Application of fuzzy C-Means Algorithm for Determining Field of Interest in Information System Study STTH Medan

Syahputra, Edy Rahman; Dalimunthe, Yulia Agustina; Irvan,

doi:10.1088/1742-6596/930/1/012014

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The Fuzzy C-Means method is one of the simplest and is frequently used in data grouping approaches because it produces accurate estimates with few parameters. The Fuzzy C-Means method may be used to categorize data depending on specific qualities, according to various studies [12].…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

Implementation of Fuzzy C-Means and K-Medoids in Grouping People's Welfare Indicators (Case Study in Riau Province in 2020)

Farafisha¹,

Fauzan²,

Novianti³

2021

Proceedings of the 1st International Seminar on Teacher Training and Education, ISTED 2021, 17-18 July 2021, Purwokerto, Indone

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Riau Province is one of the provinces that has social inequality in its society. This is due to the uneven distribution of development carried out in remote areas that are difficult for the government to access. Therefore, the researchers grouped districts in Riau Province with a low welfare index by clustering using Fuzzy C-Means and K-Medoids methods. Clustering is a method of grouping objects or events into groups based on their similarity values. Later, a comparison will be made between the Fuzzy C-Means and K-Medoids methods to get the best result on the Riau Province community welfare data in 2020. From the analysis that has been carried out, there is a grouping of data into 3 clusters for Fuzzy C-Means method and 2 clusters for K-Medoids. Based on the result of the variances comparison of the two methods, namely Fuzzy C-Means and K-Medoids, each of which has a variance of 0.375681 and 3.248903, it can be concluded that the best cluster method for this case is Fuzzy C-Means. Based on the results of clustering on districts/cities by Fuzzy C-Means method can be applied as a reference by related parties to improve the welfare of the people of the Riau province.

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Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

Implementation of Fuzzy C-Means and K-Medoids in Grouping People's Welfare Indicators (Case Study in Riau Province in 2020)

Farafisha¹,

Fauzan²,

Novianti³

2021

Proceedings of the 1st International Seminar on Teacher Training and Education, ISTED 2021, 17-18 July 2021, Purwokerto, Indone

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Certainty Factor for Early Detection of Children’s Respiratory Disease

Hasdiana

Syahputra

Dewi

et al. 2019

J. Phys.: Conf. Ser.

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The immune system of children are weaker than adults. They are susceptible to diseases. One of the diseases that often affects them is respiratory disease. Expert systems can be used to determine early diagnosis of it. Certainty Factor allows the user to measure the certainty’s degree of the diagnosis. It is also affecting the level of trust. It is easy for users to make decisions in determining the healing step. Symptoms of the disease are analysed and managed into the knowledge base of the expert system. The result of disease diagnosis is appeared in the form of disease data. The result data are accompanied by certainty value or percentage of confidence that obtained by certainty factor method applied.

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Application of fuzzy C-Means Algorithm for Determining Field of Interest in Information System Study STTH Medan

Cited by 2 publications

References 3 publications

Implementation of Fuzzy C-Means and K-Medoids in Grouping People's Welfare Indicators (Case Study in Riau Province in 2020)

Implementation of Fuzzy C-Means and K-Medoids in Grouping People's Welfare Indicators (Case Study in Riau Province in 2020)

Certainty Factor for Early Detection of Children’s Respiratory Disease

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