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      "keywords": "Analisis Sentimen, CapCut, SMOTE, SVM, Sentiment Analysis.",
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      "department": "Fakultas Ilmu Komputer",
      "date": "2025-12-22",
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            "given": "Gibran Hait",
            "family": "Sami",
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      "title": "ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI\r\nCAPCUT DI PLAY STORE MENGGUNAKAN ALGORITMA\r\nSUPPORT VECTOR MACHINE (SVM)",
      "abstract": "Volume ulasan aplikasi CapCut di Google Play Store yang sangat besar dan\r\ntidak terstruktur, meliputi komponen seperti emoji serta ragam bahasa informal,\r\nmenyulitkan pengembang dalam mengekstraksi wawasan secara manual. Selain itu,\r\nketimpangan jumlah data yang signifikan antara ulasan positif dan negatif\r\nmenimbulkan masalah ketidakseimbangan data (imbalanced data) yang berdampak\r\npada biasnya prediksi model. Penelitian ini bertujuan mengklasifikasikan sentimen\r\nulasan menggunakan algoritma Support Vector Machine (SVM) yang\r\ndikombinasikan dengan Synthetic Minority Over-sampling Technique (SMOTE)\r\nuntuk mengatasi tantangan tersebut. Metode penelitian meliputi pengumpulan data,\r\nfiltering rating bintang 3, pra-pemrosesan teks menggunakan library Sastrawi untuk\r\nmenangani pola bahasa, pembobotan fitur TF-IDF, serta penerapan SMOTE untuk\r\nmenyeimbangkan dataset menjadi 4.800 data sebelum diklasifikasikan dengan\r\nSVM Kernel Linear. Hasil akhir pada penelitian menunjukkan bahwa penerapan\r\nSMOTE mampu meningkatkan performa model secara signifikan dengan perolehan\r\nakurasi sebesar 94% dan recall kelas negatif mencapai 0.98. Hasil penelitian ini\r\ndapat dimanfaatkan oleh tim pengembang CapCut untuk melakukan evaluasi\r\nperbaikan fitur secara otomatis dan efisien, serta menjadi referensi bagi peneliti lain\r\ndalam penanganan data teks tidak seimbang.",
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