Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

Dwipayana, I Kadek Agus; sugiartawan, putu

doi:10.22146/ijccs.85584

Open access Jurnal SINTA 2

Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 3 (2023) · pp. 301–312

DOI: 10.22146/ijccs.85584
ISSN: 2460-7258, 1978-1520
Published: 2023-07-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

The distribution of pests in rice plants results in significant losses in production and damage to rice plants for farmers, seen from data on the area of rice borer attacks in the province of Bali in Tabanan district. Therefore, by predicting the distribution of rice pests, we can know the pattern of pest attacks so that we can anticipate them because predicting can provide accuracy and error values through the test results. One of the prediction models is BPNN, where BPNN's advantages for solving complex problems are very suitable for use where large amounts of data are involved and many input/output variables, BPNN is also capable of modeling nonlinear relationships between input and output variables, which may be difficult to capture by this type of predictive model. other. Backpropagation includes supervised learning, which means it can learn from labeled examples and can make accurate predictions on new, unlabeled data. Split data using K-fold cross-validation serves to assess the process performance of an algorithmic method by dividing random data samples and grouping the data as many as K k-fold values.

Keywords

Collaboration network of the authors

9 authors · 13 co-authorship links · 1 across different organisations

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Institut Bisnis dan Teknologi Indonesia
Universitas Mercu Buana Yogyakarta
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
I Gede Iwan Sudipa	Institut Bisnis dan Teknologi Indonesia	3
I Komang Arya Ganda Wiguna	Institut Bisnis dan Teknologi Indonesia	3
I Made Yudiana	Affiliation unknown	1
Paholo Iman Prakoso	Universitas Mercu Buana Yogyakarta	1
I Putu Yudi Pratama	Institut Bisnis dan Teknologi Indonesia	1
Ni Nengah Dita Ardriani	Institut Bisnis dan Teknologi Indonesia	1
Gede Agus Santiago	Institut Bisnis dan Teknologi Indonesia	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network</dc:title>
	<dc:creator>Dwipayana, I Kadek Agus</dc:creator>
	<dc:creator>sugiartawan, putu</dc:creator>
	<dc:subject xml:lang="en-US">Artificial intelligence; computer science</dc:subject>
	<dc:subject xml:lang="en-US">Rice Pest Prediction; Backpropagation Analysis; Machine Learning</dc:subject>
	<dc:description xml:lang="en-US">The distribution of pests in rice plants results in significant losses in production and damage to rice plants for farmers, seen from data on the area of rice borer attacks in the province of Bali in Tabanan district. Therefore, by predicting the distribution of rice pests, we can know the pattern of pest attacks so that we can anticipate them because predicting can provide accuracy and error values through the test results. One of the prediction models is BPNN, where BPNN's advantages for solving complex problems are very suitable for use where large amounts of data are involved and many input/output variables, BPNN is also capable of modeling nonlinear relationships between input and output variables, which may be difficult to capture by this type of predictive model. other. Backpropagation includes supervised learning, which means it can learn from labeled examples and can make accurate predictions on new, unlabeled data. Split data using K-fold cross-validation serves to assess the process performance of an algorithmic method by dividing random data samples and grouping the data as many as K k-fold values.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Departemen Of Technic Informatika Institut Bisnis Dan Teknologi Indonesia</dc:contributor>
	<dc:date>2023-07-31</dc:date>
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	<dc:identifier>10.22146/ijccs.85584</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 3 (2023): July; 301-312</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/85584/36979</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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