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Implementasi Metode Backpropogation dengan Inisialisasi Bobot Nguyen Widrow untuk Peramalan Harga Saham

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 6 No. 1 (2019) · pp. 49–54

DOI
10.25126/jtiik.201961904
ISSN
2528-6579, 2355-7699
Published
2019-01-16
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
id

Abstract

Jaringan saraf tiruan merupakan suatu ilmu yang terus berkembang pesat hingga saat ini. Jaringan saraf tiruan merupakan suatu ilmu komputasi yang didasarkan dan terinspirasi dari cara kerja sistem saraf manusia. Sama halnya dengan sistem saraf manusia, jaringan saraf tiruan bekerja melalui proses pembelajaran terhadap data-data yang sudah ada untuk memformulakan keluaran dari data-data baru. Jaringan saraf tiruan dengan metode backpropagation mampu melakukan peramalan untuk data nonlinear seperti bentuk data harian harga saham. Salah satu algoritma inisialisasi bobot yang dapat meningkatkan waktu eksekusi adalah nguyen-widrow. Pada penelitian ini akan dilakukan implementasi metode backpropagation dengan inisialisasi bobot nguyen widrow untuk meramalkan harga saham. Proses implementasi melalui 3 tahapan, yaitu preprosesing data, pelatihan jaringan, dan pengujian jaringan. Hasil dari penelitian ini menunjukkan bahwa pelatihan jaringan saraf tiruan dengan jumlah dataset yang banyak membutuhkan perhitungan yang kompleks, sehingga jaringan saraf tiruan dengan arsitektur jaringan yang sederhana kurang efektif dan dapat terjebak pada titik lokal minimum. Hasil peramalan untuk harga close saham BBCA.JK memiliki nilai MAPE 0,85% dan untuk harga close saham AALI.JK memiliki nilai MAPE sebesar 1,84%.AbstractArtificial neural network is a hot topic and invite a lot of admiration in the last decade. Artificial Neural Network is one of the artificial representations of the humans brain who always try to simulate the learning process of the humans brain. Artificial neural network with backpropagation method is able to forecast nonlinear data such as daily data form stock price. One of the weight initialization algorithms that can be increase the execution time is nguyen-widrow. In this research will be implemented backpropagation method with nguyen widrow weight initialization to forecast stock prices. The process of implementation through 3 stages, that is preprosesing data, training, and testing or simulate. The results of this research indicate that the training of artificial neural networks with many datasets required a complex calculations, so the artificial neural network with simple architectures is less effective and can get stuck at minimum local points. The results forecasting for the close price of BBCA.JK have a MAPE value 0.85% and for the close price of AALI.JK have 1.84% of MAPE value

Keywords

Collaboration network of the authors

40 authors · 77 co-authorship links · 2 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.

