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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Chen, Y., & Ge, Y. (2022). Spatiotemporal image fusion using multiscale attention-aware two-stream convolutional neural networks. Science of Remote Sensing, 6(June), 100062.

Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, D. (2022). Deep Convolution Neural Network sharing for the multi-label images classification. Machine Learning with Applications, 10(April 2021), 100422.

Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C. W. (2023). Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting. Future Generation Computer Systems, 139, 100–108.

Fonda, H. (2020). Klasifikasi Batik Riau Dengan Menggunakan Convolutional Neural Networks (Cnn). Jurnal Ilmu Komputer, 9(1), 7–10.

Garrido, A. (2010). Mathematics and artificial intelligence, two branches of the same tree. Procedia - Social and Behavioral Sciences, 2(2), 1133–1136.

Hindarto, D., & Santoso, H. (2019). Plat Nomor Kendaraan Dengan Metode Convolutional Neural Network. Jurnal Inovasi Informatika Universitas Pradita, September 2021, 1–12.

Mavromatis, I., Stanoev, A., Carnelli, P., Jin, Y., Sooriyabandara, M., & Khan, A. (2022). A dataset of images of public streetlights with operational monitoring using computer vision techniques. Data in Brief, 45, 108658.

Nugroho, B., & Puspaningrum, E. Y. (2021). Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(3), 533.

Özkan, A. (2022). An artificial neural network model in predicting VTEC over central Anatolia in Turkey. Geodesy and Geodynamics, xxxx.

Saldarriaga, J. F. (2022). Heliyon Application of an arti fi cial neural networks for predicting the heat transfer in conical spouted bed using the Nusselt module. Heliyon, 8(June), e11611.

Seawram, S., Nimmanterdwong, P., Sema, T., Piemjaiswang, R., & Chalermsinsuwan, B. (2022). Specific heat capacity prediction of hybrid nanofluid using artificial neural network and its heat transfer application. Energy Reports, 8, 8–15.

Suryawanshi, Y., Patil, K., & Chumchu, P. (2022). VegNet: Dataset of vegetable quality images for machine learning applications. Data in Brief, 45, 108657.

Sze, E., Hindarto, D., & Wirayasa, I. K. A. (2022). Performance Comparison of Ultrasonic Sensor Accuracy in Measuring Distance. 7(4), 2556–2562.

Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.

Walle, M., Eggemann, D., Atkins, P. R., Kendall, J. J., Stock, K., Müller, R., & Collins, C. J. (2023). Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks. Bone, 166(June 2022), 116607.

Wu, J. (2017). Introduction to Convolutional Neural Networks. Introduction to Convolutional Neural Networks, 1–31.

Wulandari, I., Yasin, H., & Widiharih, T. (2020). Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (Cnn). Jurnal Gaussian, 9(3), 273–282.


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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, 7(1), 247-255.

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