Authors :
Fernandus Paian Sitorus; Ema Utami; Mei Parwanto Kurniawan
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2p9f9uyk
DOI :
https://doi.org/10.5281/zenodo.8310746
Abstract :
The educational curriculum is a device or
system of plans and arrangements regarding learning
materials on teaching and learning activities. If the
curriculum does not meet the requirements or facilitate
teaching and learning activities, then the curriculum
cannot be said to be good. The aim of this research is to
filter and analyze sentiment from public opinion towards
the newest curriculum in Indonesia, namely the
independent curriculum, which will be made into the
national curriculum in the upcoming 2024. The dataset
used is tweets from Twitter as many as 667 lines of tweets
labeled as positive and negative categories. The labeling
process is done automatically using the VADER
sentiment library. In sentiment analysis, one of the
classification methods that is quite good in sentiment
classification is Naive Bayes and K-Nearest Neighbor
(KNN). The Naive Bayes method is fairly good in the data
classification process that can study the training data
provided to it properly, while KNN is a simple method
that is quite easy to understand and is often used in the
classification process which produces quite good
accuracy compared to other methods. The stages in
conducting sentiment analysis in this study are data
collection, data preprocessing, labeling or annotation of
data, data visualization, classification and evaluation.In addition, the results of
sentiment tend to be negative, so it can be concluded that
the independent curriculum which will be made into the
national curriculum has not been well received by the
public, so this can be taken into consideration for the
Indonesian government to make the independent
curriculum a national curriculum.
Keywords :
Sentiment Analysis, Independent Curriculum, Naive Bayes, KNN, VADER, Natural Language Processing.
The educational curriculum is a device or
system of plans and arrangements regarding learning
materials on teaching and learning activities. If the
curriculum does not meet the requirements or facilitate
teaching and learning activities, then the curriculum
cannot be said to be good. The aim of this research is to
filter and analyze sentiment from public opinion towards
the newest curriculum in Indonesia, namely the
independent curriculum, which will be made into the
national curriculum in the upcoming 2024. The dataset
used is tweets from Twitter as many as 667 lines of tweets
labeled as positive and negative categories. The labeling
process is done automatically using the VADER
sentiment library. In sentiment analysis, one of the
classification methods that is quite good in sentiment
classification is Naive Bayes and K-Nearest Neighbor
(KNN). The Naive Bayes method is fairly good in the data
classification process that can study the training data
provided to it properly, while KNN is a simple method
that is quite easy to understand and is often used in the
classification process which produces quite good
accuracy compared to other methods. The stages in
conducting sentiment analysis in this study are data
collection, data preprocessing, labeling or annotation of
data, data visualization, classification and evaluation.In addition, the results of
sentiment tend to be negative, so it can be concluded that
the independent curriculum which will be made into the
national curriculum has not been well received by the
public, so this can be taken into consideration for the
Indonesian government to make the independent
curriculum a national curriculum.
Keywords :
Sentiment Analysis, Independent Curriculum, Naive Bayes, KNN, VADER, Natural Language Processing.
