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Mustopo, Yuning Rum Zattayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Sentimen Proyek Strategis Nasional Food Estate Menggunakan Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine Mustopo, Yuning Rum Zattayu; Afiyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3312

Abstract

The National Strategic Project Food Estate is an initiative by the Indonesian government aimed at enhancing food security through the development of large-scale agricultural areas. In the vice-presidential debate ahead of the 2024 election, Food Estate re-emerged as a hot topic, sparking controversy. Therefore, this study aims to analyze public perspectives on the National Strategic Project Food Estate by comparing the performance of machine learning algorithms, including Naïve Bayes, Logistic Regression and Support Vector Machine. This research also experiments with feature extraction techniques TF-IDF and Word2Vec. The results indicate that TF-IDF feature extraction performs better in capturing relevant features to enhance classification performance compared to the Word2Vec method. The best-performing algorithm is Logistic Regression + TF-IDF, achieving an accuracy of 74%, followed by SVM + TF-IDF and Naïve Bayes + TF-IDF with accuracies of 73% and 72%, respectively.
Analisis Sentimen Proyek Strategis Nasional Food Estate Menggunakan Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine Mustopo, Yuning Rum Zattayu; Afiyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3312

Abstract

The National Strategic Project Food Estate is an initiative by the Indonesian government aimed at enhancing food security through the development of large-scale agricultural areas. In the vice-presidential debate ahead of the 2024 election, Food Estate re-emerged as a hot topic, sparking controversy. Therefore, this study aims to analyze public perspectives on the National Strategic Project Food Estate by comparing the performance of machine learning algorithms, including Naïve Bayes, Logistic Regression and Support Vector Machine. This research also experiments with feature extraction techniques TF-IDF and Word2Vec. The results indicate that TF-IDF feature extraction performs better in capturing relevant features to enhance classification performance compared to the Word2Vec method. The best-performing algorithm is Logistic Regression + TF-IDF, achieving an accuracy of 74%, followed by SVM + TF-IDF and Naïve Bayes + TF-IDF with accuracies of 73% and 72%, respectively.
Co-Authors Afiyati