Melola Discovery

← Melola Discovery

Jurnal SINTA 2

Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 6 No. 2 (2019) · pp. 179–186

DOI
10.25126/jtiik.201962900
ISSN
2528-6579, 2355-7699
Published
2019-02-25
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
id

Abstract

Inflasi merupakan salah satu indikator untuk mengukur perkembangan suatu bangsa. Apabila inflasi tidak terkontrol akan memberikan banyak dampak negative terhadap masyarakat disuatu negara. Ada banyak cara untuk mengendalikan inflasi, salah satunya dengan peramalan. Peramalan adalah suatu aktivitas untuk mengetahui kejadian di masa mendatang berdasarkan data masa lalu. Pada penelitian ini menggunakan metode kecerdasan buatan yakni extreme learning machine (ELM). Kelebihan ELM yaitu cepat dalam proses pembelajaran. Berdasarkan penggujian yang dilakukan metode ELM mendapatkan nilai kesalahan sebesar 0.0202008, lebih kecil dibandingkan dengan metode backpropagation sebesar 1.16035821. Hal tersebut membuktikan bahwa metode ELM sangat cocok digunakan untuk peramalan.AbstractInflation is one indicator to measure the development of a nation. If inflation is not controlled will give many negative impacts to the people in a country. There are many ways to control inflation, one with forecasting. Forecasting is an activity to know future events based on past data. In this research using artificial intelligence method is extreme learning machine (ELM). The advantages of ELM are fast in the learning process. Based on ELM testing gets obtained an error value of 0.0202008, smaller than the backpropagation method of 1.16035821. It proves that ELM method is very suitable for forecasting.

Collaboration network of the authors

40 authors · 69 co-authorship links · 5 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
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Asyrofa RahmiUniversity of Brawijaya2
Syaiful AnamUniversity of Brawijaya2
Aprilia Nur FauziyahUniversity of Brawijaya2
Gusti Eka YuliastutiUniversity of Brawijaya2
Firnanda Al Islama Achyunda PutraUniversity of Brawijaya1
Fitri UtaminingrumUniversity of Brawijaya1
Ida WahyuniUniversity 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
Imam CholissodinUniversity of Brawijaya1
Winda CahyaningrumUniversity of Brawijaya1
Irma Lailatul KhoiriyahUniversity of Brawijaya1
Oktavianis KartikasariUniversity of Brawijaya1
Farhanna Mar’iUniversity of Brawijaya1
Cleoputri YusainyUniversity of Brawijaya1
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

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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 27 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="id">Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia</dc:title>
	<dc:creator xml:lang="id">Alfiyatin, Adyan Nur</dc:creator>
	<dc:creator xml:lang="id">Mahmudy, Wayan Firdaus</dc:creator>
	<dc:creator xml:lang="id">Ananda, Candra Fajri</dc:creator>
	<dc:creator xml:lang="id">Anggodo, Yusuf Priyo</dc:creator>
	<dc:description xml:lang="id">Inflasi merupakan salah satu indikator untuk mengukur perkembangan suatu bangsa. Apabila inflasi tidak terkontrol akan memberikan banyak dampak negative terhadap masyarakat disuatu negara. Ada banyak cara untuk mengendalikan inflasi, salah satunya dengan peramalan. Peramalan adalah suatu aktivitas untuk mengetahui kejadian di masa mendatang berdasarkan data masa lalu. Pada penelitian ini menggunakan metode kecerdasan buatan yakni extreme learning machine (ELM). Kelebihan ELM yaitu cepat dalam proses pembelajaran. Berdasarkan penggujian yang dilakukan metode ELM mendapatkan nilai kesalahan sebesar 0.0202008, lebih kecil dibandingkan dengan metode backpropagation sebesar 1.16035821. Hal tersebut membuktikan bahwa metode ELM sangat cocok digunakan untuk peramalan.AbstractInflation is one indicator to measure the development of a nation. If inflation is not controlled will give many negative impacts to the people in a country. There are many ways to control inflation, one with forecasting. Forecasting is an activity to know future events based on past data. In this research using artificial intelligence method is extreme learning machine (ELM). The advantages of ELM are fast in the learning process. Based on ELM testing gets obtained an error value of 0.0202008, smaller than the backpropagation method of 1.16035821. It proves that ELM method is very suitable for forecasting. </dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2019-02-25</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://jtiik.ub.ac.id/index.php/jtiik/article/view/900</dc:identifier>
	<dc:identifier>10.25126/jtiik.201962900</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 6 No 2: April 2019; 179-186</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 6 No. 2: April 2019; 179-186</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201962</dc:source>
	<dc:language>id</dc:language>
	<dc:relation>https://jtiik.ub.ac.id/index.php/jtiik/article/view/900/pdf</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2019 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
</oai_dc:dc>