Klasifikasi Image Untuk Jenis Buku Bacaan Anak-Anak dengan Menggunakan Convolutional Neural Network

Sri Winiarti; Cendani Wukir; Ulaya Ahdiani; Taufiq Ismail

doi:10.30865/mib.v6i2.3504

Authors

Sri Winiarti Universitas Ahmad Dahlan, Yogyakarta
Cendani Wukir Universitas Ahmad Dahlan, Yogyakarta
Ulaya Ahdiani Universitas Ahmad Dahlan, Yogyakarta
Taufiq Ismail Universitas Ahmad Dahlan, Yogyakarta

DOI:

https://doi.org/10.30865/mib.v6i2.3504

Keywords:

Children's Reading Book, Deep Learning, Convolutional Neural Network, Confusion Matrix

Abstract

This study was made to classify the types of children's reading books based image on the on the cover. The types of books used in this research are fairy tales, educational books and comics. The problem that occurs is that there are many types of reading books so that there are no errors in identifying children's literature, then classification is carried out. Uses approximately 1002 image data. This study uses the method Convolutional Neural Network (CNN). The purpose of this study was to classify the types of children's reading books to suit the age of the reader. It is hoped that the research objectives can make it easier for parents to find books that are appropriate for their child's age. Convolutional Neural Network (CNN) is a method deep learning that is usually used to process data in the form of images. The research stages start from literature study, data collection, data processing, needs analysis, design, implementation and testing. The collection uses several methods, namely: literature study, documentation method, interview and questionnaire method. The design is carried out from input image data, followed by calculating the accuracy and classification process which results in image classification. Implementation using the Python programming language. Evaluation of the performance of testing the accuracy value using the confusion matrix. The result of the research is a system that can classify the types of children's reading books using 70% training data and 30% test data. With an accuracy rate above 80%.

References

M. M. Syaâ€™bani and R. Umilasari, "Penerapan Metode Cosine Similarity Dan Pembobotan TF / IDF Pada Sistem Klasifikasi Sinopsis Buku Di Perpustakaan Kejaksaan Negeri Jember," Justindo( Jurnal Sistem & Teknologi Indonesia, vol. 3, p. 31â€“42, 2018.

M. Habibi and P. W. Cahyo, "Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine.â€," JISKA (Jurnal Informatika Sunan Kalijaga), vol. 4, p. 48, 2020.

N. d. Piska , A. P. Wibawa, I. A. E. Zaeni and A. Nafalski, "Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal," International Journal of Artificial Intelligence Research, vol. 3, 2019.

I. B. Vicky, E. Utami and A. Sunyoto, "Klasifikasi Jenis Buku Berdasarkan Judul Dan Sinopsis Menggunakan Metode NaÃƒÂ¯ve Bayes Classifier (Studi Kasus : STMIK Kadiri).," Sisfo, vol. 5, 2015.

E. Purwanto, "Konsumsi Anak dalam Teks Sastra di Sekolah," in Konferensi Internasional Kesusasteraan XIX HISKI, 2008.

B. Nurgiyantoro, Sastra Anak: Pengantar Pemahaman Dunia Anak. Yogyakarta, Gajah Mada University Press, 2016.

R. K. T. Sarumpaet, Pedoman Penelitian Sastra Anak, Jakarta Pusat: Pusat Bahasa Kementerian Pendidikan. Nasional 2010. xvi +262 him., 2010.

L. M. Azizah, S. F. Umayah and F. Fajar, "Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer," Semesta Teknika, vol. 21(2), pp. 230-236., 2018.

E. P. Suartika, Wijaya and Soelaiman, "Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) pada Caltech 101," Jurnal Teknik ITS, p. 65, 2012.

D. H. Hubel, and T. N. Wiesel, "Receptive fields and functional architecture of monkey striate cortex," J Physiol , vol. 195, p. 215â€“243, 1968.

N. Fadlia and R. Kosasih , "Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (CNN).," Jurnal Ilmiah Teknologi dan Rekayasa., 2020.

R. Yamashita, M. Nishio and R. K. Gian, "Convolutional neural networks: an overview and application in radiology," Springer Open, vol. 9, no. Insights into Imaging, p. pages611â€“629, 2018.

T. Hu , D. Zhang and J. Wang, "A meta-analysis of the trait resilience and mental health," Personality and Individual Differences, vol. 76, pp. 18-27, 2015.

I. Wulandari, H. Yasin and T. Widiharih, "Klasifikasi citra digital bumbu dan rempah dengan algoritma convolutional neural network," J. Gaussia, vol. 9, p. 273â€“282, 2020.

B. Xiao, K. Wang, X. Bi, W. Li and J. Han,, "2D-LBP: An Enhanced Local Binary Feature for Texture Image Classification," EEE Trans. Circuits Syst. Video Technol, vol. 29, p. 2796â€“2808, 2019.

Bejiga, M. B.,, Zeggada, A.,, Nouffidj, A., and Melgan, "A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery.," Remote Sensing, vol. 9, no. 2, 2017.

Akhmad Rohim, Yuita Arum Sari and Tibyani, "Convolutional Neural Network (CNN) Untuk Pengklasifikasian Citra Makanan Tradisional," Jurnal Pengembang Teknologi Informasi dan Ilmu Komputer , vol. 3, no. 7, pp. 7037-7042, 2017.

Albelwi, S., and Mahmood, A., "A Framework for Designing the Architectures of Deep Convolutional Neural Networks," Entropy, vol. 19, p. 242, 2017.

S. Winiarti, m. Y. Andi Saputro and Sunardi, "Deep Learning Dalam Mengidentifikasi Jenis Bangunan Heritage Dengan Algoritma Convolutional Neural Network," Budidharma, vol. 3, 2021.

H. G. LEWIS, "A generalized confusion matrix for assessing area estimates from," remote sensing, vol. 22, no. 16, p. 3223â€“3235, 2001.