Analisis Sentimen Dampak Putusan MK Batas Usia Minimum Capres-Cawapres dengan SVM, Naïve Bayes, dan KNN
DOI:
https://doi.org/10.30812/corisindo.v1.5523Keywords:
Analisis Sentimen, Mahkamah Konstitusi, Support Vector Machine, Naïve Bayes, K-Nearest NeighborsAbstract
Mahkamah Konstitusi (MK) berperan penting dalam menegakkan konstitusi, termasuk
menetapkan batas usia minimum pencalonan Presiden dan Wakil Presiden. Putusan ini memicu beragam
reaksi di media sosial, mulai dari dukungan hingga penolakan yang dinilai politis. Penelitian ini bertujuan
menganalisis sentimen publik terhadap putusan tersebut menggunakan Support Vector Machine (SVM),
Naïve Bayes (NB), dan K-Nearest Neighbors (KNN), serta membandingkan kinerjanya berdasarkan
akurasi, presisi, recall, dan F1-score. Penelitian dilakukan melalui enam tahap: (1) Business
Understanding – menentukan kebutuhan, tujuan, dan pengumpulan data; (2) Data Understanding –
mengumpulkan, mendeskripsikan, dan mengevaluasi kualitas data; (3) Data Preparation – membersihkan,
memilih, dan mentransformasi data; (4) Modelling – menerapkan algoritma SVM, NB, dan KNN; (5)
Evaluation – mengukur kinerja model menggunakan confusion matrix; serta (6) Deployment – menyusun
laporan dan dokumentasi hasil analisis. Data diambil dari media sosial X dan YouTube, diolah
menggunakan teknik text mining dan machine learning. Hasil menunjukkan SVM dan KNN memiliki
akurasi tertinggi, masing-masing 89,5%, sedangkan NB mencapai 88,5%, sehingga SVM dan KNN dinilai
lebih efektif dalam menganalisis sentimen publik terhadap putusan MK.
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