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An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income

IAES International Journal of Artificial Intelligence · Vol. 12 No. 2 (2023) · hlm. 543–551

DOI
10.11591/ijai.v12.i2.pp543-551
ISSN
2252-8938, 2089-4872
Terbit
2023-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Regression analysis is a popular tool used in data analysis, whereas fuzzy regression is usually used for analyzing uncertain and imprecise data. In the industrial area, the company usually has problems in predicting the future manufacturing income. Therefore, a new approach model is needed to solve the future company prediction income. This article analyzed the manufacturing income by using the multiple linear regression (MLR) model and fuzzy linear regression (FLR) model proposed by Tanaka and Zolfaghari, involving 9 explanatory variables. In order to find the optimum of the FLR model, the degree of fitting (H) was adjusted between 0 to 1. The performance of three methods has been measured by using mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The analysis proved that FLR with Zolfaghari’s model with the degree of fitting of 0.025 outperformed the MLR and FLR with Tanaka’s model with the smallest error value. In conclusion, the manufacturing income is directly proportional to 6 independent variables. Furthermore, the manufacturing income is inversely proportional to 3 independent variables. This model is suitable in predicting future manufacturing income.

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  • Tun Hussein Onn University of Malaysia
  • Universitas Sembilanbelas November Kolaka
  • Hasanuddin University
  • Universitas Ahmad Dahlan
  • Universitas Muhammadiyah Semarang
  • Universitas Jambi
  • Universitas Islam Negeri Alauddin Makassar
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Rekan penulisOrganisasiArtikel bersama
Shuhaida Binti IsmailTun Hussein Onn University of Malaysia4
Gan Pei YeeTun Hussein Onn University of Malaysia4
Nursalam NursalamUniversitas Sembilanbelas November Kolaka2
Kartomo KartomoUniversitas Sembilanbelas November Kolaka2
Muhammad Obi KasminUniversitas Sembilanbelas November Kolaka2
Fitriani Nur DamayantiUniversitas Muhammadiyah Semarang2
Harapin HafidUniversitas Halu Oleo1
La Ode BaaAfiliasi tidak diketahui1
Sakaria SakariaHasanuddin University1
Muhammad BasrunHasanuddin University1
Surianti SuriantiHasanuddin University1
Nirwana IndahHasanuddin University1
Muhtar AminUniversitas Sembilanbelas November Kolaka1
Mhd. Usni Zamzami HasibuanUniversitas Jambi1
Roli MardianUniversitas Jambi1
Bella AulinaAfiliasi tidak diketahui1
Bambang IsmantoAfiliasi tidak diketahui1
Robbi TanggaraAfiliasi tidak diketahui1
Sakaruddin MandjarrekiUniversitas Islam Negeri Alauddin Makassar1
Muhammad Alim IhsanUniversitas Islam Negeri Alauddin Makassar1
Ismanto IsmantoSunan Gunung Djati State Islamic University Bandung1
Rizki Firmansyah Setya BudiNational Nuclear Energy Agency of Indonesia1
Anggit PrabowoUniversitas Ahmad Dahlan1
Chong Su LiUniversiti Teknologi Petronas1
Dahlia JananSultan Idris Education University1
TRI DESSY DAMAYANTIUniversitas Ahmad Dahlan1
R. AzizahUniversitas Airlangga1
Soedjajadi KemanUniversitas Airlangga1
Budi SantosaUniversitas Muhammadiyah Semarang1
Erna KusumawatiState University of Semarang1
Siti IstianaState University of Semarang1
Budi SantosaUniversitas Muhammadiyah Semarang1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income</dc:title>
	<dc:creator>Ramly, Nurfarawahida</dc:creator>
	<dc:creator>Saifullah Rusiman, Mohd</dc:creator>
	<dc:creator>Ammar Shafi, Muhammad</dc:creator>
	<dc:creator>.S, Suparman</dc:creator>
	<dc:creator>Mohamad Hamzah, Firdaus</dc:creator>
	<dc:creator>Gurunlu Alma, Ozlem</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Degree of fitting; Fuzzy linear regression; Multiple linear regression; Mean square error; Manufacturing income</dc:subject>
	<dc:description xml:lang="en-US">Regression analysis is a popular tool used in data analysis, whereas fuzzy regression is usually used for analyzing uncertain and imprecise data. In the industrial area, the company usually has problems in predicting the future manufacturing income. Therefore, a new approach model is needed to solve the future company prediction income. This article analyzed the manufacturing income by using the multiple linear regression (MLR) model and fuzzy linear regression (FLR) model proposed by Tanaka and Zolfaghari, involving 9 explanatory variables. In order to find the optimum of the FLR model, the degree of fitting (H) was adjusted between 0 to 1. The performance of three methods has been measured by using mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The analysis proved that FLR with Zolfaghari’s model with the degree of fitting of 0.025 outperformed the MLR and FLR with Tanaka’s model with the smallest error value. In conclusion, the manufacturing income is directly proportional to 6 independent variables. Furthermore, the manufacturing income is inversely proportional to 3 independent variables. This model is suitable in predicting future manufacturing income.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-06-01</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://ijai.iaescore.com/index.php/IJAI/article/view/22353</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i2.pp543-551</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 2: June 2023; 543-551</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22353/13665</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>