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Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model Dengan Algoritma Evolusi Differensial

Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model dengan Algoritma Evolusi Differensial

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 11 No. 4 (2024) · pp. 837–844

DOI
10.25126/jtiik.1148867
ISSN
2528-6579, 2355-7699
Published
2024-08-26
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
id
License
creativecommons.org/licenses/by-sa/4.0

Abstract

English

Perdagangan saham berdasarkan Indeks Harga Saham Gabungan (IHSG) di Indonesia adalah area dinamis dan kompleks. Prediksi pergerakan harga IHSG memiliki volatilitas pasar saham yang tinggi. Penggunaan Hybrid Weighted Fuzzy Time series Hidden Markov Model (WFTS-HMM) dengan Algoritma Evolusi Diferensial (DE) menjanjikan solusi dengan pendekatan terbaru. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga IHSG melalui optimasi model hybrid.. Penelitian menggunakan data IHSG tiap bulan dari Januari hingga Desember 2023 dari situs www.yahoo.finance.com. Prediksi yang dihasilkan dari Model Hybrid WFTS-HMM dioptimasi dengan Algoritma ED memiliki tingkat kesalahan prediksi yang lebih rendah (1.45%) dibandingkan dengan model tanpa DE (1.49%). Abstract Stock trading based on IHSG in Indonesia is a dynamic and complex area. Predicting IHSG price movements entails high stock market volatility. Utilizing the Hybrid WFTS-HMM Model with the DE Algorithm promises a cutting-edge approach. This research aims to enhance the prediction of IHSG price through hybrid model optimization and performance evaluation. The study employs IHSG monthly data from January to December 2023 from www.yahoo.finance.com. Forecasting from the Hybrid WFTS-HMM Model with the DE Algorithm has lower prediction error (1.45%) compared to the model without DE (1.49%).

Bahasa Indonesia

Perdagangan saham berdasarkan Indeks Harga Saham Gabungan (IHSG) di Indonesia adalah area dinamis dan kompleks. Prediksi pergerakan harga IHSG memiliki volatilitas pasar saham yang tinggi. Penggunaan Hybrid Weighted Fuzzy Time series Hidden Markov Model (WFTS-HMM) dengan Algoritma Evolusi Diferensial (DE) menjanjikan solusi dengan pendekatan terbaru. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga IHSG melalui optimasi model hybrid.. Penelitian menggunakan data IHSG tiap bulan dari Januari hingga Desember 2023 dari situs www.yahoo.finance.com. Prediksi yang dihasilkan dari Model Hybrid WFTS-HMM dioptimasi dengan Algoritma ED memiliki tingkat kesalahan prediksi yang lebih rendah (1.45%) dibandingkan dengan model tanpa DE (1.49%). Abstract Stock trading based on IHSG in Indonesia is a dynamic and complex area. Predicting IHSG price movements entails high stock market volatility. Utilizing the Hybrid WFTS-HMM Model with the DE Algorithm promises a cutting-edge approach. This research aims to enhance the prediction of IHSG price through hybrid model optimization and performance evaluation. The study employs IHSG monthly data from January to December 2023 from www.yahoo.finance.com. Forecasting from the Hybrid WFTS-HMM Model with the DE Algorithm has lower prediction error (1.45%) compared to the model without DE (1.49%).

