Privacy-Preserving K-NN Classification Using Vector Operations

Jalla, Hanumantharao; Girija, P. N.

doi:10.1007/978-981-13-0586-3_64

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…In addition, Twitter data nowadays can be obtained easily, so the phenomenon is attractive to study [5]. Utilizing Twitter data submitted for OVO and DANA has led to the phenomenon of big data.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Model Development on E-Money Service Complaints

Tahyudin,

Rafi Hananto,

Anggun Rahayu

et al. 2023

TEM Journal

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Technology provides various conveniences for users in many aspects, such as in the commercial business world with the development of financial technology or fintech. In Indonesia, there are fintech services with the most users, namely OVO and DANA. Various kinds of criticisms and suggestions related to deficiencies and weaknesses in OVO and DANA services were submitted by users via social media Twitter. This study aims to analyze and develop a sentiment identification model for user complaints against OVO and DANA services on Twitter. This study uses the four classification algorithm methods: Random Forest, K-NN, Decision Tree, and Naïve Bayes Classifier. The data from social media Twitter is taken from user tweets regarding criticism and complaints on OVO and DANA services. The data obtained was 5,000 instances consisting of 20% positive and 80% negative reviews. Accuracy values represent the study results with 87% for Naive Bayes, 86% for Decision Tree, 91% for K-NN, and 87% for Random Forest. Based on these results, it can be concluded that testing of sentiment analysis on user complaints on OVO and DANA services using the K-NN algorithm is superior to the other algorithms. This research results in a system that combines the four prediction models. The evaluation shows that the developed model can accurately classify user sentiment.

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Section: Introductionmentioning

confidence: 99%