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      "abstract": "Aplikasi Cek Bansos yang dikembangkan oleh Kementerian Sosial Republik\r\nIndonesia menjadi sarana utama bagi masyarakat untuk mengakses informasi\r\nbantuan sosial. Namun, aplikasi ini menerima beragam tanggapan dari pengguna,\r\nsehingga diperlukan analisis sentimen untuk memahami persepsi masyarakat\r\nsecara lebih objektif. Penelitian ini bertujuan untuk membandingkan kinerja\r\nalgoritma Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN)\r\ndalam mengklasifikasikan sentimen ulasan pengguna aplikasi Cek Bansos.\r\n\r\nData ulasan diperoleh melalui proses web scraping dan dilabeli secara manual\r\nuntuk menghasilkan dataset yang akurat. Tahapan penelitian meliputi\r\npreprocessing teks, ekstraksi fitur menggunakan TF-IDF, pembagian dataset\r\nmenjadi data latih dan data uji, serta penyeimbangan data pada data latih\r\nmenggunakan SMOTE. Proses pemodelan dan optimasi parameter dilakukan\r\nmenggunakan GridSearchCV dengan teknik cross-validation.\r\n\r\nHasil evaluasi menunjukkan bahwa SVM memiliki performa lebih baik\r\ndibandingkan KNN, dengan akurasi sebesar 88,62% dan F1-Score Macro sebesar\r\n0,79, sedangkan KNN memperoleh akurasi sebesar 87,13% dan F1-Score Macro\r\nsebesar 0,75. Performa SVM yang lebih stabil terutama terlihat pada\r\nkemampuannya dalam mengenali ulasan positif sebagai kelas minoritas. Temuan\r\nini menunjukkan bahwa SVM lebih efektif digunakan dalam analisis sentimen\r\nulasan aplikasi Cek Bansos, khususnya pada data yang tidak seimbang dan\r\nmemiliki variasi linguistik yang tinggi.",
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      "title": "EVALUASI METODE SVM DAN KNN DALAM ANALISIS\r\nSENTIMEN PADA APLIKASI CEK BANSOS",
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