Fruit Zone : Media Pembelajaran Interaktif Pengenalan Buah Anak Kelompok Belajar Menggunakan ResNet18

Siti Ingefatul Komariah; Desti Fitri Aisyah Putri; Siska Yulia Rahmawati; Zilvanhisna Emka Fitri; Ery Setiyawan Jullev Atmadji; Reski Yulina Widiastuti; Arizal Mujibtamala Nanda Imron

doi:10.30998/faktorexacta.v17i1.21101

Fruit Zone : Media Pembelajaran Interaktif Pengenalan Buah Anak Kelompok Belajar Menggunakan ResNet18

Siti Ingefatul Komariah⁽¹⁾, Desti Fitri Aisyah Putri⁽²⁾, Siska Yulia Rahmawati⁽³⁾, Zilvanhisna Emka Fitri^(4*), Ery Setiyawan Jullev Atmadji⁽⁵⁾, Reski Yulina Widiastuti⁽⁶⁾, Arizal Mujibtamala Nanda Imron⁽⁷⁾

(1)
(2) Politeknik Negeri Jember
(3) Politeknik Negeri Jember
(4) Politeknik Negeri Jember
(5) Politeknik Negeri Jember
(6) Universitas Jember
(7) Universitas Jember
(*) Corresponding Author

Abstract

Learning media is very important in supporting learning activities in early childhood. Limited learning media and learning methods that are still centered on the ability and experience of teachers are an obstacle to improving learning at Pos Alamanda 105 Jumerto, Jember. An interactive, cheap, easy and accessible learning media is needed to improve students' abilities, especially in fruit recognition using both Indonesian and English. The solution, researchers used Deep Learning method for interactive learning media of fruit introduction in early childhood. The method used is Convolutional Neural Network with Resnet18 architecture. This research uses 21 types of popular fruits and unique fruits equipped with voice features in Indonesian and English. The total data of 2100 fruit images with a learning rate of 0.0002 and a maximum epoch of 100 wereable to classify the fruit with an accuracy rate of 96% (system training) and 95% (system testing).

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References

I. Magdalena, A. Fatakhatus Shodikoh, A. R. Pebrianti, A. W. Jannah, I. Susilawati, and U. M. Tangerang, “Pentingnya Media Pembelajaran Untuk Meningkatkan Minat Belajar Siswa Sdn Meruya Selatan 06 Pagi,” Ed. J. Edukasi Dan Sains, vol. 3, no. 2, pp. 312–325, 2021.

U. Sulaiman, N. Ardianti, and S. Selviana, “Tingkat Pencapaian Pada Aspek Perkembangan Anak Usia Dini 5-6 Tahun Berdasarkan Strandar Nasional Pendidikan Anak Usia Dini,” NANAEKE Indones. J. Early Child. Educ., vol. 2, no. 1, p. 52, 2019, doi: 10.24252/nananeke.v2i1.9385.

Maghfiroh and D. Shofia Suryana, “Pembelajaran di Pendidikan Anak Usia Dini,” J. Pendidik. Tambusai, vol. 05, no. 01, p. 1561, 2021.

Z. E. Fitri et al., “Attractive Learning Media for Introduction to Popular Fruits Using Computer Vision,” pp. 14–19, 2022.

I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (Cnn),” J. Gaussian, vol. 9, no. 3, pp. 273–282, 2020, doi: 10.14710/j.gauss.v9i3.27416.

N. Na’imah, “Urgensi Bahasa Inggris Dikembangkan Sejak Anak Usia Dini,” J. Obsesi J. Pendidik. Anak Usia Dini, vol. 6, no. 4, pp. 2564–2572, 2022, doi: 10.31004/obsesi.v6i4.1916.

A. P. Wulandari, A. A. Salsabila, K. Cahyani, T. S. Nurazizah, and Z. Ulfiah, “Pentingnya Media Pembelajaran dalam Proses Belajar Mengajar,” J. Educ., vol. 5, no. 2, pp. 3928–3936, 2023, doi: 10.31004/joe.v5i2.1074.

F. M. Qotrunnada and P. H. Utomo, “Metode Convolutional Neural Network untuk Klasifikasi Wajah Bermasker,” Prisma, vol. 5, pp. 799–807, 2022.

E. N. Arrofiqoh and H. Harintaka, “Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi,” Geomatika, vol. 24, no. 2, p. 61, 2018, doi: 10.24895/jig.2018.24-2.810.

U. Muhamammadiyah Jember, I. Rizki Ramadhani, and A. Nilogiri, “Klasifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network Classification Of Plants Based On Leaf Image Using Convolutional Neural Network Method,” J. Smart Teknol., vol. 3, no. 3, pp. 2774–1702, 2022.

A. Hibatullah and I. Maliki, “Penerapan Metode Convolutional Neural Network Pada Pengenalan Pola Citra Sandi Rumput,” J. Inform. Comput. Sci., vol. 1, no. 02, pp. 1–8, 2019.

Bambang Pilu Hartato, “Penerapan Convolutional Neural Network pada Citra Rontgen Paru-Paru untuk Deteksi SARS-CoV-2,” J. RESTI Rekayasa Sist. Dan Teknol. Inf., vol. 5, no. 4, pp. 747–759, 2021, doi: 10.29207/resti.v5i4.3153.

I. W. Suartika E. P, “Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) Pada Caltech 101Dewa, C. K., Fadhilah, A. L., & Afiahayati, A. (2018). Convolutional Neural Networks for Handwritten Javanese Character Recognition. IJCCS (Indonesian Journal of Computing an,” J. Tek. ITS, vol. 5, no. 1, p. 76, 2016.

N. Thongpance, P. Dangyai, K. Roongprasert, and A. Wongkamhang, “Exploring ResNet-18 Estimation Design through Multiple Implementation Iterations and Techniques in Legacy Databases,” vol. 4, no. 5, pp. 650–661, 2023, doi: 10.18196/jrc.v4i5.19589.

E. Elvin and C. Lubis, “Klasifikasi Citra Ikan Menggunakan Convolutional Neural Network,” J. Ilmu Komput. Dan Sist. Inf., vol. 10, no. 1, 2022, doi: 10.24912/jiksi.v10i1.17827.

DOI: http://dx.doi.org/10.30998/faktorexacta.v17i1.21101