Optimalisasi Kinerja Convolutional Neural Networks VGG16 dalam Identifikasi Bangunan Adat Melayu
DOI:
https://doi.org/10.32493/jtsi.v8i3.58210Keywords:
Bangunan Adat Melayu; Convolutional Neural Network; Identifikasi; Optimalisasi; VGG16 Model.Abstract
Penggunaan deep learning dalam mendeteksi berbagai objek sudah banyak diterapkan, namun untuk identifikasi kemiripan bangunan untuk gaya arsitektur masih terbatas. Analisis klasifikasi model desain arsitektur bangunan adat Melayu dapat dilakukan dengan menerapkan metode Convolusional Neural Network (CNN). Pendekatan yang digunakan untuk menganalisis klasifikasi dan kemiripan model bangunan adat melayu menggunakan model arsitektur VGG16. Ekstraksi fitur menggunakan model deep learning untuk mengidentifikasi jenis bangunan adat Melayu menggunakan parameter atap, jendela, dan ornamen bangunan. Dataset citra bangunan adat Melayu didapatkan dari pengambilan langsung ke lokasi bangunan adat melayu Riau di Kawasan Jalan Muhammad Arifin Pekanbaru Riau untuk training sebanyak 644 gambar dan testing model sebanyak 106 gambar. Model yang digunakan adalah VGG16. Parameter ukuran kinerja meliputi accuracy, precision, recall, dan F1-score. Akurasi yang didapatkan dalam penelitian ini adalah 98,77% dari total 106 data yang diuji, sedangkan precision 0,8678, recall 0,9633, dan F1-score 0,9877. Hasil yang didapatkan ini melalui setting parameter learning rate 0,0001, drop out 0,20, dan epoch sebesar 25. Secara keseluruhan model VGG16 yang digunakan dalam penelitian ini menghasilkan akurasi yang baik.
References
Abed, M. H., & Hussain, Z. M. (2021). Architectural heritage images classification using deep learning with CNN Architectural Heritage Images Classification Using Deep Learning With CNN. Proceedings of the 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, 1.
Ahdiani, U., Winiarti, S., Pramono, H., & Ismail, T. (2025). Collaboration of Digital Literacy and Artificial Intelligence in Preserving Traditional Malay House Culture. E3S Web of Conferences, 622, 1–8. https://doi.org/10.1051/e3sconf/202562203008
Alsheikh Mahmoud, S., & Bin Hashim, H. (2025). Traditional Malay House Preservation: Guidelines for Structural Evaluation. Buildings, 15(5). https://doi.org/10.3390/buildings15050782
Bashaddadh, O., Omar, N., Mohd, M., & Khalid, M. N. A. (2025). Machine Learning and Deep Learning Approaches for Fake News Detection: A Systematic Review of Techniques, Challenges, and Advancements. IEEE Access, 13(May), 90433–90466. https://doi.org/10.1109/ACCESS.2025.3572051
Devi Yunita, & Maulana Fansyuri. (2025). Analisis Kinerja Algoritma Logistic Regression Dalam Klasifikasi Citra Wajah Berdasarkan Fitur Citra Warna Dan Bentuk. Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 5(2), 48–56. https://doi.org/10.55606/teknik.v5i2.7200
Fawwaz, I., Yennimar, Y., Dharsinni, N. P., & Wijaya, B. A. (2023). The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification. Sinkron, 8(4), 2236–2245. https://doi.org/10.33395/sinkron.v8i4.12893
Fu, D. S., Huang, J., Hazra, D., Dwivedi, A. K., Gupta, S. K., Shivahare, B. D., & Garg, D. (2024). Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture. In PLoS ONE (Vol. 19, Issue 7 July). https://doi.org/10.1371/journal.pone.0303462
Janković, R. (2020). Machine learning models for cultural heritage image classification: Comparison based on attribute selection. Information (Switzerland), 11(1). https://doi.org/10.3390/info11010012
Jiang, Z. P., Liu, Y. Y., Shao, Z. E., & Huang, K. W. (2021). An improved VGG16 model for pneumonia image classification. Applied Sciences (Switzerland), 11(23). https://doi.org/10.3390/app112311185
Kevseroglu, O., & Kurban, R. (2024). Re-exploring the Kayseri Culture Route by Using Deep Learning for Cultural Heritage Image Classification Cultural Heritage Image Classification by Using Deep Learning: Kayseri Culture Route. ACM International Conference Proceeding Series, 196–201. https://doi.org/10.1145/3660853.3660913
Li, H., & Dong, H. (2025). Architectural Style Classification Based on Deep Learning. Computer-Aided Design and Applications, 22, 16–31. https://doi.org/10.14733/cadaps.2025.S1.16-31
Li, S., Wu, C., & Xiong, N. (2022). Hybrid Architecture Based on CNN and Transformer for Strip Steel Surface Defect Classification. Electronics (Switzerland), 11(8), 1–14. https://doi.org/10.3390/electronics11081200
Lydia, A. A., & Chandrasekar, S. (2022). A Comparative Study on Regularization Techniques in Convolutional Neural Networks. International Journal of Research in Engineering and Science (IJRES) ISSN, 10(7), 784–793.
