The quality of air can be influenced by the amount of pollution that occurs in an area. The city of Jakarta is ranked in the top ten as the nation’s capital with the worst air quality in the world. Poor air quality both inside and outside the room can have an impact on the emergence of various diseases and even death. For this reason, forecasting of air quality in the city of Jakarta, Indonesia is needed to anticipate the likely impact that will arise. In this study forecasting air quality using the neural network method in which this method has the advantage of being able to solve problems, especially large data samples and has been able to prove in handling non-linear problems. The data collection used is secondary data from the Environmental Service Office of DKI Jakarta Province as many as 2989 records with variables as determinants consisting of 5 of which PM10, SO2, CO, O3, NO2 and 1 output variable are good, moderate, unhealthy and very unhealthy. From the calculations result in this study it is known that the Neural Network method obtained an accuracy performance of 88.86% in which the Lubang Buaya area noted as the most unhealthy air quality
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Implementation of Neural Network Method for Air Quality Forecasting in Jakarta Region
[1] Jensen S S et al., 2017 High resolution multi-scale air quality modelling for all streets in Denmark Transp.Res. Part D Transp. Environ. 52 p. 322–339.
[2] Walter J, 2018, Air pollution and health: Summary. WHO.
[3] Ning X Ji X Li G and Sang N, 2019 Ambient PM2.5 causes lung injuries and coupled energy metabolic disorder Ecotoxicol. Environ. Saf. 170, December 2018 p. 620–626.
[4] Jia J Yuan X Peng X and Yan B, 2019 Cr(VI)/Pb 2+ are responsible for PM2.5-induced cytotoxicity in A549 cells while pulmonary surfactant alleviates such toxicity Ecotoxicol. Environ. Saf. 172, December 2018 p.152–158.
[5] Ajdour A Leghrib R Chaoufi J Chirmata A Menut L and Mailler S, 2020 Towards air quality modeling in Agadir City (Morocco) Mater. Today Proc. 24, xxxx p. 17–23.
[6] Lal B and Tripathy S S, 2012 Prediction of dust concentration in open cast coal mine using artificial neural network Atmos. Pollut. Res. 3, 2 p. 211–218.
[7] Nejadkoorki F and Baroutian S, 2014 Forecasting Extreme PM10 Concentrations Using Artificial Neural Networks Int. J. Environ. Resour. 8, 1 p. 157–164.
[8] Kolehmainen M Martikainen H and Ruuskanen J, 2001 Neural networks and periodic components used in air quality forecasting Atmos. Environ. 35, 5 p. 815–825.
[9] 2020, AIR QUALITY ANALYSIS AND STATISTICS FOR JAKARTA, https://www.iqair.com/.
[10] 2020, Air quality and pollution city ranking, July 2020, https://www.iqair.com/. .
[11] Palani S Liong S Y and Tkalich P, 2008 An ANN application for water quality forecasting Mar. Pollut. Bull.56, 9 p. 1586–1597.
[12] Voukantsis D Karatzas K Kukkonen J R¨as¨anen T Karppinen A and Kolehmainen M, 2011 Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki Sci. Total Environ. 409, 7 p. 1266–1276
[13] Dimopoulos I F Tsiros I X Serelis K and Chronopoulou A, 2004 Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source J.Air Waste Manag. Assoc. 54, 12 p. 1506–1515.
[14] S¨ozen A G¨ulseven Z and Arcaklioˇglu E, 2009 Estimation of GHG emissions in turkey using energy and economic indicators Energy Sources, Part A Recover. Util. Environ. Eff. 31, 13 p. 1141–1159.
[15] Athira V Geetha P Vinayakumar R and Soman K P, 2018 DeepAirNet: Applying Recurrent Networks for Air Quality Prediction Procedia Comput. Sci. 132 p. 1394–1403.
[16] Ghaderi A Sanandaji B M and Ghaderi F, 2017 Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting ii.
[17] Lin S W Shiue Y R Chen S C and Cheng H M, 2009 Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks Expert Syst. Appl. 36, 9 p. 11543–11551.
[18] Kassomenos P Karakitsios S and Papaloukas C, 2006 Estimation of daily traffic emissions in a South-European urban agglomeration during a workday. Evaluation of several “what if” scenarios Sci. Total Environ. 370, 2–3 p. 480–490.
[19] Balram D Lian K Y and Sebastian N, 2019 Air quality warning system based on a localized PM2.5 soft sensor using a novel approach of Bayesian regularized neural network via forward feature selection Ecotoxicol. Environ. Saf. 182, June p. 109386.
[20] Abdul-Wahab S A and Al-Alawi S M, 2002 Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks Environ. Model. Softw. 17, 3 p. 219–228.
[21] Li G and Shi J, 2010 On comparing three artificial neural networks for wind speed forecasting Appl. Energy 87, 7 p. 2313–2320.
[22] Li G Shi J and Zhou J, 2011 Bayesian adaptive combination of short-term wind speed forecasts from neural network models Renew. Energy 36, 1 p. 352–359.
[23] Cheng S Li L Chen D and Li J, 2012 A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling J. Environ. Manage. 112 p. 404–414.
[24] Kristiyanti D A and Wahyudi M, 2017 Feature selection based on Genetic algorithm, particle swarm optimization and principal component analysis for opinion mining cosmetic product review in 2017 5th International Conference on Cyber and IT Service Management, CITSM 2017.
[25] Wahyudi M and Kristiyanti D A, 2016 Sentiment analysis of smartphone product review using support vector machine algorithm-based particle swarm optimization J. Theor. Appl. Inf. Technol. 91, 1.
[26] C. W D, 2009 Projects In Computing And Information System A Student’s Guide England: Addison-Wesley.
[27] Vercellis C, 2009 Business Intelligence: Data Mining and Optimization for Decision Making Bus. Intell. Data Min. Optim. Decis. Mak. p. 1–417.
[28] Peraturan Pemerintah no. 41 tentang Pengendalian Pencemaran udara, 1999, Peraturan Pemerintah no. 41 tentang Pengendalian Pencemaran udara, Jakarta