concerns are escalating, air quality forecasting emerges. Forecasting is a crucial tool for addressing the adverse impacts of pollution on public health and ecosystems. In urban centers like Bandar Lampung, economic activities intensify pollution levels. This condition leveraging advanced machine learning forecasting methods can significantly mitigate these effects. This study evaluates the precision of long short-term memory (LSTM) and Prophet methods in predicting air quality. This study utilizes data from January 12, 2022 to November 9, 2023. The results reveal a distinct advantage of the LSTM method over the Prophet. The LSTM method showcases superior accuracy across all evaluation metrics. Specifically, the LSTM method achieved an average root mean squared error (RMSE) of 5.38, mean absolute error (MAE) of 3.94, and mean absolute percentage error (MAPE) of 0.07. In contrast, the Prophet method recorded higher error rates, with an average RMSE of 18.48, MAE of 15.61, and MAPE of 0.25. These numbers underscore the LSTM method's robustness and reliability in forecasting air quality. The result highlights its potential as a pivotal resource for environmental monitoring and policymaking to safeguard public health and promote sustainable urban development.
PENINGKATAN KETERLIBATAN SISWA DI SEKOLAH: PERAN GURU, ORANG TUA DAN TEMAN SEBAYA Riasnugrahani, Missiliana; Djono, Olivia Budi; Nasya, Amanda Eillen; Khalisha, Mazaya Afatharani; Fadhiya, Raysha Auliya; Michel, Michel; Sabrina, Azra Nur; Belinda, Carissa; Manalu, Cicilya Theodora; Azka, Zauzan
SWARNA: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 12 (2024): SWARNA : Jurnal Pengabdian Kepada Masyarakat, Desember 2024
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/swarna.v3i12.1468

Abstract

Pembelajaran positif diperlukan oleh siswa agar dapat terlibat secara emosi, pikiran dan perilaku. Siswa yang terlibat di sekolah akan tampak aktif dalam kegiatan sekolah, mengerjakan tugas dan belajar dengan baik. Faktor-faktor pembentuk keterlibatan sekolah (school engagement) adalah parental involvement, parenat autonomy support dan parent structure. Melalui metode psikoedukasi orang tua dan guru diberikan materi tentang bagaimana memahami anak dan menunjukkan kepedulian pada kegiatan anak, mendorong kemandirian serta memberikan aturan yang jelas. Sementara siswa kelas 1-6 diberikan psikoedukasi melalui permainan edukatif yang mengajarkan dan menanamkan nilai-nilai pertemanan, saling menghormati, empati dan saling mengasihi. Nilai-nilai ini diharapkan dapat menghambat perilaku negatif seperti bullying. Hasil kegiatan menunjukkan semua faktor orang tua berkaitan erat dengan keterlibatan siswa di sekolah, dan siswa pun memahami pentingnya menjalin pertemanan yang positif, untuk membentuk lingkungan belajar yang positif.
Co-Authors Azka, Zauzan Belinda, Carissa Djono, Olivia Budi Fadhiya, Raysha Auliya Hery Dian Septama Khalisha, Mazaya Afatharani Manalu, Cicilya Theodora Missiliana Riasnugrahani Muhamad Komarudin Muhammad Irsyad Nasya, Amanda Eillen Ratna Sulistiyanti, Sri Sabrina, Azra Nur Sophian, Ali Suharso Suharso Titin Yulianti