Implementasi Fuzzy C-Means Clustering Dalam Penentuan Beasiswa

Rahakbauw, Dorteus L.; Ilwaru, Venn Yan Ishak; Hahury, M. H.

doi:10.30598/barekengvol11iss1pp1-12

Cited by 24 publications

(27 citation statements)

References 1 publication

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“…Keunggulan utama fuzzy clustering adalah dapat memberikan hasil pengelompokan bagi objek-objek yang tersebar tidak teratur, karena jika terdapat suatu data yang penyebaranya tidak teratur maka terdapat kemungkinan suatu titik data mempunyai sifat atau karakteristik dari cluster lain [11]. Dalam teori fuzzy, keanggotaan sebuah data tidak diberikan nilai secara tegas dengan nilai 1 (menjadi anggota) dan 0 (tidak menjadi anggota), melainkan dengan suatu nilai suatu nilai derajat keanggotaan yang jangkauan nilainya 0 sampai 1 [12]. Semakin tinggi nilai keanggotaannya semakin objek tersebut masuk dalam cluster tersebut.…”

Section: Fuzzy K Means (Fcm)unclassified

Soft Clustering Dengan Algoritma Fuzzy K-Means (Studi Kasus : Pengelompokan Desa Di Kota Tidore Kepulauan)

Johra¹

2021

BAREKENG: J. Il. Mat. & Ter.

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Mengembangkan wilayah untuk mengurangi kesenjangan dan menjamin pemerataan merupakan salah satu dari tujuh agenda Pembangunana RPJMN IV Tahun 2020-2024. Setiap wilayah tentunya memiliki potensi yang berbeda, baik potensi fisik maupun non-fisik. Perbedaan inilah yang menjadi dasar dalam pengelompokan desa sehingga pembangunan desa menjadi lebih terarah. Secara umum metode klaster dapat dibedakan menjadi dua kelompok yaitu hard clustering dan soft clustering. Pada hard clustering setiap objek dipetakan terhadap setiap kelompok. Metode yang populer pada kelompok hard clustering adalah Cluster K-Means. Sedangkan pada soft clustering objek tidak hanya dipetakan kedalam satu kelompok. Fuzzy K Means (FCM) merupakan salah satu metode dalam soft clustering, dimana Fuzzy K Means merupakan pengembangan dari Cluster K-Means. Cara kerja FCM adalah objek diberi probabilitas yang pada dasarnya menggambarkan kepemilikan objek ke dalam Cluster.

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Section: Fuzzy K Means (Fcm)unclassified

Soft Clustering Dengan Algoritma Fuzzy K-Means (Studi Kasus : Pengelompokan Desa Di Kota Tidore Kepulauan)

Johra¹

2021

BAREKENG: J. Il. Mat. & Ter.

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“…The initial fuzzy clustering approach is to find the cluster's center, which will serve as the average position for every cluster. After a series of upgrades to the cluster center, the cluster center will be moved to its proper position [10]. Each data will be given a value or weight to enter each cluster.…”

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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“…Other research conducted by Rahakabhaw implemented Fuzzy C-Means in determining scholarships by grouping data of students who are eligible to receive scholarships and students who are not eligible for scholarships. Fuzzy C-Means can also be used as a method and as a determinant of eligibility for scholarship acceptance (Rahakbauw et al, 2017).…”

Section: Year 2014)mentioning

confidence: 99%

Clustering Customer Data Using Fuzzy C-Means Algorithm

Nurfaizah

Fathuzaen

2021

PIKSEL

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The pattern of the service industry is influenced mostly by economic growth. When economic growth rises, the economic activity will also grow as in the case of insurance activities. One of the assets owned by an insurance company is the customer, hence the existence of a loyal or potential customer should be maintained by the insurance company. This study focuses on clustering or grouping the existing customer data in insurance companies using the Fuzzy C-Means (FCM) algorithm. This study uses data from the company for analysis and the results can be used as a basis for insurance companies in making decisions, especially those related to further insurance marketing to customers who have participated in insurance or who are still actively registered in payment insurance. Fuzzy C-Means can be used for clustering the customer datasets. It obtained 3 clustering results using Partition Coefficient (PC) in determining the validity index and the centers value was ranged from 0.5 to 1.0.

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Implementasi Fuzzy C-Means Clustering Dalam Penentuan Beasiswa

Cited by 24 publications

References 1 publication

Soft Clustering Dengan Algoritma Fuzzy K-Means (Studi Kasus : Pengelompokan Desa Di Kota Tidore Kepulauan)

Soft Clustering Dengan Algoritma Fuzzy K-Means (Studi Kasus : Pengelompokan Desa Di Kota Tidore Kepulauan)

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

Clustering Customer Data Using Fuzzy C-Means Algorithm

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