relation: https://eprints.untirta.ac.id/51201/ title: ANALISIS KLASIFIKASI VARIETAS BERAS MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) creator: MAULANA IQBAL, RISYAD subject: TK Electrical engineering. Electronics Nuclear engineering description: Rice Variety Analysis Using the Convolutional Neural Network Method (CNN) Rice is a staple food that is widely consumed by the community. The high level of need is widely used by business actors to commit fraud in the form of mixing. This can harm the community economically and contrary to existing consumer protection regulations. Convolutional Neural Network is a method used for image data identifying image objects. Input rice image measuring 250x250 pixels from 7 varieties with 75:25 split data division. Using google colabs CNN architecture from keras frameworks used are Vgg16Net, ResNet50, InceptionV3, and InceptionResNetV2. The results obtained are InceptionV3 architecture has the best results with an accuracy of 0.985 out of 345 correct predictions, InceptionResNetV2 with an accuracy of 0.982 out of 344 correct predictions, Vgg16Net with an accuracy of 0.98 out of 343 correct predictions, and ResNet50 with an accuracy of 0.957 out of 335 correct predictions. Convolutional Neural Network architecture gets good performance in the process of classifying varieties, with the help of transfer learning stages the use of learning models can be done efficiently and quickly. Word Keys: Rice, Deep Learning, Convolutional Neural Network, Transfer Learning date: 2025-05-28 type: Thesis type: NonPeerReviewed format: text language: id identifier: https://eprints.untirta.ac.id/51201/1/Risyad%20Maulana%20Iqbal%20_3332180024_Full%20Text.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/2/Risyad%20Maulana%20Iqbal_3332180024_01.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/3/Risyad%20Maulana%20Iqbal_3332180024_02.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/4/Risyad%20Maulana%20Iqbal_3332180024_03.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/5/Risyad%20Maulana%20Iqbal_3332180024_04.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/6/Risyad%20Maulana%20Iqbal_3332180024_05.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/7/Risyad%20Maulana%20Iqbal_3332180024_Ref.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/8/Risyad%20Maulana%20Iqbal_3332180024_Lamp.pdf format: text language: id identifier: https://eprints.untirta.ac.id/51201/9/Risyad%20Maulana%20Iqbal_3332180024_CP.pdf identifier: MAULANA IQBAL, RISYAD (2025) ANALISIS KLASIFIKASI VARIETAS BERAS MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.