Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit

Tri Septianto, E. Setyati, Joan Santoso
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引用次数: 3

Abstract

The object of the inscription has a feature that is difficult to recognize because it is generally eroded and faded. This study analyzed the performance of CNN using LeNet model to recognize the object of year digit found on the relic inscriptions of Majapahit Kingdom. Object recognition with LeNet model had a maximum accuracy of 85.08% at 10 epoch in 6069 seconds. This LeNet's performance was better than the VGG as the comparison model with a maximum accuracy of 11.39% at 10 epoch in 40223 seconds.
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美国有线电视新闻网LeNet模型在马贾帕希特王国投降实践年度表彰中的应用
铭文的对象具有难以识别的特征,因为它通常被侵蚀和褪色。本研究利用LeNet模型,分析了CNN对玛迦巴希王国遗址铭文中发现的年数字对象的识别性能。LeNet模型在6069秒内10历元的目标识别精度达到85.08%。该LeNet的性能优于VGG作为比较模型,在40223秒内10历元的最大精度为11.39%。
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审稿时长
6 weeks
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