{
      "department": "Fakultas Ilmu Komputer",
      "rev_number": 18,
      "title": "ANALISIS SENTIMEN REVIEW PENGGUNA TERHADAP\r\nAPLIKASI RUANG GURU PADA PLAY STORE \r\nMENGGUNAKAN ALGORITMA BERT DENGAN METODE\r\nNLP",
      "date": "2025-08-11",
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            "family": "Yaqin",
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            "family": "Yudharistya",
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          "nim": "20.11.3395"
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      "institution": "Universitas AMIKOM Yogyakarta",
      "subjects": [
        "000.000.000A",
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      "abstract": "Ruangguru merupakan perusahaan teknologi terbesar di Indonesia yang\r\nberfokus pada layanan berbasis pendidikan. Ruangguru telah memiliki lebih dari\r\n22.000.000 pengguna baik didalam App Store maupun Play Store serta mengelola\r\nlebih dari 300.000 guru yang menawarkan jasa lebih dari 100 bidang pelajaran.\r\nRuangguru telah dipercaya untuk bermitra dengan 32 Pemerintah Provinsi dan 326\r\nPemerintah Kota dan Kabupaten di Indonesia. Sehubungan dengan kepopuleran\r\naplikasi Ruangguru beragam reaksi dan tanggapan positif maupun negatif dari\r\npengguna(masyarakat). Tanggapan ini pun dilontarkan baik didalam situs\r\nRuangguru dan Play Store atau App Store. Maka dari itu perlu dilakukan analisis\r\ncustomer review pada aplikasi Ruangguru pada Play Store untuk mengukur\r\nbagaimana respon masyarakat terhadap Ruangguru. Algoritma untuk penelitian ini\r\nmenggunakan BERT dengan metode NLP, dengan klasifikasi sentimen positif,\r\nnetral, dan negatif. Dengan dataset awal sebanyak 5001 data dan setelah di\r\nbalancing menjadi 8493 data, rasio pembagian 80:10:10 untuk latih, validasi, dan\r\nuji, serta hyperparameter batch size 32, optimizer AdamW dengan learning rate\r\n2e-6, dan 20 epoch. Hasil penelitian ini dengan model BERT menghasilkan akurasi\r\nsebesar 91%. Model BERT dalam mengklasifikasikan menunjukkan precision,\r\nrecall, dan F1-score rata-rata makro sebesar 0.91. Kinerja untuk kelas negatif dan\r\nnetral kuat, dengan precision dan recall masing-masing di atas 88%, sementara\r\nkelas positif memiliki precision tinggi (94%) tetapi recall lebih rendah (85%),\r\nmenunjukkan beberapa sampel positif tidak terdeteksi.",
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