Perbaikan Kinerja Clustering K-Means pada Data Ekonomi Nelayan dengan Perhitungan Sum of Square Error (SSE) dan Optimasi nilai K cluster

Refialy, Leonardo Petra; Maitimu, Hervin; Pesulima, Melgusen Soyano

doi:10.33633/tc.v20i2.4572

Cited by 9 publications

(10 citation statements)

References 4 publications

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“…Metode Sum Squared Error (SSE) digunakan untuk mengukur selisih total dari nilai sebenarnya terhadap nilai yang tercapai. SSE merupakan hasil penjumlahan dari seluruh jarak masing-masing data dengan titik pusat cluster-nya [27].…”

Section: Pengujian Hasil Clusteringunclassified

Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia

Puspitasari

Lempas

Hamdani

et al. 2023

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Analyzing Covid-19 data has been conducted in many types of research, but research on classifying each case from Covid-19 data in all provinces in Indonesia has yet to be available. This study uses two clustering algorithms, namely K-Means and K-Medoids, to classify positive cases recovered and died in the Covid-19 data into three clusters, namely low, medium and high. The research data is Covid-19 case data in all provinces in Indonesia from 2020 to 2021. In the clustering calculations, the three distance methods used in this study are the Chebyshev Distance, Manhattan Distance, and Euclidean Distance. Based on the Silhouette Coefficient test results for the three distance calculation methods, it was found that Manhattan Distance is the best distance calculation method for K-Means and K-Medoids. Furthermore, the results of testing the Sum Squared Error (SSE), Silhouette Coefficient (SC) and Davies Index Bouldin (DBI) methods for the resulting clusters show that the value generated by the K-Means algorithm is higher in the SC and DBI methods. This result is evidenced by the SC value of 0.838; 0.838; and 0.925 in positive cases, recovered and died. While the DBI value is 0.305 for positive cases, 0.295 for recovered cases and 1.569 for dead cases. Based on these values, it proves that K-Means is superior in grouping and placing clusters compared to K-Medoids.

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Section: Pengujian Hasil Clusteringunclassified

Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia

Puspitasari

Lempas

Hamdani

et al. 2023

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“…Sum of Square Error (SSE) merupakan hasil penjumlahan dari seluruh jarak masing-masing data dengan titik pusat clusternya. Semakin kecil nilai SSE yang didapat, semakin seragam data yang ada didalam masing-masing cluster, semakin baik cluster yang dihasilkan [10].…”

Section: Pendahuluanunclassified

Seleksi Fitur Menggunakan Eigen Vector Untuk Peningkatan Kinerja K-Means Clustering Dalam Pengelompokan Data

Syahputra

Zarlis

Efendi

2022

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The large number of data set attributes from the data grouping process with K-Means Clustering can affect the number of iterations produced. In this research, Eigen Vector is used to perform feature selection on the data set. The selected data set is then clustered using K-Means Clustering. The data set used in this research is the Wine Quality Dataset obtained from the UCI Machine Learning Repository, with 11 attributes, 4898 data records and 7 attribute classes. Then the South German Credit Dataset was obtained from kaggle.com with 20 attributes, 1000 data records and 2 attribute classes. The results of this research indicate that the number of iterations obtained from the comparison of tests using K-Means without feature selection is that in the Wine Quality Dataset, 11 iterations are obtained, and in the South German Credit Dataset, there are 10 iterations. Meanwhile, K-Means with Eigen Vector feature selection obtained the number of iterations in the Wine Quality Dataset with a total of 5 iterations, and in the South German Credit Dataset with a total of 4 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering without feature selection from the Wine Quality Dataset is 678.5735, while in the South German Credit Dataset it is 1534.3167. While the K-Means Clustering with Eigen Vector from the Wine Quality Dataset is 383.0517, and the South German Credit Dataset is 469.0698. From the results of the proposed method is able to reduce the percentage of errors and minimize the number of iterations on K-Means Clustering with feature selection using Eigen Vector

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“…Kemudian semakin kecil Semakin jauh jarak yang membentuk titik siku, maka jumlah cluster tersebut menjadi yang paling optimal [18]. Rumus SSE adalah sebagai berikut [19]:…”

Section: Pengukuran Kinerja Clusteringunclassified

Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index

Zarkasyi

Mawengkang

Sitompul

2022

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In K-Means Clustering, the number of attributes of a data can affect the number of iterations generated in the data grouping process. One of the solutions to overcome these problems is by using a reduction technique on the dimensions of the dataset. In this study, the authors apply the Gini Index to perform attribute reduction on the data set to reduce attributes that have no effect on the dataset before clustering with K-Means Clustering. The dataset used to be tested as a testing instrument in this research is Absenteeism at work obtained from the UCI Machine Learning Repository, with 20 attributes, 740 data records and 4 attribute classes. The results of the tests in this research indicate that the number of iterations obtained from the comparison of tests using the K-Means in a Conversional (Without Attribute Reduction) is obtained by the number of 9 iterations, while the K-Means with attribute reduction with the Gini Index obtains the number of iterations totaling 6 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering in a Conversional (Without Attribute Reduction) is 1391.613, while in K-Means Clustering with attribute reduction with a Gini Index, it is 440.912. From the results of the proposed method, it is able to reduce the percentage of errors and minimize the number of iterations in K-Means Clustering by reducing the dimensions of the dataset using the Gini Index

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Perbaikan Kinerja Clustering K-Means pada Data Ekonomi Nelayan dengan Perhitungan Sum of Square Error (SSE) dan Optimasi nilai K cluster

Cited by 9 publications

References 4 publications

Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia

Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia

Seleksi Fitur Menggunakan Eigen Vector Untuk Peningkatan Kinerja K-Means Clustering Dalam Pengelompokan Data

Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index

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