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      "title": "OPTIMASI THRESHOLD ANALISIS SENTIMEN\r\nKOMENTAR YOUTUBE PROGRAM MBG DENGAN\r\nMETODE LOGISTIC REGRESSION",
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            "given": "Edelweiss Rinjani"
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      "institution": "Universitas AMIKOM Yogyakarta",
      "dir": "disk0\/00\/03\/20\/93",
      "thesis_type": "bachelor",
      "thesis_name": "skripsi",
      "keywords": "Analisis Sentimen, Program Makanan Bergizi, YouTube, Logistic\r\nRegression, Optimasi Threshold, Sentiment Analysis, Nutritious Meal Program.",
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      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/32093",
      "date": "2026-03-02",
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      "abstract": "Program Makanan Bergizi (MBG) merupakan salah satu kebijakan \r\npemerintah yang bertujuan untuk meningkatkan kualitas gizi peserta didik di\r\nsekolah. Namun, pelaksanaan program ini menimbulkan beragam tanggapan dari\r\nmasyarakat, khususnya yang disampaikan melalui media sosial YouTube.\r\nPerbedaan persepsi tersentuh menunjukkan adanya potensi kesenjangan antara\r\ntujuan program dan penerimaan publik. Oleh karena itu, diperlukan analisis\r\nsentimen untuk mengetahui kecenderungan opini masyarakat serta sebagai bahan\r\nevaluasi terhadap pelaksanaan Program Makanan Bergizi.Penelitian ini\r\nmenggunakan data berupa komentar pengguna YouTube yang membahas Program\r\nMakanan Bergizi Gratis (MBG). Data kemudian melalui tahapan preprocessing\r\nteks dan ekstraksi fitur menggunakan metode Term Frequency-Inverse Document\r\nFrequency (TF_IDF). Karena distribusi kelas sentimen tidak seimbang, diterapkan\r\nmetode Synthetic Minority Oversampling Technique (SMOTE) pada data latih.\r\nProses klasifikasi dilakukan menggunakan algoritma Logistic Regression dengan\r\ntuning threshold. Evaluasi model dilakukan menggunakan metrik accuracy,\r\nprecision, recall, F1-Score.\r\n \r\nHasil penelitian menunjukkan bahwa persepsi publik terhadap Program \r\nMakanan Bergizi (MBG) cenderung didominasi oleh sentimen negatif. Selain itu,\r\nperbedaan nilai threshold berpengaruh signifikan terhadap kinerja model,\r\nkhususnya dalam mengenali kelas minoritas. Threshold 0.30 menghasilkan nilai\r\nF1-Score tertinggi, sehingga memberikan keseimbangan terbaik antara precision\r\ndan recall. Hasil penelitian ini dapat dimanfaatkan oleh pembuat kebijakan sebagai\r\nbahan evaluasi berbasis opini publik, serta menjadi referensi bagi penelitian\r\nselanjutnya dalam pengembangn analisis sentimen pada data tidak seimbang.",
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