Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection


  • Endang Suherman Universitas Pradita, Serpong, Banten, Indonesia
  • Djarot Hindarto Universitas Pradita, Serpong, Banten, Indonesia
  • Amelia Makmur Universitas Pradita, Serpong, Banten, Indonesia
  • Handri Santoso Universitas Pradita, Serpong, Banten, Indonesia




Artificial Neural Network, Convolutional Neural Network, Detection, Deep Learning, Machine Learning, Rice


Rice is a staple food for people in tropical countries. Indonesia is a country that needs a lot of rice for its people in providing food. This country has implemented various ways to plant rice properly. Many agricultural fields have implemented harvests up to three times a year, due to the role of technology which has helped a lot in agriculture. Planting to harvest already uses advanced technology and tools. A good rice harvest can improve the welfare of the surrounding community. Meanwhile with lots of rice products because many rice plants produce with lots of rice. The type of rice from different regions of origin, the yield of rice is also different from other regions of origin. But with advances in technology, it is possible to plant rice whose types of plants come from other regions. The rice sold to the public varies, so that people who are unfamiliar with the types of rice find it difficult to detect the types of rice. Machine learning is present in detecting various kinds of rice. Machine learning, especially deep learning can make better detection, because one of the deep learning methods works similar to the human brain. In the human brain there are millions or even billions of neurons. This research uses neural networks in experiments using public datasets. Experiments using Artificial Neural Networks achieve an training accuracy of 98.2%, loss: 0.2351. It takes about 10 minutes of training. Testing accuracy reaches accuracy: 96%, loss: 0.6641. By conducting experiments using the Convolution Neural Network, it achieves an accuracy of 99.3% and the training time requires around 18 hours. The purpose of this research is to classify the rice image dataset and detect the rice image.

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How to Cite

Suherman, E. ., Hindarto, D. ., Makmur, A. ., & Santoso, H. . (2023). Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 247-255.

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