K-Means and Elbow Method for Cluster Analysis of Elementary School Data

Permadi, Vynska Amalia; Tahalea, Sylvert Prian; Agusdin, Riza Prapascatama

doi:10.29303/prospek.v4i1.328

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The "elbow method," which involves plotting the WCSS values against the number of clusters, revealed two significant 'elbows' at two and four clusters. These points suggested potential optimal cluster numbers [25]. However, the relatively low silhouette scores, with a maximum of only 0.293 for four clusters, indicated moderate clustering quality, potentially due to the small sample size.…”

Section: Fig 5 Hierarchical Clustering Of Isolates Showing the Two Cl...mentioning

confidence: 96%

Characterisation and Bacteriophage Sensitivity of Enterococci Isolated from Traditional Fermented Dairy Product

Hansa,

Anil,

Chandran

et al. 2024

Eur. J. Nutr. Food. Saf.

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Aims: The study aimed to detect and classify Enterococci isolates from Thayir, a traditional fermented dairy product. Study Design: A statistically significant number of Thayir samples were collected from across the study area to isolate and characterise Enterococci. Place and Duration of Study: The study took place in Wayanad District of Kerala, India in 2022-2023. Methodology: The Thayir samples were aseptically collected and bacteriologically cultured to detect enterococci. The isolates obtained were tested for the ability to form biofilms and hydrolyse gelatin. The antibiotic sensitivity pattern was studied using disk diffusion assay. A simple Kmeans clustering algorithm was applied to group the isolates. Phage-sensitive and resistant isolates were identified among the safer isolates. Results: Eight isolates were obtained. None of the isolates hydrolysed gelatin, but four produced biofilm. Antibiogram revealed increased sensitivity to nitrofurantoin, but most isolates were sensitive to antimicrobials. Clustering analysis revealed a set of three safer isolates, of which one was sensitive to the phage EFΦ91. Conclusion: The study revealed the presence of Enterococcus fecalis and Enterococcus faecium in thayir samples. Analyses revealed that the isolates were diverse and continued monitoring for antimicrobial resistance against Enterococci in food samples is necessary.

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Section: Fig 5 Hierarchical Clustering Of Isolates Showing the Two Cl...mentioning

confidence: 96%

Characterisation and Bacteriophage Sensitivity of Enterococci Isolated from Traditional Fermented Dairy Product

Hansa,

Anil,

Chandran

et al. 2024

Eur. J. Nutr. Food. Saf.

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“…The K-Means algorithm is a partitioning method for separating data into different groups. Selection of the number of groups using the Elbow Method [15]. The partition method that is used iteratively can minimize the average closeness of each data to its cluster according to Euclidean distance [16].…”

Section: K-means Clusteringmentioning

confidence: 99%

Clustering Sukuk Using the K-Means Algorithm for Allocation of Investors Based on Investment Risk Profile

Novika,

Rahayu

2024

E3S Web Conf.

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The number of capital market investors has increased by 33.53% from 7,489,337 at the end of 2021 to 10,000,628 on 3 November 2022. One of the most popular Islamic capital markets today is sukuk with high yields, lower taxes and short returns. Investors consider four main factors that affect the issuance of sukuk, namely the type of sharia contract, yield, effective term, and nominal value of the sukuk. Investors will find it very difficult to decide on their investment because they will face a lot of data and variables. The solution to this problem can be done by perform multivariate analysis by grouping sukuk based on the investor’s risk profile, namely defensive, conservative, balanced, moderately aggressive, aggressive using the KMeans machine learning compile with phyton. Sukuk data used are from Financial Services Authority (OJK) and PT Kustodian Sentral Efek Indonesia (KSEI). From the results, 3 clusters were obtained cluster 1 (65 sukuk), cluster 2 (68 sukuk) and cluster 3 (20 sukuk). The results investor risk profile classifications are the defensive and conservative types investor can invest in cluster 3, the balanced type investor can invest in cluster 2, the moderately aggressive and aggressive investor can invest in cluster 1.

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K-Means and Elbow Method for Cluster Analysis of Elementary School Data

Cited by 2 publications

References 8 publications

Characterisation and Bacteriophage Sensitivity of Enterococci Isolated from Traditional Fermented Dairy Product

Characterisation and Bacteriophage Sensitivity of Enterococci Isolated from Traditional Fermented Dairy Product

Clustering Sukuk Using the K-Means Algorithm for Allocation of Investors Based on Investment Risk Profile

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