Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Penerapan Algoritma Optimasi Chaos pada Jaringan Ridge Polynomial untuk Prediksi Jumlah Pengangguran

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 6 No. 2 (2013)

DOI
10.22146/ijccs.2151
ISSN
2460-7258, 1978-1520
Published
2013-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

AbstrakRidge polynomial neural network (RPNN) awalnya diusulkan oleh Shin dan Ghosh, dibangun dari jumlah peningkatan order pi-sigma neuron (PSN). RPNN mempertahankan pembelajaran cepat, pemetaan yang kuat dari layer tunggal higher order neural network (HONN) dan menghindari banyaknya bobot karena meningkatnya sejumlah input. Algoritma optimasi chaos digunakan dengan memanfaatkan persamaan logistik yang sensitif terhadap kondisi awal, sehingga pergerakan chaos dapat berubah di setiap keadaan dalam skala tertentu menurut keteraturan, ergodik dan mempertahankan keragaman solusi.Algoritma Optimasi Chaos diterapkan pada RPNN dan digunakan untuk prediksi jumlah pengangguran di Kalimantan Barat. Proses pelatihan jaringan menggunakan ridge polynomial neural network, sedangkan pencarian nilai awal bobot dan bias jaringan menggunakan algoritma optimasi chaos. Struktur yang digunakan terdiri dari 6 neuron layer input dan 1 neuron layer output. Data diperoleh dari Badan Pusat Statistik.Hasil dari penelitian ini menunjukkan bahwa algoritma yang diusulkan dapat digunakan untuk prediksi. Kata kunci—prediksi jumlah pengangguran, jaringan syaraf tiruan, algoritma optimasi chaos, ridge polynomial neural network Abstract Ridge polynomial neural network was initially proposed by Shin and Ghosh, made of total increased pi-sigma neural (PSN) orders. Ridge polynomial neural network maintains quick learning, strong mapping of single layer of higher order neural network (HONN) and avoids many weights because total increased inputs. Chaos optimization algorithm is used by utilizing sensitive logistic equation to initial condition, so that chaos movement can change in each condition in specific scale according to orderliness, ergodic, and maintaining solution variety. Chaos optimization algorithm is applied to ridge polynomial neural network and used to predict total unemployed persons in West Kalimantan. Network training process used ridge polynomial neural network; while, initial values and weights and bias of network were found using Chaos optimization algorithm. Structure used consisted of 6 input layer neurons and one output layer neuron. Data were obtained from Central Statistic Agency. The results of research indicated that algorithm proposed could be used to predict Keywords— predict the number of unemployed, neural networks, chaos optimization algorithm, ridge polynomial neural network

Keywords

Collaboration network of the authors

40 authors · 54 co-authorship links · 7 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.

