Data Mining for Determining The Best Cluster Of Student Instagram Account As New Student Admission Influencer

Abdullah, Ahmad Irfan; Priadana, Adri; Muhajir, Muhajir; Nur, Syahrir Nawir

doi:10.31315/telematika.v18i2.5067

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…If the Silhouette Score value of 0 is close to 1, then the cluster containing the objects is very dense, and the objects are far apart from other clusters, which shows the better quality of the cluster. On the other hand, if the Silhouette Score of 0 is close to -1, it means that the cluster that contains objects is not dense, and the object is very close to other clusters, which shows that the quality of the cluster is getting worse [26].…”

Section: Evaluation Of Clustering Resultsmentioning

confidence: 99%

A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform

Cahyo

Sudarmana²

2022

ELINVO

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Brainly is a question and answer (QA) site that students can use as a media for questions and answers. Students can also use Brainly to find and share educational information that helps students solve their homework problems. In Brainly, users can answer questions according to their interests. However, it could be that the interest is not necessarily following the competencies possessed. It causes many answers to the questions given not to have a high rating because the answers given are of low quality to be prioritized as the main answer. This study aims to apply the K-Means and Agglomerative Clustering methods to segment users based on the reputation of the answerers by conducting clustering based on track records in answering questions on mathematics subjects. This study used the number of the brightest scores and the number of answers that did not get a rating as the basic features for clustering. The comparison between the two methods used is based on the Silhouette Score, representing the quality of the clustering results, calculated by applying the Silhouette Coefficient method. This study result indicates that the K-Means method gives better results than the Agglomerative Clustering. The Silhouette Score generated by the K-Means method is higher at 0.9081 than the Agglomerative Clustering method, which is 0.8990, which produces two clusters or two segments.

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Section: Evaluation Of Clustering Resultsmentioning

confidence: 99%

A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform

Cahyo

Sudarmana²

2022

ELINVO

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“…Precision, recall, and F1-score was also used to measure the detail performance of the proposed CNN architecture based on the distribution of the dataset. The precision was calculated by using an equation described in (5), the recall is calculated by using an equation described in (6), and the F1-score is calculated by using an equation described in (7). TP indicates true positive, FP indicates false positive, and FN indicates false negative.…”

Section: Discussionmentioning

confidence: 99%

“…The emergence of technology such as web scraping allows the acquisition and collection of information from various web pages to be made automatically in a short time [3]- [7]. In addition to performing it, categorizing information obtained is also needed to facilitate the recipients of information in determining and filtering which information is required.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text

Murdiyanto

Habibi

2022

Compiler

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The volume of digital documents available online is growing exponentially due to the increasing use of the internet. Categorization of information obtained online is needed to make it easier for recipients of information to determine and filter which information is needed. Classification of web pages can be based on titles and descriptions, which are text data that can be done by utilizing deep learning technology for text classification. This study aimed to conduct data training and analysis experiments to determine the accuracy of the proposed deep learning architecture in classifying web page titles and descriptions. In this research, we proposed a Convolution Neural Network (CNN) architecture that generates few parameters. The training and evaluation set was conducted on the web page dataset provided by DMOZ. As a result, the proposed CNN architecture with the number of N (Dropout + 1D Convolution + ReLU activation) equal to 1 achieves the best validation accuracy. It achieves 79.51% with only generates 825,061 parameters. The proposed CNN architecture achieved outperformed performance on the accuracy of the five other technologies in the state-of-the-art.

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Method to map human and infrastructure vulnerability using CNN land cover: Case study of floating tank explosion at petrochemical plants of LaemChabang, Thailand

Sentagne

Zerbola

Garcia

et al. 2023

Journal of Loss Prevention in the Process Industries

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Data Mining for Determining The Best Cluster Of Student Instagram Account As New Student Admission Influencer

Cited by 3 publications

References 13 publications

A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform

A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform

Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text

Method to map human and infrastructure vulnerability using CNN land cover: Case study of floating tank explosion at petrochemical plants of LaemChabang, Thailand

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