Technology-Based Classification of Clerodendrum Paniculatum Using CNN and Confusion Matrix

https://doi.org/10.35912/jatra.v2i1.4598
Published: Jan 5, 2024

Abstract:

Purpose: This study aims to develop a classification system for the Clerodendrum paniculatum plant (Bunga Pagoda), focusing on its key parts—stems, flowers, leaves, and trees—using the Convolutional Neural Network (CNN) algorithm. The objective is to support conservation efforts and facilitate digital data grouping through technology-based classification.

Methodology: The research involved collecting a dataset of images representing different parts of the Clerodendrum paniculatum plant. These images were then used to train a CNN model. The training process included 200 epochs to optimize performance. The model's accuracy and performance were evaluated using a confusion matrix to measure classification success across the plant's various parts.

Results: The CNN model achieved its highest accuracy of 97.78% when trained for 200 epochs. The results indicated a significant improvement in evaluation metrics compared to models trained with fewer epochs. The mo del successfully classified the plant parts with high precision, demonstrating its robustness and reliability for rare plant classification.

Conclusions: This study confirms that the CNN algorithm is effective in classifying the parts of the Clerodendrum paniculatum plant. Increasing the number of training epochs substantially enhances the model's performance, making it a practical tool for digital plant conservation initiatives.

Limitations: The study is limited by its reliance on a specific dataset, which may not encompass all possible variations of the Clerodendrum paniculatum plant under different environmental conditions.

Contributions: This research contributes to digital plant conservation by developing a CNN-based classification system for rare plants. It highlights the importance of deep learning in biodiversity preservation and provides a foundation for future AI-driven botanical studies.

Keywords:
1. Clerodendrum Paniculatum
2. Convolutional Neural Network (CNN)
3. Plant Clasification
4. Tropical Forest
Authors:
1 . Pandu Wijaya
2 . Alvin Reihansyah Makarim
3 . Meizano Ardhi Muhammad
4 . Cela Febriyani
5 . Vezan Hidayatullah
6 . Resty Annisa
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How to Cite
Wijaya, P. ., Makarim, A. R. ., Muhammad, M. A. ., Febriyani, C. ., Hidayatullah, V. ., & Annisa, R. . (2024). Technology-Based Classification of Clerodendrum Paniculatum Using CNN and Confusion Matrix. Jurnal Teknologi Riset Terapan, 2(1), 27–36. https://doi.org/10.35912/jatra.v2i1.4598
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Issue & Section
Vol. 2 No. 1 (2024): Januari
Articles
References

    Abade, A. S., Ferreira, P. A., & Vidal, F. de B. (2020). Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (No. arXiv:2009.04365). arXiv. http://arxiv.org/abs/2009.04365

    Bijalwan, V., Semwal, V. B., & Gupta, V. (2022). Wearable sensor-based pattern mining for human activity recognition: Deep learning approach. Industrial Robot: The International Journal of Robotics Research and Application, 49(1), 21–33. Https://doi.org/10.1108/IR-09-2020-0187

    Darmatasia & A. Muhammad Syafar. (2023). IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN RIMPANG SECARA VIRTUAL. Jurnal Instek, 8(1).

    Hartono, A., Adlini, M. N., Ritonga, Y. E., Tambunan, M. I. H., Nasution, M. S., & Jumiah, J. (2020). IDENTIFIKASI TUMBUHAN TINGKAT TINGGI (PHANEROGAMAE) DI KAMPUS II UINSU. Jurnal Biolokus, 3(2), 305. Https://doi.org/10.30821/biolokus.v3i2.755

    Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., Spitzer, A. I., & Ramkumar, P. N. (2020). Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Current Reviews in Musculoskeletal Medicine, 13(1), 69–76. Https://doi.org/10.1007/s12178-020-09600-8

    Khan, A. I., & Al-Habsi, S. (2020). Machine Learning in Computer Vision. Procedia Computer Science, 167, 1444–1451. Https://doi.org/10.1016/j.procs.2020.03.355

    Khorram, Asghar, Vahidi, Bahman, & Ahmadian, Bahram. (2020). Computational analysis of adhesion between a cancer cell and a white blood cell in a bifurcated microvessel. Computer Methods and Programs in Biomedicine, 186, 105195.

