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The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

Aptisi Transactions on Technopreneurship (ATT) · Vol. 4 No. 3 (2022) · hlm. 313–324

DOI
10.34306/att.v4i3.282
ISSN
2656-8888, 2655-8807
Terbit
2022-10-31
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables.

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  • Universitas Raharja
  • Satya Wacana Christian University
  • Universitas Buddhi Dharma
  • Universitas Catur Insan Cendekia Cirebon
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Irwan SembiringSatya Wacana Christian University21
Nuke Puji Lestari SantosoUniversitas Raharja14
Ninda LutfianiUniversitas Raharja14
Alfiah KhoirunisaUniversitas Raharja8
Dwi ApriliasariAfiliasi tidak diketahui7
Shofiyul MillahAfiliasi tidak diketahui7
Agung RizkyAfiliasi tidak diketahui5
Sutarto WijonoAfiliasi tidak diketahui5
Adam FaturahmanAfiliasi tidak diketahui5
Untung RahardjaUniversitas Raharja4
Ade IrianiSatya Wacana Christian University4
Hindriyanto Dwi PurnomoSatya Wacana Christian University4
Ihsan Nuril HikamAfiliasi tidak diketahui4
Marviola HardiniAfiliasi tidak diketahui4
Yulia Putri Ayu SanjayaAfiliasi tidak diketahui4
Muhtarom MuhtaromAfiliasi tidak diketahui3
Nesti Anggraini SantosoAfiliasi tidak diketahui3
Natasya Aprila YusufAfiliasi tidak diketahui3
Alwiyah AlwiyahAfiliasi tidak diketahui3
Nuke Puji Lestari SantosoAfiliasi tidak diketahui3
Sri WatiniAfiliasi tidak diketahui3
Desy AprianiAfiliasi tidak diketahui2
Mohamad RakhmansyahAfiliasi tidak diketahui2
Lena MagdalenaAfiliasi tidak diketahui2
Umi NarimawatiAfiliasi tidak diketahui2
Joko SiswantoAfiliasi tidak diketahui2
Fitra Putri OgandaUniversitas Raharja2
Hendry HendrySatya Wacana Christian University2
Tarisya RamadhanAfiliasi tidak diketahui2
Nikita IvanovAfiliasi tidak diketahui2
Raihan RaihanAfiliasi tidak diketahui2
Mochamad Heru Riza ChakimAfiliasi tidak diketahui2
Chandra LukitaUniversitas Catur Insan Cendekia Cirebon2
Nanda SeptianiAfiliasi tidak diketahui2
Benny DaniawanUniversitas Buddhi Dharma1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://att.aptisi.or.id/index.php/att/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5</dc:title>
	<dc:creator xml:lang="en">Rahardja, Untung </dc:creator>
	<dc:creator xml:lang="en">Aini, Qurotul </dc:creator>
	<dc:creator xml:lang="en">Sunarya, Po Abas </dc:creator>
	<dc:creator xml:lang="en">Manongga, Danny </dc:creator>
	<dc:creator xml:lang="en">Julianingsih, Dwi</dc:creator>
	<dc:subject xml:lang="en">-</dc:subject>
	<dc:description xml:lang="en"> Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables. </dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2022-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://att.aptisi.or.id/index.php/att/article/view/282</dc:identifier>
	<dc:identifier>10.34306/att.v4i3.282</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 4 No. 3 (2022): November; 313-324</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 4 No 3 (2022): November; 313-324</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v4i3</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/282/188</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Untung  Rahardja, Qurotul  Aini, Po Abas  Sunarya, Danny  Manongga, Dwi Julianingsih</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>