  • State University of Semarang
  • Universitas Bhayangkara Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Haryono HaryonoUniversitas Bhayangkara Surabaya4
Wahyu HardyantoAffiliation unknown3
Muhammad KhumaediAffiliation unknown2
Anan NugrohoState University of Semarang1
Budi SunarkoState University of Semarang1
Anggraini MulwindaState University of Semarang1
Anas FauziState University of Semarang1
Dwi OktaviyantiState University of Semarang1
Dina Wulung SavitriState University of Semarang1
Dania Ayu WulandariState University of Semarang1
Agus SuryantoState University of Semarang1
Agus MurnomoState University of Semarang1
Amat BasirAffiliation unknown1
Nur Laely FatimahAffiliation unknown1
Dharu WihartasihAffiliation unknown1
Alfian BudiarsoAffiliation unknown1
Aji BudiantoAffiliation unknown1
Hilal Aji WibowoAffiliation unknown1
Muhammad HarlanuAffiliation unknown1
Fifi Novalita SariAffiliation unknown1
R KartonoAffiliation unknown1
Faridah Muthi’ahAffiliation unknown1
Mas'ul FauziAffiliation unknown1
Puji AstutiAffiliation unknown1
Widya -Affiliation unknown1
Rodia SyamwilAffiliation unknown1
Firyomanto FiryomantoAffiliation unknown1
Ika Umaya SintaAffiliation unknown1
A. Tri WidodoAffiliation unknown1
Surya Puspita SariAffiliation unknown1
Widya Puji AstutiAffiliation unknown1
Totok Sumaryanto FlorentinusState University of Semarang1
Luqman Azhar JuliantriAffiliation unknown1
Muhammad Elfin NoorAffiliation unknown1
Ninok Eyiz SumianingrumAffiliation unknown1
Urip Muhayat Wiji WahyudiAffiliation unknown1
Aris MunandarAffiliation 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://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Implementasi Metode Backpropogation dengan Inisialisasi Bobot Nguyen Widrow untuk Peramalan Harga Saham</dc:title>
	<dc:creator xml:lang="id">Kurniawan, Eliv</dc:creator>
	<dc:creator xml:lang="id">Wibawanto, Hari</dc:creator>
	<dc:creator xml:lang="id">Widodo, Djoko Adi</dc:creator>
	<dc:subject xml:lang="id">Peramalan</dc:subject>
	<dc:subject xml:lang="id">Jaringan Saraf Tiruan</dc:subject>
	<dc:subject xml:lang="id">Backpropagation</dc:subject>
	<dc:subject xml:lang="id">Nguyen Widrow</dc:subject>
	<dc:description xml:lang="id">Jaringan saraf tiruan merupakan suatu ilmu yang terus berkembang pesat hingga saat ini. Jaringan saraf tiruan merupakan suatu ilmu komputasi yang didasarkan dan terinspirasi dari cara kerja sistem saraf manusia. Sama halnya dengan sistem saraf manusia, jaringan saraf tiruan bekerja melalui proses pembelajaran terhadap data-data yang sudah ada untuk memformulakan keluaran dari data-data baru. Jaringan saraf tiruan dengan metode backpropagation mampu melakukan peramalan untuk data nonlinear seperti bentuk data harian harga saham. Salah satu algoritma inisialisasi bobot yang dapat meningkatkan waktu eksekusi adalah nguyen-widrow. Pada penelitian ini akan dilakukan implementasi metode backpropagation dengan inisialisasi bobot nguyen widrow untuk meramalkan harga saham. Proses implementasi melalui 3 tahapan, yaitu preprosesing data, pelatihan jaringan, dan pengujian jaringan. Hasil dari penelitian ini menunjukkan bahwa pelatihan jaringan saraf tiruan dengan jumlah dataset yang banyak membutuhkan perhitungan yang kompleks, sehingga jaringan saraf tiruan dengan arsitektur jaringan yang sederhana kurang efektif dan dapat terjebak pada titik lokal minimum. Hasil peramalan untuk harga close saham BBCA.JK memiliki nilai MAPE 0,85% dan untuk harga close saham AALI.JK memiliki nilai MAPE sebesar 1,84%.AbstractArtificial neural network is a hot topic and invite a lot of admiration in the last decade. Artificial Neural Network is one of the artificial representations of the humans brain who always try to simulate the learning process of the humans brain. Artificial neural network with backpropagation method is able to forecast nonlinear data such as daily data form stock price. One of the weight initialization algorithms that can be increase the execution time is nguyen-widrow. In this research will be implemented backpropagation method with nguyen widrow weight initialization to forecast stock prices. The process of implementation through 3 stages, that is preprosesing data, training, and testing or simulate. The results of this research indicate that the training of artificial neural networks with many datasets required a complex calculations, so the artificial neural network with simple architectures is less effective and can get stuck at minimum local points. The results forecasting for the close price of BBCA.JK have a MAPE value 0.85% and for the close price of AALI.JK have 1.84% of MAPE value</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2019-01-16</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="id">Dokumentasi, Analisis</dc:type>
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	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/904</dc:identifier>
	<dc:identifier>10.25126/jtiik.201961904</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 6 No 1: Februari 2019; 49-54</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 6 No. 1: Februari 2019; 49-54</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201961</dc:source>
	<dc:language>id</dc:language>
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	<dc:rights xml:lang="en">Copyright (c) 2019 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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