Uncertainty Ontology for Module Rules Formation Waterwheel Control

Zulfian - Azmi(1), Mahyuddin K. M. Nasution(2), Herman Mawengkang(3), M Zarlis(4),


(1) STMIK Triguna Dharma
(2) Universitas Sumatera Utara, Padang Bulan USU Medan, Indonesia
(3) Universitas Sumatera Utara, Padang Bulan USU Medan, Indonesia
(4) Universitas Sumatera Utara, Padang Bulan USU Medan, Indonesia

Abstract

Implementation of Uncertainty model has not given maximum result in forming rule on an inference of a case. For testing to determine whether water quality is high, medium and low. The input variables used are temperature, pH, salinity and Disolved Oxygen. Testing is done by looking at the water turbidity change in the shrimp pond, to determine the water quality. Its water quality determines in the control module of the waterwheel rotation.Rolling the waterwheel moves quickly if pond water quality is low, moving slowly if water quality is medium and immobile if water quality is good. And the establishment of the rule with the approach of knowledge of Ontology to determine the relation between several variables (temperature, Ph, Disolved Oxygen and salinity). Each variable is set to its certainty value in the form of fuzzy value. Next is determined the relation of the four variables for the formation of rule.

Keywords

Neuron, Ontology, Uncertainty, Waterwheel.

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References

Zhang, D., & Pal, S. K. (2000). A Fuzzy Clustering Neural Networks (Fcns) System Design Methodology. IEEE Transactions on Neural Networks, 11(5), 1174-1177.

Asadi, M. (2016). Optimized Mamdani fuzzy models for predicting the strength of intact rocks and anisotropic rock masses. Journal of Rock Mechanics and Geotechnical Engineering, 8(2), 218-224.

Amiri, M., Ardeshir, A., & Zarandi, M. H. F. (2017). Fuzzy Probabilistic Expert System for Occupational Hazard Assessment in Construction. Safety Science, 93, 16-28.

Huang, S. H. (1994). Advanced Fuzzy Logic Controllers and Self-Tuning Strategy. Owa State University, Digital Repository @ Iowa State University.

Nasution, M. K. (2012). Kolmogorov Complexity: Clustering objects and similarity, Buletin of Mathematic 3(1):1-16.

Harianti, H., & Nurasia, N. (2016). Analisis Warna, Suhu, PH dan Salinitas Air Sumur Bor Di Kota Palopo. Prosiding Seminar Nasional, 2(1): 747-753.

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Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
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