KLASIFIKASI SARAN DAN KRITIK PADA SIMAK UNISMUHDENGAN MENGGUNAKAN ALGORTIMA RECCURENCTNEURAL NETWORK (RNN)
DOI:
https://doi.org/10.26618/8ttaxq04Abstract
SIMAK Unismuh Makassar is an important platform used by students to submit suggestions and
criticisms related to various academic aspects. In this study, researchers implemented the
Recurrent Neural Network (RNN) algorithm to classify suggestions and criticisms received
through SIMAK Unismuh. The purpose of this study was to determine the implementation of the
RNN Algorithm in classifying suggestions and criticisms on the SIMAK Unismuh page and how
successful the RNN Algorithm was in classifying suggestions and criticisms on the SIMAK
Unismuh page. RNN was chosen because of its ability to process sequential text data, such as
input in the form of sentences, which allows the model to capture the context of the input more
effectively. The dataset used in this study consists of a number of suggestion and criticism data
that have been categorized manually. The RNN model that was built was then trained and tested
using the data to assess its accuracy and performance. The results showed that the model
achieved the highest accuracy of 91% and the lowest accuracy of 90%. Although there were
variations in model performance, these results indicate that RNN has good potential in classifying
suggestion and criticism texts. The RNN model can help institutions understand and respond to
user input more effectively, although it still requires further optimization to improve the consistency
and accuracy of the results. The conclusion of this study shows that the RNN model is able to
classify suggestions and criticisms with an adequate level of accuracy. The application of this
model is expected to help the Unismuh administration in managing student input more efficiently,
as well as providing more appropriate and faster responses to academic needs.
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