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Meramalkan Curah Hujan di Kabupaten Maros dengan Menggunakan Metode Adaptive Neuro Fuzzy Inference System

ESTIMASI: Journal of Statistics and Its Application · Vol. 7 No. 1 (2026) · pp. 12–23

DOI
10.20956/ejsa.v7i1.26433
ISSN
2721-379X, 2721-3803
Published
2026-05-05
Publisher
Hasanuddin University
Language
en

Abstract

The adaptive neuro-fuzzy inference system or ANFIS method is a hybrid of the fuzzy time series method and artificial neural networks. This algorithm maps the input data in the input layer to the target in the output layer via neurons in the hidden layer using time series data. The working principle of ANFIS has layers that function as input and output. This study aims to obtain the results of rainfall forecasting using the ANFIS method in Maros Regency, South Sulawesi. This research is divided into training data and testing data with details of 292 training data and 73 test data. Then the forecasting results were obtained using 73 test data, namely the period October 20 - October 31, which obtained a value of 0.384% from the calculation of MAPE (Mean Absolute Percentage Error) in the very good forecasting category. The correlation coefficient was obtained by 0.99 with a strong correlation category. So, it can be concluded that the ANFIS method can predict rainfall in Maros Regency with a good degree of accuracy.

Keywords

Collaboration network of the authors

40 authors · 110 co-authorship links · showing the 40 most connected

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  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Georgina Maria TinungkiHasanuddin University5
Nasrah SirajangHasanuddin University5
Nurtiti SunusiHasanuddin University3
La Podje TalangkoHasanuddin University2
Saleh AfAffiliation unknown2
Andi Kresna JayaHasanuddin University2
Rizki AdiputraHasanuddin University1
Sitti SahrimanHasanuddin University1
Ika FitrianaHasanuddin University1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Ratmila MammiAffiliation unknown1
Aqilah Salsabila RahmanAffiliation unknown1
Riska RasyidHasanuddin University1
Mukrimin AdamAffiliation unknown1
Nurhidaya LAffiliation unknown1
Sonya SonyaAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nuralyatussa’ AdaAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1
Siswanto SiswantoHasanuddin University1
Andi Isna YunitaHasanuddin University1
Muhammad FadilAffiliation unknown1
A. Muthiah Nur AngrianyHasanuddin University1
Rini JamalAffiliation unknown1
Andi M. Alfin BasoHasanuddin University1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1
Alimatun NajihaHasanuddin University1
Fadhila Febriyanti NajamuddinHasanuddin University1
Mayashari MayashariHasanuddin University1
Anisa AnisaHasanuddin University1
Mustabsyirah MustabsyirahHasanuddin University1
Agung Muhammad TakdirHasanuddin University1
Sri Astuti ThamrinHasanuddin University1
Nuravia NuraviaAffiliation 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.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Meramalkan Curah Hujan di Kabupaten Maros dengan Menggunakan Metode Adaptive Neuro Fuzzy Inference System</dc:title>
	<dc:creator xml:lang="en">Tandirerung, Rael Hofni</dc:creator>
	<dc:creator xml:lang="en">Herdiani, Erna Tri</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:subject xml:lang="en">Rainfall</dc:subject>
	<dc:subject xml:lang="en">Fuzzy Time Series</dc:subject>
	<dc:subject xml:lang="en">Generalized Bell</dc:subject>
	<dc:subject xml:lang="en">Fuzzy Inference System</dc:subject>
	<dc:subject xml:lang="en">Neural Network, Adaptive Neuro Fuzzy Inference System</dc:subject>
	<dc:description xml:lang="en">The adaptive neuro-fuzzy inference system or ANFIS method is a hybrid of the fuzzy time series method and artificial neural networks. This algorithm maps the input data in the input layer to the target in the output layer via neurons in the hidden layer using time series data. The working principle of ANFIS has layers that function as input and output. This study aims to obtain the results of rainfall forecasting using the ANFIS method in Maros Regency, South Sulawesi. This research is divided into training data and testing data with details of 292 training data and 73 test data. Then the forecasting results were obtained using 73 test data, namely the period October 20 - October 31, which obtained a value of 0.384% from the calculation of MAPE (Mean Absolute Percentage Error) in the very good forecasting category. The correlation coefficient was obtained by 0.99 with a strong correlation category. So, it can be concluded that the ANFIS method can predict rainfall in Maros Regency with a good degree of accuracy.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2026-05-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/26433</dc:identifier>
	<dc:identifier>10.20956/ejsa.v7i1.26433</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 7, No. 1, Januari, 2026 : Estimasi; 12-23</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/26433/14379</dc:relation>
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