TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sentiments analysis of customer satisfaction in public services
using K-nearest neighbors algorithm and natural language processing approach

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sentiments analysis of customer satisfaction in public services
using K-nearest neighbors algorithm and natural language processing approach

K-nearest neighbors
Natural language processing
Public service
Sentiment analysis
Speech recognition system

Customer satisfaction is very important for public service providers, customer
satisfaction can be delivered with a survey application or writing criticism that
can be used to evaluate and improve service. Unfortunately, there are only a
few customers who are willing to give an assessment. The survey application
cannot represent the overall feeling of the customer, so it is necessary to
analyze the content of the conversation between the customer and the service
personnel to determine the level of customer satisfaction. In small amounts, it
can be done manually, but in large quantities it is more effective to use the
system. A solution is needed in the form of a system that converts voice
conversations into text and analyzes customer satisfaction to obtain

information for evaluation and improvement of services. This research uses K-
nearest neighbors (KNN) and term frequency-inverse document frequency

(TF-IDF) algorithm with natural language processing (NLP) approach to
classify conversations into 2 classes, "satisfied" and " dissatisfied ". The results
of this study received 74.00% accuracy, 76.00% precision and 73.08% recall.
In conversations with the label "satisfied" shows customers satisfied with the
service and fulfillment of customer desires, while in conversations with the
label "not satisfied" customers are less satisfied with the waiting time.

Elik Hari Muktafin, Pramono, Kusrini

http://journal.uad.ac.id/index.php/TELKOMNIKA

Sep 5, 2020

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