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Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 6 No. 2 (2025) · hlm. 820–828

DOI
10.37385/jaets.v6i2.6117
ISSN
2715-6079, 2715-6087
Terbit
2025-06-08
Penerbit
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Bahasa
en

Abstrak

An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. ANN has one of its tasks, namely prediction. Multilayer perceptron (MLP) is one of the ANN methods that can be prediction all of data. Where the prediction needs to be reviewed because the prediction process does not always run normally. So, it takes a good measurement accuracy in order to get an accuracy sensitivity. The accuracy technique in this paper is carried out using Mean Absolute Percentage Error (MAPE) based on absolute error and detection rate. The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error.

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  • Universitas Halu Oleo
  • Universitas Sumatera Utara
  • State University of Makassar
  • Universitas Muhammadiyah Sumatera Utara
  • Media Design School
  • Politeknik Negeri Medan
  • Telkom University
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Rekan penulisOrganisasiArtikel bersama
Muharman LubisTelkom University11
Mahyuddin K. M. NasutionUniversitas Sumatera Utara4
Sarah PurnamawatiUniversitas Sumatera Utara4
Aminuddin AminuddinInstitut Agama Islam Negeri Kendari2
Syahril EfendiUniversitas Sumatera Utara2
Elviawaty Muisa ZamzamiUniversitas Sumatera Utara2
Opim Salim SitompulUniversitas Sumatera Utara2
Amirullah AbduhState University of Makassar2
Muhammad Azwar ParammaUniversitas Islam Negeri Sultan Aji Muhammad Idris Samarinda2
Yandres Answo Djedelbert LaoState University of Makassar1
Sukardi WedaState University of Makassar1
Sundi KombaUniversitas Halu Oleo1
Abdul Salam SahadiaUniversitas Halu Oleo1
Jopang JopangUniversitas Halu Oleo1
Muhamad KamalUniversitas Halu Oleo1
Abdul KadirUniversitas Halu Oleo1
Rodi RodiUniversitas Halu Oleo1
Chika Zafa LibrastiAfiliasi tidak diketahui1
Firdaus AnasUniversitas Veteran Republik Indonesia Makassar1
Ninik Endang PurwatiUniversitas Halu Oleo1
Esti MulyawatiUniversitas Halu Oleo1
Muh YusufUniversitas Halu Oleo1
Dewinta YulianaUniversitas Halu Oleo1
Akhyar AbdullahUniversitas Halu Oleo1
Altun AltunUniversitas Halu Oleo1
Makmur KambolongUniversitas Halu Oleo1
Nyilan SariUniversitas Halu Oleo1
Mustakim MustakimUniversitas Halu Oleo1
Jamsir JamsirUniversitas Halu Oleo1
Wa Ode MariosinAfiliasi tidak diketahui1
Eka SuaibUniversitas Halu Oleo1
La Ode Ahmad DarmawanUniversitas Halu Oleo1
Rahman RahmanUniversitas Halu Oleo1
Masnia MasniaUniversitas Halu Oleo1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate </dc:title>
	<dc:creator xml:lang="en">Al-Khowarizmi, Al-Khowarizmi</dc:creator>
	<dc:creator xml:lang="en">Rahmat, Romi Fadillah</dc:creator>
	<dc:creator xml:lang="en">Watts, Michael J</dc:creator>
	<dc:creator xml:lang="en">Akrim, Akrim</dc:creator>
	<dc:creator xml:lang="en">Lubis, Arif Ridho</dc:creator>
	<dc:creator xml:lang="en">Basri, Muhammad</dc:creator>
	<dc:subject xml:lang="en">Prediction</dc:subject>
	<dc:subject xml:lang="en">MLP</dc:subject>
	<dc:subject xml:lang="en">MAPE</dc:subject>
	<dc:subject xml:lang="en">Detection Rate</dc:subject>
	<dc:description xml:lang="en">An artificial neural network (ANN) is a network of a group of units to be processed which is modeled based on the behavior of human neural networks. ANN has one of its tasks, namely prediction. Multilayer perceptron (MLP) is one of the ANN methods that can be prediction all of data. Where the prediction needs to be reviewed because the prediction process does not always run normally. So, it takes a good measurement accuracy in order to get an accuracy sensitivity. The accuracy technique in this paper is carried out using Mean Absolute Percentage Error (MAPE) based on absolute error and detection rate. The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error.</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2025-06-08</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.yrpipku.com/index.php/jaets/article/view/6117</dc:identifier>
	<dc:identifier>10.37385/jaets.v6i2.6117</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS); 820-828</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v6i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/6117/4438</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Al-Khowarizmi Al-Khowarizmi, Romi Fadillah Rahmat, Michael J Watts, Akrim Akrim, Arif Ridho Lubis, Muhammad Basri</dc:rights>
</oai_dc:dc>