Clustering Artikel Berita Berbahasa Indonesia Menggunakan Unsupervised Feature Selection

wigati, catur tyas surya

doi:10.31219/osf.io/tm8c5

Cited by 1 publication

(1 citation statement)

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“…Text mining is used to find and process information in large amounts of text and unstructured data, and identify patterns and relationships between these patterns [10]. Text mining has several processing stages, such as text preprocessing, feature extraction, and mining [11]. The text preprocessing step consists of case folding, filtering, tokenization, normalization, and stemming.…”

Section: Text Preprocessingmentioning

confidence: 99%

Topic Modeling for Support Ticket using Latent Dirichlet Allocation

Wiranto

Uswatunnisa

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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In the business world, communication over customers must be built properly to make it easier for companies to find out what customers want. Support ticket is one of the business instrument for communication between the customers and the companies. Through a support ticket, customers can respond, complain or ask questions about products with a support team. Increasing the business process of the companies will be increasing the support ticket volume that should be handled by support team. It also has a value for analysis to get business intelligence decision. With that chance, an efficient data processing method is needed to find topics are being discussed by customers. One way that can be used to solve this problem is Topic Modeling. This research uses several parameters the number of topics, alpha value, beta value, iteration, and random seed. With this combination of parameters, the best results based on evaluation of human judgement and topic coherence with 5 topics, an alpha value of 50, a beta value of 0.01, 100 iterations, and 50 random seeds. The five topics interpretation consists of hosting migration, error problems in wordpress, domain email settings and domain transfer, ticketing and transaction processing. The total of 5 topics has a coherence value of 0.507897.

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Section: Text Preprocessingmentioning

confidence: 99%