The Analysis of Digital Provider Sentiment Of ‘by.u’ On Google Play Which Uses The Support Vector Machine (SVM) Method

Ardiansyah, Angga; Aji, Sopian; Pratmanto, Dany; Kuryanti, Sandra Jamu; Putra, Octa Pratama; Adiwihardja, Cep

doi:10.1088/1742-6596/1641/1/012048

Cited by 2 publications

(1 citation statement)

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“…There needs to be a method that can be used to get information on reviews effectively and efficiently, one of which is Text Mining. One of the Text Mining analysis techniques is sentiment analysis (Ardiansyah, et al, 2020). With sentiment analysis, the company can find out information about the responses and attitudes of a group or individual towards a topic of contextual discussion of the entire document.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

Hakim

Putra

Khasanah

2021

Int. J. Ind. Optim.

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In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample. The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%. Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product.

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Section: Introductionmentioning

confidence: 99%