APPLICATION OF VGG16 ARCHITECTURE IN WOOD TYPE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Nurul Anggun Afiah; Syahrullah Syahrullah; Rizka Ardiansyah; Rahmah Laila; Rinianty Pohontu

doi:10.52436/1.jutif.2025.6.1.3874

Nurul Anggun Afiah Informatics Engineering, Faculty of Engineering, Universitas Tadulako, Indonesia
Syahrullah Information System, Faculty of Engineering, Universitas Tadulako, Indonesia
Rizka Ardiansyah Informatics Engineering, Faculty of Engineering, Universitas Tadulako, Indonesia
Rahmah Laila Informatics Engineering, Faculty of Engineering, Universitas Tadulako, Indonesia
Rinianty Pohontu Informatics Engineering, Faculty of Engineering, Universitas Tadulako, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.3874

Keywords: Clasification, Convolutional Neural Network, Image, VGG-16, wood Types

Abstract

Wood is an important natural resource in construction and the furniture industry, with various types possessing unique characteristics. The selection of wood types is often done manually, which is prone to errors that can negatively impact the working process, product quality, and the sustainability of the forests that source the wood. Therefore, this research aims to improve classification accuracy through the application of technology. This study utilizes Convolutional Neural Network (CNN) with the VGG16 architecture to process images in analyzing the visual characteristics of wood, with the goal of building a model capable of classifying wood types based on images. The dataset used consists of 1,584 samples of wood images sourced from Kaggle. Four models were tested with variations in the training and validation data splits, as well as the use of Adam and Adamax optimizers, over 100 epochs. Model 1 achieved a training accuracy of 96.68% and a testing accuracy of 98.10%. Model 2, with a training accuracy of 99.47% and a testing accuracy of 98.41%, showed the best performance. Models 3 and 4 also yielded testing accuracies of 97.46% and 97.78%, respectively. The results of this study indicate that the application of CNN with the VGG16 architecture can enhance the effectiveness of wood type classification and contribute to more accurate and efficient wood selection practices.

Downloads

Download data is not yet available.

References

B. Klaranita, G. Septilaila, I. S. Afifah, and M. Rahmayu, “Sistem Pendukung Keputusan Dalam Memilih Bahan Furniture Terbaik Menggunakan Metode Simple Addative Weighting (SAW),” Jurnal Teknik Informatika, vol. 7, no. 2, pp. 74–78, 2021.

Neneng, N. Putri, and E. R. Susanto, “Klasifikasi Jenis Kayu Menggunakan Support Vector Machine Berdasarkan Ciri Tekstur Local Binary Pattern,” CYBERNETICS, vol. 4, no. 02, pp. 93–100, 2020.

E. Y. Prastowo, “Pengenalan Jenis Kayu Berdasarkan Citra Makroskopik Menggunakan Metode Convolutional Neural Network,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 2, pp. 489–497, Aug. 2021, doi: 10.28932/jutisi.v7i2.3706.

H. Aszahrah, S. Anraeni, and H. Darwis, “Penerapan Metode K-Nearest Neighbour untuk Mengindentifikasi Jenis Kayu Sebagai Bahan Furniture,” Buletin Sistem Informasi dan Teknologi Islam , vol. 3, no. 4, pp. 284–292, 2022.

R. R. Waliyansyah and C. Fitriyah, “Perbandingan Akurasi Klasifikasi Citra Kayu Jati Menggunakan Metode Naive Bayes dan k-Nearest Neighbor (k-NN),” JEPIN (Jurnal Edukasi dan Penelitian Informatika) , vol. 5, no. 2, pp. 157–163, 2019.

A. Putri, R. Soekarta, and I. Amri, “Sistem pendukung keputusan pemilihan beberapa jenis kayu untuk kerajinan meuble dengan metode simple additive weighting,” Framework : Jurnal Ilmu Komputer dan Informatika, vol. 01, no. 02, pp. 156–161, 2023.

I. Romli and A. Turmudi, “Penentuan Jadwal Overtime Dengan Klasifikasi Data Karyawan Menggunakan Algoritma C4.5,” Jurnal Sains Komputer & Informatika (J-SAKTI), vol. 4, no. 2, pp. 694–702, 2020.

N. R. Sari and Y. Mar’atullatifah, “Penerapan Multilayer Perceptron untuk Identifikasi Kanker Payudara,” Jurnal Cakrawala Ilmiah, vol. 2, no. 8, pp. 3261–3268, 2023.

I. B. Trisno and M. A. Raharja, “Webinar Artificial Intelligence dan Machine Learning,” JPM Jurnal Pengabdian Mandiri, vol. 2, no. 11, pp. 2307–2315, 2023.

