Sentiment Analysis of the Joko Widodo Diploma Controversy Using NLP and Multi-Layer Perceptron
DOI:
https://doi.org/10.69916/jkbti.v5i3.591Keywords:
Sentiment Analysis, Natural Language Processing, Multi Layer Perceptron, TF-IDF, YouTubeAbstract
The polemic regarding the authenticity of President Joko Widodo’s diploma has become a public issue widely discussed on social media, particularly on the YouTube platform. YouTube comment sections contain various public opinions that are unstructured and written in informal language, making manual analysis difficult. Therefore, this study aims to analyze public sentiment toward the polemic of Joko Widodo’s diploma using a Natural Language Processing (NLP) approach with the Multi Layer Perceptron (MLP) algorithm. The research data were obtained from YouTube user comments related to the issue. The data were processed through text preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming. Furthermore, the text data were transformed into numerical representations using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was performed using the Multi Layer Perceptron algorithm into three sentiment classes, namely positive, negative, and neutral. Model performance evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the Multi Layer Perceptron algorithm is able to classify public sentiment with a good level of accuracy and effectively learn non-linear patterns in textual data. This study is expected to serve as a reference for the development of Indonesianlanguage sentiment analysis based on machine learning.
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Copyright (c) 2026 Herdiansyah Herdiansyah, Nazori Suhandi, Dwi Asa Verano
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