Optimasi Cluster Pada Data Stunting: Teknik Evaluasi Cluster Sum of Square Error dan Davies Bouldin Index

Jollyta, Deny; Efendi, Syahril; Zarlis, Muhammad; Mawengkang, Herman

doi:10.30645/senaris.v1i0.100

Cited by 13 publications

(12 citation statements)

References 2 publications

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“…Prinsip pendekatan pada pengukuran evaluasi DBI ialah memaksimalkan jarak inter cluster dan meminimalkan jarak intra cluster [15]. Semakin kecil nilai DBI yang dihasilkan maka skema klaster termasuk ke dalam cluster yang optimal [16].…”

Section: Discussionunclassified

Implementasi Metode K-Means Clustering dalam Analisis Persebaran UMKM di Jawa Barat

Syifa

Fahmi

2021

Journal of Information System

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Usaha Mikro Kecil dan Menengah atau UMKM ialah usaha produktif yang sudah teruji membuka lapangan kerja dan menjadi penggerak roda perekonomian di Indonesia. Perlu adanya pengembangan potensi dalam melaksanakan UMKM dengan menganalisis strategi persebaran UMKM dan peningkatan jumlah UMKM. Penelitian ini akan menjelaskan cara mengimplementasikan metode k-means clustering untuk menganalisis persebaran UMKM sehingga diharapkan dapat menjadi perhatian bagi pemerintah atau institusi terkait dalam meningkatkan perekonomian UMKM di Jawa Barat. Clustering UMKM akan dibagi menjadi 3 bagian berdasarkan tingkat persebaran UMKM yaitu cluster 0 atau rendah memperoleh hasil 9 Kabupaten/Kota, cluster 1 atau sedang memperoleh hasil 15 Kabupaten/Kota, dan cluster 2 atau tinggi memperoleh hasil 3 Kabupaten/Kota. Hasil evaluasi clustering akan dibandingkan dengan 2 metode evaluasi yaitu silhouette coefficient yang menghasilkan nilai sebesar 0,73, sedangkan metode davies bouldin index (DBI) menghasilkan nilai sebesar 0,29. Dari hasil perbandingan kedua evaluasi cluster tersebut menunjukan hasil cluster dengan menerapkan algoritma k-means yang terbentuk baik.

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Section: Discussionunclassified

Implementasi Metode K-Means Clustering dalam Analisis Persebaran UMKM di Jawa Barat

Syifa

Fahmi

2021

Journal of Information System

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“…Study [15], The outcome is information on stunting data derived from the ideal number of clusters, with values of 23,403 and 1,178 for the highest SSE and smallest DBI, respectively, for k=5. Study [16], The number of villages or sub-districts based on the presence and kind of small and micro industries was utilized as the data.…”

Section: Related Researchmentioning

confidence: 99%

Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

Solikhun

Yasin²,

Nasution

2022

Int. J. Artif. Intell. Robot.

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The need for eggs that continues to increase will not increase with large egg production so that there is a shortage of egg supplies which results in high egg prices. It is necessary to group egg production in Indonesia to find out which areas fall into the high cluster and which areas fall into the low cluster. This study aims to classify the egg production of laying hens in Indonesia. The method used is the K-Means Clustering method which is a popular clustering method. To find out how optimal the number of clusters in the K-Means method is for grouping egg production in Indonesia, the researcher evaluates the DBI value of each number of existing clusters. In this study, 8 clusters were used, namely 2 clusters, 3 clusters, 4 clusters, 5 clusters, 6 clusters, 7 clusters, 8 clusters, and 9 clusters. The results of measuring the DBI value are the number of clusters 2 = 0.215, the number of clusters 3 = 0.149, the number of clusters 4 = 0.146, the number of clusters 5 = 0.157, the number of clusters 6 = 0.180, the number of clusters 7 = 0.205, the number of clusters 8 = 0.192 and the number of clusters 9 = 0.154. This study shows that the best number of clusters is the number of clusters 4 with the smallest DBI value of 0.146.

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“…DBI evaluation is seen from the number and proximity of data from clustering results, where whether or not the cluster results are seen from the quantity and proximity between the data from the cluster results. The DBI measurement method is to maximize the distance between clusters and minimize the distance between clusters (Jollyta, Efendi, Zarlis, & Mawengkang, 2019). The smaller the DBI value, the better the cluster.…”

Section: Evaluation and Validitymentioning

confidence: 99%

Comparative Study of Fuzzy C-Means and K-Means Algorithm for Grouping Customer Potential in Brand Limback

Aditya

Fitrianah

2021

JRisInf

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The customer is a stakeholder for a business, to maintain and increase customer enthusiasm and develop it for the company's performance, it is necessary to do customer segmentation which aims to find out potential customers. This study uses purchase transaction data from Brand Limback customers in the period 2020. The use of RFM (Recency, Frecuency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The datasets generated from RFM are grouped using the Fuzzy C-Means and K-Means algorithms, the two algorithms will compare the quality in the formation of clusters using the Silhoutte Coefficient and Davies-Bouldin Index methods. Customer segmentation from the RFM dataset that has been clustered produces 7 optimal clusters, namely Cluster 0 is a bronze customer. Cluster 1 is a silver customer. Cluster 2 is a gold customer. Cluster 3 is a platinum customer. Cluster 4 is a diamond customer. Cluster 5 is a super customer, and cluster 6 is a superstar customer. The cluster validation of k-means using the silhouette coefficient produces a value of 0.934 while the Davies bouldin index produces a value of 0.155 and the validation results of the fuzzy c-means algorithm using the silhouette coefficient produces a value of 0.921 while the Davies bouldin index produces a value of 0.145.

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Optimasi Cluster Pada Data Stunting: Teknik Evaluasi Cluster Sum of Square Error dan Davies Bouldin Index

Cited by 13 publications

References 2 publications

Implementasi Metode K-Means Clustering dalam Analisis Persebaran UMKM di Jawa Barat

Implementasi Metode K-Means Clustering dalam Analisis Persebaran UMKM di Jawa Barat

Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

Comparative Study of Fuzzy C-Means and K-Means Algorithm for Grouping Customer Potential in Brand Limback

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