Malhotra, R., & Singh, P. (2023). Recent advances in deep learning models: a systematic literature review. In Multimedia Tools and Applications (Vol. 82, Issue 29). Springer US. https://doi.org/10.1007/s11042-023-15295-z
Mall, P. K., Singh, P. K., Srivastav, S., Narayan, V., Paprzycki, M., Jaworska, T., & Ganzha, M. (2023). A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthcare Analytics, 4(June), 100216. https://doi.org/10.1016/j.health.2023.100216
Maulidi, M. R., Indriani, F., Farmadi, A., Budiman, I., & Kartini, D. (2024). Optimizing South Kalimantan Food Image Classification Through CNN Fine-Tuning. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), 10(4), 897–913. https://doi.org/10.26555/jiteki.v10i4.30325
Mustafid, A., Pamuji, M. M., & Helmiyah, S. (2020). A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification. IJID (International Journal on Informatics for Development), 9(2), 100–110. https://doi.org/10.14421/ijid.2020.09207
Poojary, R., Raina, R., & Mondal, A. K. (2021). Effect of data-augmentation on fine-tuned cnn model performance. IAES International Journal of Artificial Intelligence, 10(1), 84–92. https://doi.org/10.11591/ijai.v10.i1.pp84-92
Shehata, A. M., & Alaboud, N. S. (2025). Identification of Features of Architectural Heritage Using Deep Learning Techniques. Civil Engineering and Architecture, 13(4), 3280–3298. https://doi.org/10.13189/cea.2025.130431
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0197-0
Su, Y. P., Hu, W. Y., Lin, J. W., Chen, Y. C., Sezer, S., & Chen, S. J. (2011). Low power Gm-boosted differential Colpitts VCO. International System on Chip Conference, 247–250. https://doi.org/10.1109/SOCC.2011.6085109
Winiarti, S., Pramono, H., & Pranolo, A. (2022). Application of Artificial Intelligence in Digital Architecture to Identify Traditional Javanese Buildings. Journal of Artificial Intelligence in Architecture, 1(1), 20–29. https://doi.org/10.24002/jarina.v1i1.4916
Yoshimura, Y., Cai, B., Wang, Z., & Ratti, C. (2019). Deep learning architect: Classification for architectural design through the eye of artificial intelligence. Lecture Notes in Geoinformation and Cartography, 249–265. https://doi.org/10.1007/978-3-030-19424-6_14
Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A review of convolutional neural networks in computer vision. In Artificial Intelligence Review (Vol. 57, Issue 4). Springer Netherlands. https://doi.org/10.1007/s10462-024-10721-6
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2025 Sri Winiarti, Sunardi Sunardi, Abdul Fadlil
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License
YOU ARE FREE TO:
- Share - copy and redistribute the material in any medium or format
- Adapt - remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms
_2020_-_7(2)_2024_-_Thumbnail.png)
