Publications

@inproceedings{10.1145/3575882.3575913b,

title = {Impact of Air Pollution on Solar Radiation in Megacity Jakarta},

author = {Inna Syafarina and Arnida Lailatul Latifah and Intan Nuni Wahyuni and Rido Dwi Ismanto and Ariani Indrawati and Mohammad Rosyidi and Windy Iriana and Sheila Dewi Ayu Kusumaningtyas and Ahmad Daudsyah Imami and Erma Yulihastin},

url = {https://doi.org/10.1145/3575882.3575913},

doi = {10.1145/3575882.3575913},

isbn = {9781450397902},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications},

pages = {158–162},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Indonesia},

series = {IC3INA '22},

abstract = {Air pollution can intrude on the process of solar radiation reaching the earth’s surface, disrupting the earth’s heat balance. Global warming is one of its consequences. This study aims to analyze the impact of air pollution on solar radiation using Random Forest (RF) and Support Vector Regression (SVR) models. We use six pollutant types to predict the diffuse solar radiation, i.e., PM2.5, PM10, NO2, SO2, CO, and O3. Besides, near-surface temperature and sunshine duration are also expected to influence solar radiation or vice versa. The models are applied in two locations in Jakarta, Kemayoran and Jagakarsa, from January-August 2019. Based on the model performance, RF outperformed compared to the SVR model. RF model found that all variables, pollutants, temperature, and sunshine duration, impact the solar radiation in both locations. While the SVR model showed that the solar radiation in Kemayoran is affected by all variables, excluding O3. Meanwhile, PM2.5, PM10, NO2, temperature, and sunshine duration affect the solar radiation in Jagakarsa. Overall, PM2.5 is one of the top three most influential pollutants.},

keywords = {air pollution, prediction, radiation, random forest, support vector machine},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3575882.3575920,

title = {Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network},

author = {Ayu Shabrina and Irma Palupi and Bambang Ari Wahyudi and Intan Nuni Wahyuni and Mulya Diana Murti and Arnida Lailatul Latifah},

url = {https://doi.org/10.1145/3575882.3575920},

doi = {10.1145/3575882.3575920},

isbn = {9781450397902},

year  = {2023},

date = {2023-01-01},

booktitle = {Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications},

pages = {194–198},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Indonesia},

series = {IC3INA '22},

abstract = {Carbon emissions produced by forest fires contribute to the global emission increase. The amount of carbon emission may indicate the severity of the fires. In a dry climate condition, forest fires become an unexpected serious problem. This paper investigates the effect of climate variables on forest fires in Sumatra from 1998 to 2018. We employ two methods, Random Forest (RF) and Artificial Neural Network (ANN) to predict the carbon emission in 2019-2021. The total emission over the domain and the fire distribution map are compared in both models. As a result, the RF model is more accurate in predicting the location and intensity in 2019 but overestimates in 2020-2021. This indicates that the RF model gives a slightly better prediction when the carbon emission is high. This result is consistent with the evaluation metrics showing that ANN mostly gives smaller errors. Also, we found that the climate variables are still relevant to describe the carbon emissions through both models with importance scores of more than .},

keywords = {Artificial Neural Network, Carbon Emission, Forest Fire, random forest, Sumatra},

pubstate = {published},

tppubtype = {inproceedings}

}