Deteksi Jenis Rempah-Rempah Menggunakan Metode Convolutional Neural Network Secara Real Time

Mellynia Sanjaya(1*), Eddy Nurraharjo(2),

(1) Universitas Stikubank Semarang, Indonesia
(2) Universitas Stikubank Semarang, Indonesia
(*) Corresponding Author

Abstract


Spices are one of the products of natural wealth owned by Indonesia which has been recognized by the world. Spices are part of plants that have benefits not only as a complement to spices in cooking, but are also used to boost the immune system of living things. Due to the many types of spices that exist in Indonesia, a system is needed to help the community, especially millennial children, to know the various types of spices correctly and clearly. The spice detection process is carried out using an accuracy calculation on the system using a dataset in the form of 1800 images and 12 types of spices. The classification process for spices is generated using the Convolutional Neural Network (CNN) method with the tensorflow module used for the training and testing process of data. The results of the accuracy experiment on spices resulted in an accuracy value of recognizing the type of spice of 60%.

Full Text:

PDF

References


Tanuwijaya, Evan, and Angelica Roseanne. "Modifikasi Arsitektur VGG16 untuk Klasifikasi Citra Digital Rempah Rempah Indonesia." (2021).

Kaharruddin, Kaharruddin, Kusrini Kusrini, and Emha Taufiq Luthfi. "Klasifikasi Jenis Rempah-Rempah Berdasarkan Fitur Warna Rgb Dan Tekstur Menggunakan Algoritma K-Nearest Neighbor." Informasi Interaktif 4.1 (2019): 17-22.

Hasma, Yunita Aulia, and Widya Silfianti. "Implementasi Deep Learning Menggunakan Framework Tensorflow Dengan Metode Faster Regional Convolutional Neural Network Untuk Pendeteksian Jerawat." Jurnal Ilmiah Teknologi Dan Rekayasa 23.2 (2020): 89-102.

Prastika, Indah Widhi, and Eri Zuliarso. "Deteksi penyakit kulit wajah menggunakan tensorflow dengan metode convolutional neural network." Jurnal Manajemen Informatika dan Sistem Informasi 4.2 (2021): 84-91.

Hutauruk, Junita Sri Wisna, Tekad Matulatan, and Nurul Hayaty. "Deteksi kendaraan secara real time menggunakan metode YOLO berbasis android." Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan 9.1 (2020): 8-14.

Putra, I. W. S. E. Klasifikasi citra menggunakan convolutional neural network (CNN) pada caltech 101. Diss. Institut Teknologi Sepuluh Nopember, 2016.

Nugroho, Pulung Adi, Indah Fenriana, and Rudy Arijanto. "Implementasi Deep Learning Menggunakan Convolutional Neural Network (Cnn) Pada Ekspresi Manusia." Algor 2.1 (2020): 12-20.

Rozaqi, Abdul Jalil, Andi Sunyoto, and M. rudyanto Arief. "Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network." Creative Information Technology Journal 8.1 (2021): 22-31.

Prastika, Indah Widhi, and Eri Zuliarso. "Deteksi penyakit kulit wajah menggunakan tensorflow dengan metode convolutional neural network." Jurnal Manajemen Informatika dan Sistem Informasi 4.2 (2021): 84-91.

Choirunisa, Nadia Azahro, Tita Karlita, and Rengga Asmara. "Deteksi Ras Kucing Menggunakan Compound Model Scaling Convolutional Neural Network." Technomedia Journal 6.2 Februari (2022): 236-251.




DOI: http://dx.doi.org/10.30645/j-sakti.v7i1.567

Refbacks

  • There are currently no refbacks.


J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats