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All Journal Jurnal Ilmiah Informatika Komputer JSM (Jurnal SIFO Mikroskil) JUTI: Jurnal Ilmiah Teknologi Informasi CESS (Journal of Computer Engineering, System and Science) Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Mantik REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer INFOKUM International Journal of Research and Applied Technology (INJURATECH) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pendidikan dan Pengabdian Masyarakat
Roni Yunis
Fakultas Informatika, Program Studi Sistem Informasi. Universitas Mikroskil Medan
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : CogITo Smart Journal

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Hybridization Model for Air Pollution Prediction Using Time Series Data Roni Yunis; Andri Andri; Djoni Djoni
CogITo Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i1.619.422-435

Abstract

In recent years, data science analysis, particularly time series predictions, has been widely employed across various industrial sectors. However, time series data presents high complexity, especially in seasonal patterns such as monthly, daily, or hourly fluctuations. Irregular fluctuations and external factors increasingly challenge accurate predictions. Therefore, this research proposes a hybrid approach combining SVR-SARIMA, SVR-Prophet, LSTM-SARIMA, and LSTM-Prophet to enhance time series prediction accuracy. This study followed the OSEMN methodology approach: gathering data, cleaning data, exploring data, developing models, and interpreting crucial aspects of problem-solving. Seasonal effect predictions indicated a rise in SO2 and NO2 during dry and rainy seasons until the next two years (average daily increments of 0.0831 μg/m3 for SO2 and 0.0516 μg/m3 for NO2). Estimates suggest a decrease in the order of three particles. The evaluation showed that the SVR model performed better compared to the other three models (RMSE 7.765, MAE 5.477, and MAPE 0.261). The best-performing hybrid model was LSTM-Prophet (99.74% accuracy) with RMSE 12.319, MAE 12.057, and MAPE 0.259 values.
Co-Authors Andri, Andri Angela Angela Angelia Angelia Anugrahi Bawani Sipayung Ayu Ulfa Jamila Bella Merlin Siregar Chandra Wijaya Cosmas Samuel Daeli Desi Arisandy Dika Jur Aeini Silaen Dito Putro Utomo Djoni Djoni Djoni Elly . Elly, Elly Erwin S Panjaitan Erwin S. Panjaitan Erwin S. Panjaitan Erwin Setiawan Panjaitan Erwin Setiawan Panjaitan Erwin Setiawan Panjaitan Fauziatul Laila Ibsah Fristiani Theresia Br Sitepu Hita Jonhariono Sihotang Kridanto Surendro Kristian Telaumbanua Kristian Telaumbanua Kristiani, Ria Liandani, Suci Marchell Rianto Mario Sukamto Megawati - Megawati Megawati Mohammad Yusup Mutiara Sinaga Putri Nurul Adinda Roma Indah Situmorang Rumada Yulan Manurung Salsalina Br Sembiring Sirait, Pahala Stanley Howard Suminar Ariwibowo Suminar Ariwibowo Suminar Ariwibowo Tomy Wijaya Verdy Simbolon Vince Amelia Sirait