  • Universitas Gadjah Mada
  • Udayana University
  • Institut Teknologi dan Sains Mandala
  • Jenderal Soedirman University
  • Politeknik Gorontalo
  • Universitas Maritim Raja Ali Haji
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sri T. HartatiUniversitas Gadjah Mada6
Aina MusdholifahUniversitas Gadjah Mada4
Edi WinarkoUniversitas Gadjah Mada4
Khabib MustofaUniversitas Gadjah Mada4
Edhy SutantaUniversitas Gadjah Mada4
Agus HarjokoUniversitas Gadjah Mada2
Rahmat BudiarsaUniversitas Gadjah Mada2
Bangun WijayantoUniversitas Gadjah Mada1
Decky HendarsyahUniversitas Gadjah Mada1
Awaluddin AwaluddinAffiliation unknown1
Putri Elfa Mas’udiaAffiliation unknown1
Albert Dian SanoAffiliation unknown1
Abdul WahidInstitut Teknologi dan Sains Mandala1
Nur HasanahAffiliation unknown1
Muhamad Munawar YusroJenderal Soedirman University1
Muhammad FakhrurrifqiAffiliation unknown1
Peggi Sri AstutiUdayana University1
Rahman EramaPoliteknik Gorontalo1
Christian Dwi SuhendraUniversitas Gadjah Mada1
Nola RithaUniversitas Maritim Raja Ali Haji1
Ria AstriratmaUniversitas Gadjah Mada1
Made Arya BudhiAffiliation unknown1
Elizabeth Paskahlia GunawanAffiliation unknown1
Ulfi Saidata AesyiAffiliation unknown1
Anny Kartika SariUniversitas Gadjah Mada1
Ida Bagus Gede SarasvanandaUdayana University1
Indra PratisthaUniversitas Gadjah Mada1
Beta Yudha MahindartaUniversitas Gadjah Mada1
Dian MustikaningrumUniversitas Gadjah Mada1
Susilo RomadhonUniversitas Gadjah Mada1
Muhammad MukharirUniversitas Gadjah Mada1
Amelia Dita HandayaniUniversitas Gadjah Mada1
Nikolas Adhi PrasetyoUniversitas Gadjah Mada1
Susi Eva Maria PurbaUniversitas Gadjah Mada1
Kharis Suryandaru PratamaUniversitas Gadjah Mada1
Ahmad AshariUniversitas Gadjah Mada1
Bernard Renaldy SutejaAffiliation unknown1
Suryo GuritnoAffiliation 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.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (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">Penerapan Algoritma Optimasi Chaos pada Jaringan Ridge Polynomial untuk Prediksi Jumlah Pengangguran</dc:title>
	<dc:creator>Pramitasari, Rina</dc:creator>
	<dc:creator>Wardoyo, Retantyo</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">predict the number of unemployed; neural networks; chaos optimization algorithm;  ridge polynomial neural network</dc:subject>
	<dc:description xml:lang="en-US">AbstrakRidge polynomial neural network (RPNN) awalnya diusulkan oleh Shin dan Ghosh, dibangun dari jumlah peningkatan order pi-sigma neuron (PSN). RPNN mempertahankan pembelajaran cepat, pemetaan yang kuat dari layer tunggal higher order neural network (HONN) dan menghindari banyaknya bobot karena meningkatnya sejumlah input. Algoritma optimasi chaos digunakan dengan memanfaatkan persamaan logistik yang sensitif terhadap kondisi awal, sehingga pergerakan chaos dapat berubah di setiap keadaan dalam skala tertentu menurut keteraturan, ergodik dan mempertahankan keragaman solusi.Algoritma Optimasi Chaos diterapkan pada RPNN dan digunakan untuk prediksi jumlah pengangguran di Kalimantan Barat. Proses pelatihan jaringan menggunakan ridge polynomial neural network, sedangkan pencarian nilai awal bobot dan bias jaringan menggunakan algoritma optimasi chaos. Struktur yang digunakan terdiri dari 6 neuron layer input dan 1 neuron layer output. Data diperoleh dari Badan Pusat Statistik.Hasil dari penelitian ini menunjukkan bahwa algoritma yang diusulkan dapat digunakan untuk prediksi. Kata kunci—prediksi jumlah pengangguran, jaringan syaraf tiruan, algoritma optimasi chaos, ridge polynomial neural network  Abstract Ridge polynomial neural network was initially proposed by Shin and Ghosh, made of total increased pi-sigma neural (PSN) orders. Ridge polynomial neural network maintains quick learning, strong mapping of single layer of higher order neural network (HONN) and avoids many weights because total increased inputs. Chaos optimization algorithm is used by utilizing sensitive logistic equation to initial condition, so that chaos movement can change in each condition in specific scale according to orderliness, ergodic, and maintaining solution variety.             Chaos optimization algorithm is applied to ridge polynomial neural network and used to predict total unemployed persons in West Kalimantan. Network training process used ridge polynomial neural network; while, initial values and weights and bias of network were found using Chaos optimization algorithm. Structure used consisted of 6 input layer neurons and one output layer neuron. Data were obtained from Central Statistic Agency.            The results of research indicated that algorithm proposed could be used to predict Keywords— predict the number of unemployed, neural networks, chaos optimization algorithm, ridge polynomial neural network</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">indoceiss</dc:contributor>
	<dc:date>2013-07-31</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://journal.ugm.ac.id/ijccs/article/view/2151</dc:identifier>
	<dc:identifier>10.22146/ijccs.2151</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 6, No 2 (2012): July</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/2151/1932</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2013 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>