Keywords

Collaboration network of the authors

40 authors · 136 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
  • Universitas Widya Kartika
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nur Silviyah RahmiAffiliation unknown3
Ani Budi AstutiUniversity of Brawijaya2
Diego IrsandyAffiliation unknown2
Henny PramoedyoUniversity of Brawijaya2
Adji Achmad Rinaldo FernandesUniversity of Brawijaya2
Sri HandayaniAffiliation unknown1
Alifiandi Rafi MuhammadAffiliation unknown1
Ulfah MaisarohAffiliation unknown1
Fang You Dwi Ayu Shalu SaniyawatiAffiliation unknown1
Fidia Raaihatul MashfiaAffiliation unknown1
Evelin Dewi LusianaAffiliation unknown1
Intan Fadhila RisdaAffiliation unknown1
Mohammad Hilmi SusantoAffiliation unknown1
Dwi Ayu LusiaUniversity of Brawijaya1
Heni KusdarwatiUniversity of Brawijaya1
Imelda SalsabilaAffiliation unknown1
Achmad EfendiAffiliation unknown1
Fachri FaisalAffiliation unknown1
Atiek IrianyUniversity of Brawijaya1
Petrus Kanisius OlaAffiliation unknown1
Rahma FitrianiAffiliation unknown1
Armando Jacquis Federal ZamelinaAffiliation unknown1
Lucius RamifidisoaAffiliation unknown1
Solimun SolimunUniversity of Brawijaya1
Usriatur RohmaAffiliation unknown1
Darmanto DarmantoUniversitas Widya Kartika1
Nurjannah NurjannahInstitut Agama Islam Negeri Kendari1
Isnani DartiAffiliation unknown1
Muhammad Hisyam LeeAffiliation unknown1
Rismania Hartanti Putri Yulianing DamayantiAffiliation unknown1
Husnul KhatimahAffiliation unknown1
Ni Wayan Surya WardhaniAffiliation unknown1
Adinda Gita MaharaniAffiliation unknown1
Atmadani Rahayu FakhrunnisaAffiliation unknown1
Silvia Intan AuliaAffiliation unknown1
Agis Wahyu LestariAffiliation 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 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model Dengan Algoritma Evolusi Differensial</dc:title>
	<dc:title xml:lang="en">Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model dengan Algoritma Evolusi Differensial</dc:title>
	<dc:creator xml:lang="en">Syalsabilla, Alya Fitri</dc:creator>
	<dc:creator xml:lang="id">Syalsabilla, Alya Fitri</dc:creator>
	<dc:creator xml:lang="id">Astutik, Suci</dc:creator>
	<dc:creator xml:lang="id">Rozy, Agus Fachrur </dc:creator>
	<dc:subject xml:lang="en">Hidden Markov</dc:subject>
	<dc:subject xml:lang="en">Weighted Fuzzy Time Series Evolusi Differensial</dc:subject>
	<dc:subject xml:lang="en">MAPE</dc:subject>
	<dc:subject xml:lang="id">Hidden Markov</dc:subject>
	<dc:subject xml:lang="id">Weighted Fuzzy Time Series Evolusi Differensial</dc:subject>
	<dc:subject xml:lang="id">MAPE</dc:subject>
	<dc:description xml:lang="en">Perdagangan saham berdasarkan Indeks Harga Saham Gabungan (IHSG) di Indonesia adalah area dinamis dan kompleks. Prediksi pergerakan harga IHSG memiliki volatilitas pasar saham yang tinggi. Penggunaan Hybrid Weighted Fuzzy Time series Hidden Markov Model (WFTS-HMM) dengan Algoritma Evolusi Diferensial (DE) menjanjikan solusi dengan pendekatan terbaru. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga IHSG melalui optimasi model hybrid.. Penelitian menggunakan data IHSG tiap bulan dari Januari hingga Desember 2023 dari situs www.yahoo.finance.com. Prediksi yang dihasilkan dari Model Hybrid WFTS-HMM dioptimasi dengan Algoritma ED memiliki tingkat kesalahan prediksi yang lebih rendah (1.45%) dibandingkan dengan model tanpa DE (1.49%).
 
Abstract 
 
Stock trading based on IHSG in Indonesia is a dynamic and complex area. Predicting IHSG price movements entails high stock market volatility. Utilizing the Hybrid WFTS-HMM Model with the DE Algorithm promises a cutting-edge approach. This research aims to enhance the prediction of IHSG price through hybrid model optimization and performance evaluation. The study employs IHSG monthly data from January to December 2023 from www.yahoo.finance.com. Forecasting from the Hybrid WFTS-HMM Model with the DE Algorithm has lower prediction error (1.45%) compared to the model without DE (1.49%). </dc:description>
	<dc:description xml:lang="id">Perdagangan saham berdasarkan Indeks Harga Saham Gabungan (IHSG) di Indonesia adalah area dinamis dan kompleks. Prediksi pergerakan harga IHSG memiliki volatilitas pasar saham yang tinggi. Penggunaan Hybrid Weighted Fuzzy Time series Hidden Markov Model (WFTS-HMM) dengan Algoritma Evolusi Diferensial (DE) menjanjikan solusi dengan pendekatan terbaru. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga IHSG melalui optimasi model hybrid.. Penelitian menggunakan data IHSG tiap bulan dari Januari hingga Desember 2023 dari situs www.yahoo.finance.com. Prediksi yang dihasilkan dari Model Hybrid WFTS-HMM dioptimasi dengan Algoritma ED memiliki tingkat kesalahan prediksi yang lebih rendah (1.45%) dibandingkan dengan model tanpa DE (1.49%).
 
Abstract 
 
Stock trading based on IHSG in Indonesia is a dynamic and complex area. Predicting IHSG price movements entails high stock market volatility. Utilizing the Hybrid WFTS-HMM Model with the DE Algorithm promises a cutting-edge approach. This research aims to enhance the prediction of IHSG price through hybrid model optimization and performance evaluation. The study employs IHSG monthly data from January to December 2023 from www.yahoo.finance.com. Forecasting from the Hybrid WFTS-HMM Model with the DE Algorithm has lower prediction error (1.45%) compared to the model without DE (1.49%). </dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2024-08-26</dc:date>
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	<dc:identifier>10.25126/jtiik.1148867</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 11 No 4: Agustus 2024; 837-844</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 11 No. 4: Agustus 2024; 837-844</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.114</dc:source>
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	<dc:relation>https://jtiik.ub.ac.id/index.php/jtiik/article/view/8867/1343</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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