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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

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36 authors · 96 co-authorship links

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Co-authorOrganisationJoint articles
Georgina Maria TinungkiAffiliation unknown5
Nasrah SirajangAffiliation unknown3
La Podje TalangkoAffiliation unknown2
Andi Kresna JayaAffiliation unknown2
Rizki AdiputraAffiliation unknown2
Risma RismaAffiliation unknown1
Ika FitrianaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Raupong RaupongAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Ratmila MammiAffiliation unknown1
Aqilah Salsabila RahmanAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Riska RasyidAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Nurhidaya LAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Sonya SonyaAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Nuralyatussa’ adaAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Muhammad FadilAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation 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 2 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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