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All Journal Biota: Jurnal Ilmiah Ilmu-Ilmu Hayati Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL PENGABDIAN KEPADA MASYARAKAT Pekobis : Jurnal Pendidikan, Ekonomi, dan Bisnis Jurnal Mu'allim Jurnal Insan Farmasi Indonesia Reformasi Hukum Trisakti ASH-SHAHABAH : Jurnal Pengabdian Masyarakat Journal of Business, Technology, & Social Science Jurnal Ilmiah Manajemen Ekonomi Dan Akuntansi (JIMEA) Journal of Economics and Business Jurnal Ekonomi Manajemen Dan Bisnis (JEMB) Jurnal Pendidikan & Pengajaran Journal Social Engagement: Jurnal Pengabdian Masyarakat Journal AXEGNAL: Tax and Economic Insights Journal International Journal of Technology and Modeling
Karmilah
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Journal : International Journal of Technology and Modeling

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Predicting Air Pollution Using Simpson Integration Karmilah; Nazwa
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.82

Abstract

Increasing air pollution, especially in urban areas, is a serious issue that has a negative impact on public health and the environment. Accurate prediction of air pollution levels is critical to supporting mitigation efforts and data-driven decision-making. This study aims to develop an air pollution prediction model using the Simpson Integration method, a numerical approach used to calculate integrals with a high degree of accuracy. The data used included concentrations of pollutants such as PM2.5, PM10, and NO2 taken from daily measurements for one year. This method utilizes an interpolation algorithm to model changes in pollutant concentrations as a function of time. Simpson integration is used to calculate the area under the daily pollutant curve that represents the accumulated exposure to air pollution. The results show that this method is able to provide accurate predictions with an average error rate of less than 5% compared to actual data. This model has advantages in computational efficiency over conventional methods such as simple linear regression analysis. These findings prove that Simpson Integration can be effectively applied in air quality prediction and provide important information for governments and the public. This system is expected to support the development of an air pollution early warning system to increase public awareness and help formulate more responsive environmental policies.
Co-Authors Abd. Rizal Ahmad Lughowi, Rifyal Ai Kurnia Andi Indah Lestari Anggun Pratiwi Austin Tee, Selfyana Azdar Setiawan, Muh. Bachtiar, Machdum Badia, Esti Daud, Nur Saadah Delli Yuliana Rahmat Fauzan Redondo Eko Fazli Gerilona Maulina Hamdat, Amin I Putu Anjas Jaya Kusuma Imbaraniati Permana Putri Jamalullai Kahirah, Rahayu Khairani Bakri Lasmita, Witri Lutfi Rohmawati Muh. Resky Naim Muh. Ridwan Malik Muliana Mustafa Musdalipah Mutmainnah, Nur Najwa Khusnul Khotimah Nashrillah, Mulya Pahlawati Nazwa Nisma Ariskha Masdar Nugraha Abhull Azwad Nurdiana Nurhaliza Nurwahyudi, Rifqi Orfyanny Syahreffy Themba Poy Saefullah Zevender Reymon Riance, Weni Rifqi Nur Wahyuni Royani, Irma Rudi Setiawan Samalam, Abd Gafar Santi Oktaviani, Santi Setiawan, Muh. Azdar Siti Patimah SUSANTI Susanti, Rozanila Tee, Selfyana Austin Tihami, M.A Via Nur Afifah Yodha, Agung Wibawa Mahatva