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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Fundamental Mathematics and Applications (JFMA)
Sulaiman, Fadhil Hanif
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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FLOWER POLLINATION ALGORITHM (FPA): COMPARING SWITCH PROBABILITY BETWEEN CONSTANT 0.8 AND DOUBLE EXPONENTGUNAKAN DOUBLE EXPONENT Afrianti, Yuli Sri; Sulaiman, Fadhil Hanif; Vantika, Sandy
Journal of Fundamental Mathematics and Applications (JFMA) Vol 6, No 2 (2023)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v6i2.18996

Abstract

Flower Pollination Algorithm (FPA) is an optimization method that adopts the way flower pollination works by selecting switch probabilities to determine the global or local optimization process. The choice of switch probability value will influence the number of iterations required to reach the optimum value. In several previous literatures, the switch probability value was always chosen as 0.8 because naturally the global probability is greater than local. In this article, comparison is studied to determine the switch probability by using the Double Exponent rule. The results are analyzed using Hypothesis Testing to test whether there is a significant difference between the optimization results. The study involved ten testing functions, and results showed that the 0.8 treatment is significantly different from the Double Exponent. However, in general no treatment is better than the other.
K-MEANS AND AGGLOMERATIVE HIERARCHY CLUSTERING ANALYSIS ON THE STAINLESS STEEL CORROSION PROBLEM Afrianti, Yuli Sri; Pasaribu, Udjianna Sekteria; Sulaiman, Fadhil Hanif; Angelia, Grace; Wattimanela, Henry Junus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0589-0602

Abstract

Stainless Steel (SS) is a material that is widely used in various fields because it is resistant to corrosion. However, if SS is exposed to heat at high temperatures for a long period of time, a sigma phase, namely the Fe-Cr compound, will form, which indicates that corrosion has begun. The appearance of this corrosion can be detected through color changes on the SS surface, ranging from light brown to dark blue. Corrosion events will be observed through the distribution of color on the sample surface at the location selected through the SS microstructure image. Cluster analysis will be used to group the colors on the surface of the SS sample through the images used. The results of cluster analysis can be used to identify SS color which indicates the appearance of corrosion in the sample. In this research, we will examine the determination of many clusters for K-Means and Agglomerative Hierarchy with Ward's Criterion, Single, Average, and Complete Linkages. In addition, the model quality measure was tested with Silhouette Coeficient. Single linkage gives the worst results because it gives the impression that only one dominant color appears so it can be said that it is unable to distribute each color to the specified cluster. Likewise with Average because the number of clusters cannot be determined with certainty. On the other hand, the K-Means results are similar to Ward's results, this is reasonable because the basic idea of both is to find the minimum distance between each object and its center, in this case the average is used as the measure of the center, while the results that are most similar to the original image are clustering uses complete linkage. These results can be used as recommendations for academics and practitioners in the fields of Statistics, Mathematics and Materials Engineering in the subsequent analysis process to solve SS corrosion problems.
Co-Authors Afrianti, Yuli Sri Angelia, Grace Pasaribu, Udjianna Sekteria Vantika, Sandy Wattimanela, Henry Junus Yuli Sri Afrianti