ANALISIS SENTIMEN BERITA ONLINE LPK IBI-K57 MENGGUNAKAN PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAÏVE BAYES CLASSIFIER
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Abstract
Perkembangan teknologi informasi menyebabkan penyebaran berita digital berlangsung sangat cepat sehingga diperlukan metode otomatis untuk mengetahui kecenderungan opini yang terkandung di dalamnya. Penelitian ini bertujuan mengembangkan sistem analisis sentimen berita online LPK IBI-K57 menggunakan algoritma Support Vector Machine (SVM) dan Naïve Bayes Classifier (NBC) serta membandingkan performa kedua algoritma dalam mengklasifikasikan sentimen positif, netral, dan negatif. Dataset simulasi terdiri atas 1.200 berita, yang dibagi menjadi 960 data latih (80%) dan 240 data uji (20%). Tahapan penelitian meliputi web scraping, preprocessing (case folding, tokenizing, stopword removal, dan stemming), ekstraksi fitur menggunakan TF-IDF, pelatihan model, serta evaluasi menggunakan confusion matrix, accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa algoritma Support Vector Machine memperoleh akurasi sebesar 95,42%, precision 95,31%, recall 95,58%, dan F1-score 95,44%, sedangkan algoritma Naïve Bayes Classifier memperoleh akurasi sebesar 91,25%, precision 91,08%, recall 91,37%, dan F1-score 91,21%. Berdasarkan hasil tersebut, algoritma SVM menunjukkan performa terbaik dalam mengklasifikasikan sentimen berita online LPK IBI-K57 sehingga dipilih sebagai model implementasi pada sistem analisis sentimen. Sistem yang dikembangkan mampu membantu lembaga memantau persepsi publik secara lebih cepat, objektif, dan akurat sebagai dasar evaluasi strategi komunikasi digital.
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