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Analysis and Performance Comparison of Fuzzy Inference Systems in Handling Uncertainty: A Review

Journal of Robotics and Control (JRC) · Vol. 5 No. 4 (2024) · hlm. 1203–1215

DOI
10.18196/jrc.v5i4.22123
ISSN
2715-5072, 2715-5056
Terbit
2024-06-25
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng

Abstrak

Uncertainty is an inevitable characteristic in human life and systems, posing challenges in decision-making and data analysis. Fuzzy theory emerges to address this uncertainty by describing variables with vague or uncertain values, one of which is the Fuzzy Inference System (FIS). This research analyzes and compares the performance of FIS from previous studies as a solution to manage uncertainty. FIS allows for flexible and responsive representations of truth levels using human-like linguistic rules. Common FIS methods include FIS-M, FIS-T, and FIS-S, each with different inference and defuzzification approaches. The findings of this research review, referencing previous studies, indicate that the application of FIS in various contexts such as prediction, medical diagnosis, and financial decision-making, yields very high accuracy levels up to 99%. However, accuracy comparisons show variations, with FIS-M tending to achieve more stable accuracy based on the referenced studies. The accuracy difference among FIS-M studies is not significantly different, only around 7.55%. Meanwhile, FIS-S has a wider accuracy range, from 81.48% to 99% (17.52%). FIS-S performs best if it can determine influencing factors well, such as determining constant values in its fuzzy rules. Additionally, the performance comparison of FIS can also be influenced by other factors such as data complexity, variables, domain, membership functions (curves), fuzzy rules, and defuzzification methods used in the study. Therefore, it is important to consider these factors and select the most suitable FIS method to manage uncertainty in the given situation.

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Hari MaghfirohAfiliasi tidak diketahui5
Davut IzciAfiliasi tidak diketahui4
Serdar EkinciAfiliasi tidak diketahui4
Mahmoud A. MossaAfiliasi tidak diketahui4
Hamid ChojaaAfiliasi tidak diketahui3
Feri AdriyantoAfiliasi tidak diketahui3
Miftahul AnwarAfiliasi tidak diketahui2
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Mahmoud A. MossaAfiliasi tidak diketahui2
N. S. AbuAfiliasi tidak diketahui2
W. M. BukhariAfiliasi tidak diketahui2
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Reza AlayiAfiliasi tidak diketahui2
Yaser EbazadehAfiliasi tidak diketahui2
Dede YusufAfiliasi tidak diketahui1
Tri Stiyo FamujiUniversitas Ahmad Dahlan1
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WillyansahAfiliasi tidak diketahui1
Jamiludin JamiludinUniversitas Halu Oleo1
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La IbalAfiliasi tidak diketahui1
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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Analysis and Performance Comparison of Fuzzy Inference Systems in Handling Uncertainty: A Review</dc:title>
	<dc:creator>Furizal, Furizal</dc:creator>
	<dc:creator>Ma'arif, Alfian</dc:creator>
	<dc:creator>Wijaya, Setiawan Ardi</dc:creator>
	<dc:creator>Murni, Murni</dc:creator>
	<dc:creator>Suwarno, Iswanto</dc:creator>
	<dc:subject xml:lang="en-US">Fuzzy Inference System</dc:subject>
	<dc:subject xml:lang="en-US">Tsukamoto</dc:subject>
	<dc:subject xml:lang="en-US">Sugeno</dc:subject>
	<dc:subject xml:lang="en-US">Mamdani</dc:subject>
	<dc:subject xml:lang="en-US">Uncertainty.</dc:subject>
	<dc:description xml:lang="en-US">Uncertainty is an inevitable characteristic in human life and systems, posing challenges in decision-making and data analysis. Fuzzy theory emerges to address this uncertainty by describing variables with vague or uncertain values, one of which is the Fuzzy Inference System (FIS). This research analyzes and compares the performance of FIS from previous studies as a solution to manage uncertainty. FIS allows for flexible and responsive representations of truth levels using human-like linguistic rules. Common FIS methods include FIS-M, FIS-T, and FIS-S, each with different inference and defuzzification approaches. The findings of this research review, referencing previous studies, indicate that the application of FIS in various contexts such as prediction, medical diagnosis, and financial decision-making, yields very high accuracy levels up to 99%. However, accuracy comparisons show variations, with FIS-M tending to achieve more stable accuracy based on the referenced studies. The accuracy difference among FIS-M studies is not significantly different, only around 7.55%. Meanwhile, FIS-S has a wider accuracy range, from 81.48% to 99% (17.52%). FIS-S performs best if it can determine influencing factors well, such as determining constant values in its fuzzy rules. Additionally, the performance comparison of FIS can also be influenced by other factors such as data complexity, variables, domain, membership functions (curves), fuzzy rules, and defuzzification methods used in the study. Therefore, it is important to consider these factors and select the most suitable FIS method to manage uncertainty in the given situation.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2024-06-25</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://journal.umy.ac.id/index.php/jrc/article/view/22123</dc:identifier>
	<dc:identifier>10.18196/jrc.v5i4.22123</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 5 No. 4 (2024); 1203-1215</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:source>10.18196/jrc.v5i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/22123/9309</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Furizal Furizal, Alfian Ma'arif, Setiawan Ardi Wijaya, Murni Murni, Iswanto Suwarno</dc:rights>
</oai_dc:dc>