Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Improving multilayer perceptron on rainfall data using modified genetics algorithm

IAES International Journal of Artificial Intelligence · Vol. 14 No. 5 (2025) · hlm. 3994–4005

DOI
10.11591/ijai.v14.i5.pp3994-4005
ISSN
2252-8938, 2089-4872
Terbit
2025-10-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Rainfall prediction is essential for managing water resources, agriculture, and disaster response, particularly in regions affected by climate variability. This study introduces a modified genetic algorithm (MGA) to optimize hyperparameters of a multilayer perceptron (MLP) for rainfall forecasting. The MGA incorporates elitism to retain top-performing solutions and adaptive selection based on model accuracy. The proposed MGA–MLP model was tested on rainfall datasets from Australia and Indonesia (BMKG). Experimental results show that configurations with two hidden layers, rectified linear unit (ReLU) activation and limited-memory Broyden Fletcher Goldfarb Shannon (LBFGS) optimizer, a learning rate of 0.001 and 1000 epochs consistently delivered strong performance. The model achieved accuracies of 86.02% and 79.05%, respectively. These findings indicate that MGA significantly improves MLP performance and provides a reliable, generalizable method for rainfall prediction across diverse climatic conditions.

Kata kunci

Jaringan kolaborasi para penulis

16 penulis · 33 tautan kepenulisan bersama · 4 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • University of Brawijaya
  • Saga University
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Yuita Arum SariUniversity of Brawijaya1
Alfisyar Jefry PranataUniversity of Brawijaya1
Umu Sa’adahUniversity of Brawijaya1
Mohammad Dicky SaifullahUniversity of Brawijaya1
Kwardiniya AndawaningtyasUniversity of Brawijaya1
Diva KurnianingtyasUniversity of Brawijaya1
Indriati IndriatiUniversity of Brawijaya1
Nathan DaudUniversity of Brawijaya1
Kohei AraiSaga University1
Brian RahardiUniversity of Brawijaya1
Estri Laras ArumningtyasAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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="en-US">Improving multilayer perceptron on rainfall data using modified genetics algorithm</dc:title>
	<dc:creator>Marji, Marji</dc:creator>
	<dc:creator>Mahmudi, Wayan Firdaus</dc:creator>
	<dc:creator>Handamari, Endang Wahyu</dc:creator>
	<dc:creator>Santoso, Edy</dc:creator>
	<dc:creator>Arifin, Maulana Muhamad</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning; Multilayer Perceptron; Modified Genetics Algorithm; Hyperparameter Tuning</dc:subject>
	<dc:subject xml:lang="en-US">Chromosome selection; Hyperparameter tuning; Modified genetics algorithm; Multilayer perceptron; Rainfall prediction</dc:subject>
	<dc:description xml:lang="en-US">Rainfall prediction is essential for managing water resources, agriculture, and disaster response, particularly in regions affected by climate variability. This study introduces a modified genetic algorithm (MGA) to optimize hyperparameters of a multilayer perceptron (MLP) for rainfall forecasting. The MGA incorporates elitism to retain top-performing solutions and adaptive selection based on model accuracy. The proposed MGA–MLP model was tested on rainfall datasets from Australia and Indonesia (BMKG). Experimental results show that configurations with two hidden layers, rectified linear unit (ReLU) activation and limited-memory Broyden Fletcher Goldfarb Shannon (LBFGS) optimizer, a learning rate of 0.001 and 1000 epochs consistently delivered strong performance. The model achieved accuracies of 86.02% and 79.05%, respectively. These findings indicate that MGA significantly improves MLP performance and provides a reliable, generalizable method for rainfall prediction across diverse climatic conditions.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-10-01</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://ijai.iaescore.com/index.php/IJAI/article/view/27490</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i5.pp3994-4005</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 5: October 2025; 3994-4005</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27490/14755</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Marji, Wayan Firdaus Mahmudi, Endang Wahyu Handamari, Edy Santoso, Maulana Muhamad Arifin</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>