Klasifikasi Jenis Burung Cendrawasih Menggunakan Convolutional Neural Network (CNN) Berdasarkan Citra
Abstract
Birds of paradise are iconic symbols of Indonesia's biodiversity, especially in Papua, with more than 40 recorded species. Manual classification requires specific expertise and is time-consuming. This study aims to develop an automated classification system for birds of paradise using Convolutional Neural Network (CNN), specifically the MobileNetV2 architecture known for its efficiency in image processing. The dataset used comprises three species: Cicinnurus regius, Paradisaea apoda, and Paradisaea rubra. The preprocessing steps include image augmentation, resizing, and normalization. The training results show an accuracy of 98.49% and validation accuracy of 97.50%. Evaluation using a confusion matrix reveals high accuracy and minimal misclassification. This model shows great potential for use in conservation applications and automatic bird species identification
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References
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Alweendo, M., Hasheela-Mufeti, V., & Mufeti, K. (2021). The development of a bilingual English-Oshikwanyama dictionary app.
Dayat, Abd. R., & Angriani, L. (2020). Perancangan model pengenalan kebudayaan Papua secara interaktif berbasis Android. https://doi.org/10.14421/JISKA.2020.51-06
Deshmukh, R. K., Markandey, S., & Sahu, P. (2018). Mobile application development with Android. International Journal of Advances in Applied Sciences. https://doi.org/10.11591/IJAAS.V7.I4.PP317-321
François, A. (2018). In search of island treasures: Language documentation in the Pacific.
Gi, M. K. (2017). AOSP optimization of internal communication between applications in the Android Open Source Project based.
Goel, M., & Singal, G. (2021). Android OS case study. https://doi.org/10.48550/arxiv.2104.09487
Grigoryeva, E., & Karpova, O. M. (2022). Modern English dictionaries on mobile phones. Vestnik Ivanovskogo Gosudarstvennogo Universiteta. https://doi.org/10.46726/h.2022.4.6
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Hanke, F. R. (2017). Computer supported collaborative language documentation.
Hillner, J. (2022). Application. https://doi.org/10.1007/978-1-4842-8745-3_2
Howard, A. G., Sandler, M., Chu, G., Chen, L. L., Chen, B., Tan, M., Wang, W., & Zhu, Y. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
K.V, C., Sharmila, S. P., & Manjunath, A. S. (2014). Customizing AOSP for different embedded devices. International Conference on Computing for Sustainable Global Development. https://doi.org/10.1109/INDIACOM.2014.6828139
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Mallikarjuna, A., & Madhuri, S. (2013). Unveiling of Android platform.
Pamungkas, S. D. P., Suputra, G., Fadillah, D. A. P., Rista, M. L. A., & Arisandi, S. R. (2023). The urgency of preserving regional languages through local educational curriculum. Foremost Journal. https://doi.org/10.33592/foremost.v4i2.3731
Pravitasari, H., & Wilantika, C. F. (2024). Technology and local language: Development of an Android-based Sundanese-English electronic dictionary for the Baduy community in Indonesia. JIIP (Jurnal Ilmiah Ilmu Pendidikan). https://doi.org/10.54371/jiip.v7i10.6046
Sanglise, M. (2023). Android-based Biak language dictionary application development. Journal of Information Science and Technology. https://doi.org/10.30862/jistech.v12i1.201
Sihite, M. R., & Sibarani, B. (2024). Technology and language revitalization in Indonesia: A literature review of digital tools for preserving endangered languages. https://doi.org/10.55299/ijere.v3i2.988
Sitompul, S. J., Syahputri, V. N., Kesha, C. N., & Aduwina, A. (2024). Peran pemerintah daerah dalam pemeliharaan bahasa daerah melalui pendekatan baru revitalisasi bahasa daerah model B di Kabupaten Nagan Raya. Lingua Susastra. https://doi.org/10.24036/ls.v5i1.231
Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., & Chen, L. (2018). Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, 4577–4586. https://doi.org/10.1109/CVPR.2018.00479
Wali, M., Akbar, R., Iqbal, T., & Al-Bahri, F. P. (2019). Development of an Android-based tourism guide (A case study: Sabang City, Indonesia). International Journal of Scientific & Technology Research.
Wikarsa, L., Suwanto, T. Ch., & Loha, C. H. (2024). Development of a bilingual dictionary of Sahu Tala'i – Indonesia using the Aho-Corasick algorithm. Jurnal Pekommas. https://doi.org/10.56873/jpkm.v9i1.5281
Winarti, S. (2017). Sistem bilangan beberapa bahasa di wilayah Papua, NTT, dan Maluku Utara. https://doi.org/10.26499/RNH.V6I2.450
Wang, X., & Li, Y. (2016). Convolutional neural network-based classification of bird species. Ecological Informatics, 32, 35–45. https://doi.org/10.1016/j.ecoinf.2015.12.006
Yana, A., & Maelani, P. (2024). Hamlet toponymy in Cadasari-Pandeglang sub-district as an effort to increase Sundanese language preservation literacy. KnE Social Sciences. https://doi.org/10.18502/kss.v9i25.16997
Published
2025-04-17
How to Cite
ThaniketR. M., RahanraM. H. J., & DharsonoW. W. (2025). Klasifikasi Jenis Burung Cendrawasih Menggunakan Convolutional Neural Network (CNN) Berdasarkan Citra. Jurnal Ilmiah Sistem Informasi Dan Teknik Informatika (JISTI), 8(1), 103-113. https://doi.org/10.57093/jisti.v8i1.280
Section
Articles
JISTI
