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      "abstract": "Teknologi menjadi salah satu alat bantu yang berguna oleh manusia dalam kehidupan \r\ndan aktivitas keseharian mereka karena dapat memudahkan proses tersebut sehingga tidak\r\nmembuang banyak energi dan waktu. Salah satu tipe orang yang dapat memanfaatkan\r\nteknologi tersebut adalah orang-orang yang memiliki disabilitas terutama yang memiliki\r\nkesusahan dalam berkomunikasi verbal. Maka dari itu, penelitian ini bertujuan untuk\r\nmenerapkan hand gesture recognition (HGR) berbasis algoritma convolutional neural\r\nnetwork (CNN) untuk mengenali bahasa isyarat. Dari 1139 total dataset yang digunakan,\r\nmodel CNN yang terlatih selama 88 epoch dapat mencapai tingkat akurasi training paling\r\ntinggi 0.76 dan 0.78 untuk validation. Saat model tersebut digunakan secara langsung,\r\nmodel berhasil menangkap dan mengenali pergerakan tangan bahasa isyarat berbasis SIBI\r\ndengan nilai akurasi diatas 50%. Salah satu alasan mengapa model ini tidak dapat meraih\r\nangka yang lebih tinggi secara konsisten dikarenakan bahan data yang kurang bervariasi\r\ndan metode pelatihan yang tidak menggunakan transfer learning sehingga perlu\r\nmenggunakan banyak data.",
      "title": "PENERAPAN HAND GESTURE RECOGNITION UNTUK\r\nPENGENALAN BAHASA ISYARAT MENGGUNAKAN \r\nALGORITMA CONVOLUTIONAL NEURAL NETWORK",
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      "keywords": "Sistem Informasi, Teknologi, Machine Learning, CNN, Image\r\nRecognition, Information Systems, Technology.",
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