Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Implementasi Neural Fuzzy Inference System dan Algoritma Pelatihan Levenberg-Marquardt untuk Prediksi Curah Hujan

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 10 No. 2 (2016) · pp. 125–136

DOI
10.22146/ijccs.15532
ISSN
2460-7258, 1978-1520
Published
2016-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Rainfall prediction can be used for various purposes and the accuracy in predicting is important in many ways. In this research, data of rainfall prediction use daily rainfall data from 2013-2014 years at rainfall station in Putussibau, West Kalimantan. Rainfall prediction using four parameters: mean temperature, average humidity, wind speed and mean sea level pressure.This research to determine how performance Neural Fuzzy Inference System with Levenberg-Marquardt training algorithm for rainfall prediction. Fuzzy logic can be used to resolve the linguistic variables used in rule of rainfall. While neural networks have ability to adapt and learning process, due to recognize patterns of data from input need training to prediction. And Levenberg-Marquardt algorithm is used for training because of effectiveness and convergence acceleration.The results showed five models NFIS-LM developed using a variety of membership functions as input obtained that model NFIS-LM with twelve of membership functions and use four inputs, such as mean temperature, average humidity, wind speed and mean sea level pressure gives best results to predict rainfall with values Mean Square Error (MSE) of 0.0262050. When compared with model NN-Backpropagation, NFIS-LM models showed lower accuracy. It is shown from MSE generated where model NN-Backpropagation generate MSE of 0.0167990.

Keywords

Collaboration network of the authors

40 authors · 54 co-authorship links · 7 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Udayana University
  • Universitas Maritim Raja Ali Haji
  • Institut Teknologi dan Sains Mandala
  • Jenderal Soedirman University
  • Politeknik Gorontalo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sri T. HartatiUniversitas Gadjah Mada6
Aina MusdholifahUniversitas Gadjah Mada4
Edi WinarkoUniversitas Gadjah Mada4
Khabib MustofaUniversitas Gadjah Mada4
Edhy SutantaUniversitas Gadjah Mada4
Agus HarjokoUniversitas Gadjah Mada2
Rahmat BudiarsaUniversitas Gadjah Mada2
Bangun WijayantoUniversitas Gadjah Mada1
Decky HendarsyahUniversitas Gadjah Mada1
Awaluddin AwaluddinAffiliation unknown1
Putri Elfa Mas’udiaAffiliation unknown1
Rina PramitasariUniversitas Gadjah Mada1
Albert Dian SanoAffiliation unknown1
Abdul WahidInstitut Teknologi dan Sains Mandala1
Nur HasanahAffiliation unknown1
Muhamad Munawar YusroJenderal Soedirman University1
Muhammad FakhrurrifqiAffiliation unknown1
Peggi Sri AstutiUdayana University1
Rahman EramaPoliteknik Gorontalo1
Christian Dwi SuhendraUniversitas Gadjah Mada1
Ria AstriratmaUniversitas Gadjah Mada1
Made Arya BudhiAffiliation unknown1
Elizabeth Paskahlia GunawanAffiliation unknown1
Ulfi Saidata AesyiAffiliation unknown1
Anny Kartika SariUniversitas Gadjah Mada1
Ida Bagus Gede SarasvanandaUdayana University1
Indra PratisthaUniversitas Gadjah Mada1
Beta Yudha MahindartaUniversitas Gadjah Mada1
Dian MustikaningrumUniversitas Gadjah Mada1
Susilo RomadhonUniversitas Gadjah Mada1
Muhammad MukharirUniversitas Gadjah Mada1
Amelia Dita HandayaniUniversitas Gadjah Mada1
Nikolas Adhi PrasetyoUniversitas Gadjah Mada1
Susi Eva Maria PurbaUniversitas Gadjah Mada1
Kharis Suryandaru PratamaUniversitas Gadjah Mada1
Ahmad AshariUniversitas Gadjah Mada1
Bernard Renaldy SutejaAffiliation unknown1
Suryo GuritnoAffiliation unknown1

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="id-ID">Implementasi Neural Fuzzy Inference System dan Algoritma Pelatihan Levenberg-Marquardt untuk Prediksi Curah Hujan</dc:title>
	<dc:creator>Ritha, Nola</dc:creator>
	<dc:creator>Wardoyo, Retantyo</dc:creator>
	<dc:subject xml:lang="id-ID"></dc:subject>
	<dc:subject xml:lang="id-ID">Levenberg-Marquardt, Neural Fuzzy Inference System, Rainfall Prediction</dc:subject>
	<dc:description xml:lang="id-ID">Rainfall prediction can be used for various purposes and the accuracy in predicting is important in many ways.  In this research, data of rainfall prediction use daily rainfall data from 2013-2014 years at rainfall station in Putussibau, West Kalimantan. Rainfall prediction using four parameters: mean temperature, average humidity, wind speed and mean sea level pressure.This research to determine how performance Neural Fuzzy Inference System with Levenberg-Marquardt training algorithm for rainfall prediction. Fuzzy logic can be used to resolve the linguistic variables used in rule of rainfall. While neural networks have ability to adapt and learning process, due to recognize patterns of data from input need training to prediction. And Levenberg-Marquardt algorithm is used for training because of effectiveness and convergence acceleration.The results showed five models NFIS-LM developed using a variety of membership functions as input obtained that model NFIS-LM with twelve of membership functions and use four inputs, such as mean temperature, average humidity, wind speed and mean sea level pressure gives best results to predict rainfall with values Mean Square Error (MSE) of 0.0262050. When compared with model NN-Backpropagation, NFIS-LM models showed lower accuracy. It is shown from MSE generated where model NN-Backpropagation generate MSE of 0.0167990.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="id-ID"></dc:contributor>
	<dc:date>2016-07-31</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-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/15532</dc:identifier>
	<dc:identifier>10.22146/ijccs.15532</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 10, No 2 (2016): July; 125-136</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/15532/11716</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2016 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>