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Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 1 (2017) · pp. 38–54

DOI
10.5614/itbj.ict.res.appl.2017.11.1.3
ISSN
2338-5499, 2337-5787
Published
2017-04-30
Publisher
DRI - ITB
Language
eng

Abstract

Countries with a tropical climate, such as Indonesia, are highly dependent on rainfall prediction for many sectors, such as agriculture, aviation, and shipping. Rainfall has now become increasingly unpredictable due to climate change and this phenomenon also affects Indonesia. Therefore, a robust approach is required for more accurate rainfall prediction. The Tsukamoto Fuzzy Inference System (FIS) is one of the algorithms that can be used for prediction problems, but if its membership functions are not specified properly, the prediction error is still high. To improve the results, the boundaries of the membership functions can be adjusted automatically by using a genetic algorithm. The proposed genetic algorithm employs two selection processes. The first one uses the Roulette wheel method to select parents, while the second one uses the elitism method to select chromosomes for the next generation. Based on this approach, a rainfall prediction experiment was conducted for Tengger, Indonesia using historical rainfall data for ten-year periods. The proposed method generated root mean square errors (RMSE) of 6.78 and 6.63 for the areas of Tosari and Tutur respectively. These results are better compared with the results using Tsukamoto FIS and the Generalized Space Time Autoregressive (GSTAR) model from previous studies.

Keywords

Collaboration network of the authors

40 authors · 64 co-authorship links · 9 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.

  • University of Brawijaya
  • State University of Malang
  • Universitas Merdeka Malang
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Yusuf Priyo AnggodoUniversity of Brawijaya3
Asyrofa RahmiUniversity of Brawijaya2
Syaiful AnamUniversity of Brawijaya2
Philip Faster Eka AdiprajaAffiliation unknown2
Adyan Nur AlfiyatinUniversity of Brawijaya2
Firnanda Al Islama Achyunda PutraUniversity of Brawijaya1
Fitri UtaminingrumUniversity of Brawijaya1
Ervin YohannesUniversity of Brawijaya1
Hilman Nuril HadiUniversity of Brawijaya1
Rizal Setya PerdanaUniversity of Brawijaya1
Ghenniy RachmansyahUniversity of Brawijaya1
Ria FebriyanaUniversity of Brawijaya1
Vitara Nindya Putri HasanUniversity of Brawijaya1
Mohammad Zoqi SarwaniUniversity of Brawijaya1
Luthfi HidayatUniversity of Brawijaya1
Rifki Setya ArmandaUniversity of Brawijaya1
Garsinia Ely RianiUniversity of Brawijaya1
Fita Lathifatul Mu’asyarohAffiliation unknown1
Agus Wahyu WidodoUniversity of Brawijaya1
Diva KurnianingtyasUniversity of Brawijaya1
Muchammad Chandra Cahyo UtomoUniversity of Brawijaya1
Fadhli Almu’iini AhdaAffiliation unknown1
Candra Fajri AnandaUniversity of Brawijaya1
Farhanna Mar’iUniversity of Brawijaya1
Cleoputri YusainyUniversity of Brawijaya1
Danang Arbian SulistyoAffiliation unknown1
Qoirul KotimahUniversity of Brawijaya1
Vivi Nur WijayaningrumUniversity of Brawijaya1
Gayatri Dwi SantikaUniversity of Brawijaya1
Agus NabaUniversity of Brawijaya1
Aji Prasetya WibawaState University of Malang1
Nadia Roosmalita SariUniversity of Brawijaya1
Elta SonalithaUniversitas Merdeka Malang1
Gusti Ahmad Fanshuri AlfarisyUniversity of Brawijaya1
Muhammad Halim NatsirUniversity of Brawijaya1
Agung Mustika RizkiInstitut Teknologi Sepuluh Nopember1
Gusti Eka YuliastutiUniversity of Brawijaya1
Andi HamdianahUniversity of Brawijaya1

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://journals.itb.ac.id/index.php/jictra/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">Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method</dc:title>
	<dc:creator>Wahyuni, Ida</dc:creator>
	<dc:creator>Mahmudy, Wayan Firdaus</dc:creator>
	<dc:subject xml:lang="en-US">genetic algorithm</dc:subject>
	<dc:subject xml:lang="en-US">hybrid</dc:subject>
	<dc:subject xml:lang="en-US">prediction</dc:subject>
	<dc:subject xml:lang="en-US">rainfall</dc:subject>
	<dc:subject xml:lang="en-US">roulette wheel</dc:subject>
	<dc:subject xml:lang="en-US">Tsukamoto FIS.</dc:subject>
	<dc:description xml:lang="en-US">Countries with a tropical climate, such as Indonesia, are highly dependent on rainfall prediction for many sectors, such as agriculture, aviation, and shipping. Rainfall has now become increasingly unpredictable due to climate change and this phenomenon also affects Indonesia. Therefore, a robust approach is required for more accurate rainfall prediction. The Tsukamoto Fuzzy Inference System (FIS) is one of the algorithms that can be used for prediction problems, but if its membership functions are not specified properly, the prediction error is still high. To improve the results, the boundaries of the membership functions can be adjusted automatically by using a genetic algorithm. The proposed genetic algorithm employs two selection processes. The first one uses the Roulette wheel method to select parents, while the second one uses the elitism method to select chromosomes for the next generation. Based on this approach, a rainfall prediction experiment was conducted for Tengger, Indonesia using historical rainfall data for ten-year periods. The proposed method generated root mean square errors (RMSE) of 6.78 and 6.63 for the areas of Tosari and Tutur respectively. These results are better compared with the results using Tsukamoto FIS and the Generalized Space Time Autoregressive (GSTAR) model from previous studies.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-04-30</dc:date>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/2577</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2017.11.1.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 1 (2017); 38-54</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/2577/2509</dc:relation>
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