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All Journal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Saputra , Agung Hari
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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PERBANDINGAN MODEL PREDIKSI DATA MINING DALAM MEMPREDIKSI KONSENTRASI POLUTAN KARBON MONOKSIDA (CO) DI JAKARTA Amanu, Rendy Syahril; Ramadhan, Faiz Ahza; Saputra , Agung Hari
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 1 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i1.12451

Abstract

DKI Jakarta, as the capital of Indonesia, faces serious challenges in terms of air quality. Carbon monoxide (CO) is one of the main air pollutants in Jakarta that is harmful to human health and the environment. Data mining is a method that can be used to predict situations based on a model. The study aims to compare data mining models with the best-performing methods to predict carbon monoxide pollutants in Jakarta. The predictive data mining model of the python library is tested and evaluated based on the evaluation metrics of MASE, RMSSE, MAE, RMSE, MAPE and SMAPE values. The model test results showed that K Neighbors with the Conditional Deseasonalize & Detrending model had the best metric evaluation value to predict CO concentration with the value evaluation metrics of MASE 0.2942, RMSSE 0.2483, MAE 2.7362, RMSE 3.3863, MAPE 0.1975 and SMAPE 0.01993. Overall, K Neighbors with the Conditional Deseasonalize & Detrending model shows good performance to predict CO concentrations in Jakarta, but further adjustments are needed to improve accuracy.
Co-Authors Amanu, Rendy Syahril Ramadhan, Faiz Ahza