Klasifikasi Perilaku Konsumen Pasca Boikot Produk Israel Menggunakan Naive Bayes dan SVM
Abstract
The ongoing Israel–Palestine conflict has contributed to the rise of consumer boycott movements directed at products associated with Israel. These reactions are prominently articulated on social media platforms and indicate changing patterns in consumer attitudes and behavior. This research seeks to analyze and classify public sentiment in Indonesia regarding the boycott issue by employing Natural Language Processing (NLP) techniques in combination with Machine Learning methods, specifically Naïve Bayes and Support Vector Machine (SVM). The dataset comprises user-generated comments obtained from TikTok and Instagram between October 2023 and September 2024 through web scraping procedures. The data were subsequently subjected to manual annotation, text preprocessing, and feature extraction using the TF-IDF weighting scheme. The dataset was partitioned into 80% training data and 20% testing data, and model performance was assessed using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the SVM model outperformed Naïve Bayes on the training set, achieving an accuracy of 81% and demonstrating stronger generalization in detecting positive sentiment. In contrast, the Naïve Bayes classifier attained an accuracy of 78%, showing consistent performance and superior capability in identifying negative sentiment. These results underscore the significance of selecting classification algorithms that are well suited to the distributional characteristics of sentiment data derived from social media.
Keywords: Text Classification; Support Vector Machine; Naïve Bayes; Boycott; Consumer Behavior
Abstrak
Konflik Israel–Palestina yang terus berlangsung telah mendorong munculnya gerakan boikot konsumen terhadap produk-produk yang memiliki keterkaitan dengan Israel. Respons tersebut banyak diekspresikan melalui platform media sosial dan mencerminkan perubahan pola sikap serta perilaku konsumen. Penelitian ini bertujuan untuk menganalisis dan mengklasifikasikan sentimen masyarakat Indonesia terhadap isu boikot tersebut dengan menerapkan teknik Natural Language Processing (NLP) yang dikombinasikan dengan metode Machine Learning (ML), yaitu Naïve Bayes dan Support Vector Machine (SVM). Dataset penelitian terdiri atas komentar pengguna yang dikumpulkan dari platform TikTok dan Instagram selama periode Oktober 2023 hingga September 2024 melalui teknik web scraping. Data selanjutnya melalui proses anotasi manual, praproses teks, serta ekstraksi fitur menggunakan skema pembobotan TF-IDF. Dataset dibagi menjadi 80% data latih dan 20% data uji, dengan kinerja model dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa model SVM menghasilkan performa yang lebih unggul pada data latih dengan tingkat akurasi sebesar 81% serta memiliki kemampuan generalisasi yang lebih baik dalam mendeteksi sentimen positif. Sementara itu, algoritma Naïve Bayes mencapai akurasi sebesar 78% dan menunjukkan kinerja yang konsisten serta lebih efektif dalam mengidentifikasi sentimen negatif. Temuan ini menegaskan pentingnya pemilihan algoritma klasifikasi yang sesuai dengan karakteristik distribusi data sentimen yang bersumber dari media sosial.
Keywords
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