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Forecasting World Crude Oil Prices using the Fuzzy Time Series Method with a Comparison of the Chen and Lee Model

Journal of Advances in Information Systems and Technology · Vol. 2 No. 1 (2020) · hlm. 1–12

DOI
10.15294/jaist.v2i1.44362
ISSN
2715-999X, 2714-9714
Terbit
2020-04-30
Penerbit
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

In this study, the Fuzzy Time Series (FTS) method compared to Chen and Lee models is used to predict world crude oil prices. The goal is to determine which model results are best between Chen and Lee models in the fuzzy time series method in predicting world crude oil prices. In the calculation of FTS number and width specified intervals beginning of the process, the process is very influential to the outcome prediction. The method for determining the number and width of the interval that effectively is by using Rules Sturgess. So that the formation of fuzzy logical relationships will be appropriate and effective yield predictive results. Of the 50 trials that have been done using daily data from the Organization of the Petroleum Exporting Countries (OPEC), it is known that the FTSLee model can predict better than the Chen model with a comparison of the results of the AFER fuzzy time series Lee model by 97.4% and RMSE of 1.617 and the results of the AFER fuzzy time series Chen model by 97.2% and RMSE of 1.693.

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  • State University of Semarang
  • Institut Teknologi Kalimantan
  • Tun Hussein Onn University of Malaysia
  • Universitas PGRI Palembang
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  • Organisasi berbeda
  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Endang SugihartiState University of Semarang5
Florentina Yuni AriniState University of Semarang3
Rochmad RochmadAfiliasi tidak diketahui3
Amin SuyitnoAfiliasi tidak diketahui3
Alamsyah AlamsyahInstitut Teknologi Kalimantan2
Anggyi Trisnawan PutraAfiliasi tidak diketahui2
Novanka Agnes SekartajiAfiliasi tidak diketahui2
Irfan Fajar MuttaqinAfiliasi tidak diketahui2
Ramadhan Ridho ArrohmanAfiliasi tidak diketahui2
Dwijanto DwijantoAfiliasi tidak diketahui2
Putriaji HendikawatiAfiliasi tidak diketahui2
Mashuri MashuriAfiliasi tidak diketahui2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Arif WidiyatmokoState University of Semarang1
Stephani Diah PamelasariState University of Semarang1
Arka YanitamaState University of Semarang1
Melissa Salma DarmawanState University of Semarang1
Aji SaputraUniversitas PGRI Palembang1
Desy Fitria AstutianingtyasAfiliasi tidak diketahui1
Syafa Nabilah SyahputraAfiliasi tidak diketahui1
Athoillah AthoillahAfiliasi tidak diketahui1
Kholiq BudimanAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Anggoro Sakti AnindhitoAfiliasi tidak diketahui1
Fajar SujitoAfiliasi tidak diketahui1
Anita Wahyu MelindaAfiliasi tidak diketahui1
Isa AkhlisState University of Semarang1
Achmad Wahyu HidayatAfiliasi tidak diketahui1
Safit FirmansyahAfiliasi tidak diketahui1
Anggil AgustaAfiliasi tidak diketahui1
Fachrizal Ahdy AndoyoAfiliasi tidak diketahui1
Margareta Ayu RiantikAfiliasi tidak diketahui1
Dandi Indra WijayaAfiliasi tidak diketahui1
Hanif Nur CahyaniAfiliasi tidak diketahui1
Andhika Seno TamtamaAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/jaist/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Forecasting World Crude Oil Prices using the Fuzzy Time Series Method with a Comparison of the Chen and Lee Model</dc:title>
	<dc:creator>Syahputra, Dian Rahman</dc:creator>
	<dc:creator>Arifudin, Riza</dc:creator>
	<dc:description xml:lang="en-US">In this study, the Fuzzy Time Series (FTS) method compared to Chen and Lee models is used to predict world crude oil prices. The goal is to determine which model results are best between Chen and Lee models in the fuzzy time series method in predicting world crude oil prices. In the calculation of FTS number and width specified intervals beginning of the process, the process is very influential to the outcome prediction. The method for determining the number and width of the interval that effectively is by using Rules Sturgess. So that the formation of fuzzy logical relationships will be appropriate and effective yield predictive results. Of the 50 trials that have been done using daily data from the Organization of the Petroleum Exporting Countries (OPEC), it is known that the FTSLee model can predict better than the Chen model with a comparison of the results of the AFER fuzzy time series Lee model by 97.4% and RMSE of 1.617 and the results of the AFER fuzzy time series Chen model by 97.2% and RMSE of 1.693.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2020-04-30</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/44362</dc:identifier>
	<dc:identifier>10.15294/jaist.v2i1.44362</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 2 No 1 (2020): April; 1-12</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v2i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/44362/18052</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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