    Krichen, M. (2023). Convolutional Neural Networks: A Survey. Computers, 12(8), 151. Https://doi.org/10.3390/computers12080151

    Mardiono, D. A., Nanra, S., & Rican, D. (2023). Rancang Bangun Pengaman Pintu Menggunakan RFID Dengan Mikrokontroler Atmega 328. doi:https://doi.org/10.35912/jatra.v1i1.1872

    Melvi, M., Nurhayati, N., Batubara, M. A. M., Septama, H. D., & Ulvan, A. (2023). Unjuk Kerja Teknologi Akses Jamak TD-CDMA dan TD-SCDMA pada Infrastruktur Jaringan High Altitude Platform Stations. Jurnal Teknologi Riset Terapan, 1(1), 51-59. doi:10.35912/jatra.v1i1.1790

    Melvi, M., Ulvan, A., Sidiq, M. R., & Batubara, M. A. M. (2023). Rancang Bangun Sistem Monitoring Ketinggian Muka Air Laut Menggunakan Arduino Pro Mini dan NodeMCU ESP8266. doi:https://doi.org/10.35912/jatra.v1i1.1794

    Mohammad Mustafa Taye. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. MDPI, 11(3), 52. Https://doi.org/10.3390/computation11030052

    Nanni, Loris, Maguolo, Gianluca, & Pancino, Fabio. (2020). Insect pest image detection and recognition based on bio-inspired methods. Ecological Informatics, 57, 101089. doi:https://doi.org/10.1016/j.ecoinf.2020.101089

    Nona, R., Ramadhani, F., & Andrea, R. (2024). Development “Sincatensa”, a Plant Sensing System Application in Land Dry Areas Using Qr-Code. TEPIAN, 5(1), 30–34. Https://doi.org/10.51967/tepian.v5i1.3023

    Penumuru, D. P., Muthuswamy, S., & Karumbu, P. (2020). Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. Journal of Intelligent Manufacturing, 31(5), 1229–1241. Https://doi.org/10.1007/s10845-019-01508-6

    Pornpanomchai, C., & Pornpanomchai, V. (2021). Plant Leaf Image Recognition Based on Convolutional Neural Network. 37(2), 79–92.

    Pratiwi, H. A., Cahyanti, M., & Lamsani, M. (2021). IMPLEMENTASI DEEP LEARNING FLOWER SCANNER MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. Sebatik, 25(1), 124–130. Https://doi.org/10.46984/sebatik.v25i1.1297

    Riesna, D. M. R., Pujianto, D. E., Efendi, A. J. I., Nugroho, B. A., & Saputra, D. I. S. (2023). Identifikasi Platform dan Faktor Sukses dalam Manajemen Proyek Teknologi Informasi. Jurnal Teknologi Riset Terapan, 1(1), 1-9. doi:10.35912/jatra.v1i1.1458

    Sudeepa, KB, Aithal, Ganesh, Rajinikanth, V, & Satapathy, Suresh Chandra. (2020). Genetic algorithm based key sequence generation for cipher system. Pattern Recognition Letters, 133, 341-348. doi:https://doi.org/10.1016/j.patrec.2020.03.015

    Tama, A. M., & Santi, R. C. N. (2023). Klasifikasi Jenis Tanaman Hias Menggunakan Metode Convolutional Neural Network (CNN). INTECOMS: Journal of Information Technology and Computer Science, 6(2), 764–770. Https://doi.org/10.31539/intecoms.v6i2.7002

    Weny.J.A Musa. (2017). ISOLASI SENYAWA ANTIFEEDANT DARI TUMBUHAN CLERODENDRUM PANICULATUM. ZAHIR publishing.

    Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 79–91. Https://doi.org/10.18653/v1/2020.eval4nlp-1.9

    Yando, J. R., Panusunan, P., & Fauzan, F. (2023). Penggunaan Filler Tanah (Silt) sebagai Perencanaan Campuran Aspal Beton AC-WC. doi:https://doi.org/10.35912/jatra.v1i1.1873

    Zhou, Zheng, Majeed, Yaqoob, Naranjo, Geraldine Diverres, & Gambacorta, Elena MT. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers and Electronics in Agriculture, 182, 106019. doi:https://doi.org/10.1016/j.compag.2021.106019