E. Hermawan, “Klasifikasi Pengenalan Wajah Menggunakan Masker atau Tidak Dengan Mengimplementasikan Metode CNN ( Convolutional Neural Network ),” JURNAL INDUSTRI KREATIF DAN INFORMATIKA SERIES (JIKIS), vol. 1, no. 1, pp. 33–43, 2021.

R. A. Imaduddin and T. N. Suharsono, “Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Citra Jamur Berbasis Mobile,” Innovative: Journal Of Social Science Research, vol. 3, no. 5, pp. 864–875, 2023.

F. Ramadhani, A. Satria, and Salamah, “Implementasi Algoritma Convolutional Neural Network dalam Mengidentifikasi Dini Penyakit pada Mata Katarak,” sudo Jurnal Teknik Informatika, vol. 2, no. 4, pp. 167–175, Dec. 2023, doi: 10.56211/sudo.v2i4.408.

M. F. Naufal, “Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) , pp. 311–318, 2021, doi: 10.25126/jtiik.202184553.

U. Ungkawa and G. Al Hakim, “Klasifikasi Warna pada Kematangan Buah Kopi Kuning menggunakan Metode CNN Inception V3,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 11, no. 3, pp. 731–743, Jul. 2023, doi: 10.26760/elkomika.v11i3.731.

R. Soekarta, N. Nurdjan, and A. Syah, “Klasifikasi Penyakit Tanaman Tomat Menggunakan Metode Convolutional Neural Network (CNN),” INSECT (Informatics and Security), vol. 8, no. 2, pp. 143–151, 2023.

N. M. Y. D. Rahayu, M. W. Antara Kesiman, and I. G. A. Gunadi, “Identifikasi Jenis Kayu Berdasarkan Fitur Tekstur Local Binary Pattern Menggunakan Metode Learning Vector Quantization,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 10, no. 3, pp. 157–166, Dec. 2021, doi: 10.23887/janapati.v10i3.40804.

W. Bismi and M. Qomaruddin, “Klasifikasi Citra Genus Panthera Menggunakan Pendekatan Deep learning Berbasis Convolutional Neural Network (CNN),” Jurnal Informatika dan Rekayasa Perangkat Lunak , vol. 5, no. 2, pp. 172–179, 2023.

A. Prayoga, Maimunah, P. Sukmasetya, M. R. A. Yudianto, and R. Abul Hasani, “Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta,” Journal of Applied Computer Science and Technology, vol. 4, no. 2, pp. 82–89, Nov. 2023, doi: 10.52158/jacost.v4i2.486.

R. A. Tilasefana and R. E. Putra, “View of Penerapan Metode Deep Learning Menggunakan Algoritma CNN Dengan Arsitektur VGG Net Untuk Pengenalan Cuaca,” Journal of Informatics and Computer Science, vol. 5, no. 1, pp. 48–57, 2023.

Q. Yin, R. Zhang, and X. Shao, “CNN and RNN mixed model for image classification,” MATEC Web of Conferences, vol. 277, no. 02001, pp. 1–7, Apr. 2019, doi: 10.1051/matecconf/201927702001.

T. I. Z. M. Putra, Suprapto, and A. F. Bukhori, “Model Klasifikasi Berbasis Multiclass Classification dengan kombinasi indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia,” Jurnal Ilmu Siber dan Teknologi Digital, vol. 1, no. 1, pp. 1–8, 2022, doi: https://doi.org/10.35912/jisted.v1i1.1509.

R. Yotenka and F. F. El Huda, “Implementasi Long Short-Term Memory Pada Harga Saham Perusahaan Perkebunan Di Indonesia,” UJMC, vol. 6, no. 1, pp. 9–18, 2020.

L. Yuwono, M. E. Fadillah, M. Indrayani, W. Maesarah, A. Ramadhan, and S. F. Panjaitan, “Klasifikasi Pendapatan Pedagang Kaki Lima Dan Pelaku Usaha Online Akibat Dampak Covid-19 Menggunakan Metode Naive Bayes,” Bulletin of Applied Industrial Engineering Theory, vol. 2, no. 1, pp. 1–6, 2021.

A. Paleyes, R. G. Urma, and N. D. Lawrence, “Challenges in Deploying Machine Learning: A Survey of Case Studies,” ACM Comput Surv, vol. 55, no. 6, Dec. 2022, doi: 10.1145/3533378.

J. Feriawan and D. Swanjaya, “Perbandingan Arsitektur Visual Geometry Group dan MobileNet Pada Pengenalan Jenis Kayu,” in Seminar Nasional Inovasi Teknologi, UN PGRI Kediri, Jun. 2020.

A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” Journal of Applied Informatics and Computing (JAIC), vol. 4, no. 1, pp. 45–51, Jul. 2020, [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC.