Perbandingan Clustering Karyawan Berdasarkan Nilai Kinerja Dengan Algoritma K-Means Dan Fuzzy C-Means

Pramitasari, Anissa Enggar; Nataliani, Yessica

doi:10.35957/jatisi.v8i3.957

Cited by 3 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Merupakan suatu proses yang dilewati untuk membuat suatu keputusan menggunakan teknik statistika yang diekstraksi dalam daftar data yang sebelumnya tidak diketahui namun sudah dipahami terlebih dahulu [4]. Data Mining bisa juga disebut dengan proses menemukan pola yang tersembunyi di dalam data.…”

Section: Studi Literatur 211 Data Miningunclassified

K-Means Algorithm and Binary Search on FiBuSI

Khuzaifi

Sari

2021

Telematika

View full text Add to dashboard Cite

Purpose: Create an application called FiBuSI (Find Business and Stock Investment) using the k-means algorithm and binary search for data search features. This application is intended for entrepreneurs and investors where they can interact with each other to build a joint business.Method: Using the RAD (Rapid Application Development) Method which focuses on system testing based on user experience related to Blackbox Testing using the Katalon Studio tools for testing functions on the FiBuSI application.Result: Based on the results of testing the FiBuSI application which focuses on the success of application functions and algorithm implementation, that each application function is successfully executed (PASSED) based on testing using the Katalon Studio tools. Meanwhile, testing the k-means algorithm (data filter) and binary search (search for letter data) was also successfully carried out by testing it directly by the user on the FiBuSI application and also using the results from the Katalon Studio tools.State of the art: Based on several studies that have been done previously related to the use of the k-means algorithm and binary search that this algorithm is carried out on 2 different features but in 1 application for business data search. In concept, the FiBuSI application focuses on bringing together entrepreneurs and investors in one platform.

show abstract

Section: Studi Literatur 211 Data Miningunclassified

K-Means Algorithm and Binary Search on FiBuSI

Khuzaifi

Sari

2021

Telematika

View full text Add to dashboard Cite

show abstract

“…Algoritma FCM merupakan algoritma pengelompokan data yang ditentukan oleh derajat keanggotaan. Untuk beberapa objek data yang menunjukkan keanggotaan parsial dan data yang tidak mempunyai batasan pada salah satu kelas saja dapat dikelompokkan berdasarkan derajat keanggotaan yaitu antara 0 sampai dengan 1 [18]. Meminimalkan fungsi objektif atau fungsi fitnes dapat dilakukan untuk mendapatkan struktur klaster dan hasil klaster yang optimal.…”

Section: Fuzzy C-meansunclassified

Penerapan K-Means dan Fuzzy C-Means untuk Pengelompokan Data Kasus Covid-19 di Kabupaten Indragiri Hilir

Octavia

Mustakim

2021

bits

View full text Add to dashboard Cite

In the beginning of 2020 world was shocked because new virus spreaded, that is Coronavirus Disease 2019 (Covid-19). This virus spread quickly in almost country, including Indonesia. Covid-19 virus deployment started in various regions in Indonesia stay increasing everyday. this research has been done the region clustering that infected Covid-19 case in Indragiri Hilir district to inform to central government about Covid-19 handling. To do Clustering in this research used K-Means and Fuzzy C-Means Algorithm. After done some of test, it's obtained the ratio which was tested with Silhouette Index and Partition Coefficient, SI validity value of K-Means is 0,950 while PCI validity value of Fuzzy C-Means is 0,960. The results have been obtained shown that Fuzzy C-Means Method is the best Method to do Clustering Covid-19 data in Indragiri Hilir district Because the validity value is closed to 1 which is located in K=3.

show abstract

“…Anissa and Yessica grouped data on employee performance values. The results were that the FCM algorithm had a better accuracy rate with a result of 76% compared to the K-Means algorithm, which had an accuracy value of 44% [7]. Aina and Nurkaromah performed a comparative analysis of the K-Means and Fuzzy C-Means algorithms with 2 cluster validity test parameters, namely Dunn-Index and Davies Bouldin-Index, to obtain optimal cluster results.…”

Section: Introductionmentioning

confidence: 99%

Algorithm Analysis of K-Means and Fuzzy C-Means for Clustering Countries Based on Economy and Health

Wulandari,

Yogantara

2022

FaktorExacta

View full text Add to dashboard Cite

Clustering adalah teknik pembelajaran mesin tanpa pengawasan yang membagi populasi menjadi beberapa kelompok atau klaster sedemikian rupa sehingga data dalam kelompok yang sama mirip satu sama lain, dan data dalam kelompok yang berbeda tidak serupa. Algoritma clustering yang ada diantaranya algoritma K-Means dan Fuzzy C-Means. Pada makalah ini proses clustering dilakukan untuk mengelompokkan negara-negara di dunia menjadi dua kategori utama yaitu negara maju dan negara berkembang berdasarkan tingkat kesejahteraan masyarakatnya. Makalah ini membahas tentang perbandingan algoritma K-Means dan Fuzzy C-Means. Algoritma K-Means menghasilkan 32 negara maju dan 135 negara berkembang. Algoritma Fuzzy C-Means menghasilkan 33 negara maju dan 134 negara berkembang. Hasil analisis pengujian performa menggunakan parameter Davies Bouldin Index pada algoritma K-Means memiliki nilai paling kecil artinya lebih baik yaitu sebesar 0.6606398 DB. Sedangkan hasil pengujian parameter Silhouette Coefficient pada Fuzzy C-Means semakin besar nilainya semakin baik dan didapatkan nilainya sebesar 0.896 S. Pengujian yang cukup signifikan terlihat pada penilitian ini adalah hasil pengukuran parameter Execution Time pada algoritma K-Means sebesar 0.00199 detik dan jauh lebih cepat.

show abstract

Perbandingan Clustering Karyawan Berdasarkan Nilai Kinerja Dengan Algoritma K-Means Dan Fuzzy C-Means

Cited by 3 publications

References 6 publications

K-Means Algorithm and Binary Search on FiBuSI

K-Means Algorithm and Binary Search on FiBuSI

Penerapan K-Means dan Fuzzy C-Means untuk Pengelompokan Data Kasus Covid-19 di Kabupaten Indragiri Hilir

Algorithm Analysis of K-Means and Fuzzy C-Means for Clustering Countries Based on Economy and Health

Contact Info

Product

Resources

About