    Zhu, Yueming, Abdalla, Alwaseela, Tang, Zheng, & Cen, Haiyan. (2022). Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning. Biosystems Engineering, 219, 165-176. doi:https://doi.org/10.1016/j.biosystemseng.2022.05.001

  1. Abade, A. S., Ferreira, P. A., & Vidal, F. de B. (2020). Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (No. arXiv:2009.04365). arXiv. http://arxiv.org/abs/2009.04365
  2. Bijalwan, V., Semwal, V. B., & Gupta, V. (2022). Wearable sensor-based pattern mining for human activity recognition: Deep learning approach. Industrial Robot: The International Journal of Robotics Research and Application, 49(1), 21–33. Https://doi.org/10.1108/IR-09-2020-0187
  3. Darmatasia & A. Muhammad Syafar. (2023). IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN RIMPANG SECARA VIRTUAL. Jurnal Instek, 8(1).
  4. Hartono, A., Adlini, M. N., Ritonga, Y. E., Tambunan, M. I. H., Nasution, M. S., & Jumiah, J. (2020). IDENTIFIKASI TUMBUHAN TINGKAT TINGGI (PHANEROGAMAE) DI KAMPUS II UINSU. Jurnal Biolokus, 3(2), 305. Https://doi.org/10.30821/biolokus.v3i2.755
  5. Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., Spitzer, A. I., & Ramkumar, P. N. (2020). Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Current Reviews in Musculoskeletal Medicine, 13(1), 69–76. Https://doi.org/10.1007/s12178-020-09600-8
  6. Khan, A. I., & Al-Habsi, S. (2020). Machine Learning in Computer Vision. Procedia Computer Science, 167, 1444–1451. Https://doi.org/10.1016/j.procs.2020.03.355
  7. Khorram, Asghar, Vahidi, Bahman, & Ahmadian, Bahram. (2020). Computational analysis of adhesion between a cancer cell and a white blood cell in a bifurcated microvessel. Computer Methods and Programs in Biomedicine, 186, 105195.
  8. Krichen, M. (2023). Convolutional Neural Networks: A Survey. Computers, 12(8), 151. Https://doi.org/10.3390/computers12080151
  9. Mardiono, D. A., Nanra, S., & Rican, D. (2023). Rancang Bangun Pengaman Pintu Menggunakan RFID Dengan Mikrokontroler Atmega 328. doi:https://doi.org/10.35912/jatra.v1i1.1872
  10. Melvi, M., Nurhayati, N., Batubara, M. A. M., Septama, H. D., & Ulvan, A. (2023). Unjuk Kerja Teknologi Akses Jamak TD-CDMA dan TD-SCDMA pada Infrastruktur Jaringan High Altitude Platform Stations. Jurnal Teknologi Riset Terapan, 1(1), 51-59. doi:10.35912/jatra.v1i1.1790
  11. Melvi, M., Ulvan, A., Sidiq, M. R., & Batubara, M. A. M. (2023). Rancang Bangun Sistem Monitoring Ketinggian Muka Air Laut Menggunakan Arduino Pro Mini dan NodeMCU ESP8266. doi:https://doi.org/10.35912/jatra.v1i1.1794
  12. Mohammad Mustafa Taye. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. MDPI, 11(3), 52. Https://doi.org/10.3390/computation11030052
  13. Nanni, Loris, Maguolo, Gianluca, & Pancino, Fabio. (2020). Insect pest image detection and recognition based on bio-inspired methods. Ecological Informatics, 57, 101089. doi:https://doi.org/10.1016/j.ecoinf.2020.101089
  14. Nona, R., Ramadhani, F., & Andrea, R. (2024). Development “Sincatensa”, a Plant Sensing System Application in Land Dry Areas Using Qr-Code. TEPIAN, 5(1), 30–34. Https://doi.org/10.51967/tepian.v5i1.3023
  15. Penumuru, D. P., Muthuswamy, S., & Karumbu, P. (2020). Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. Journal of Intelligent Manufacturing, 31(5), 1229–1241. Https://doi.org/10.1007/s10845-019-01508-6
  16. Pornpanomchai, C., & Pornpanomchai, V. (2021). Plant Leaf Image Recognition Based on Convolutional Neural Network. 37(2), 79–92.
  17. Pratiwi, H. A., Cahyanti, M., & Lamsani, M. (2021). IMPLEMENTASI DEEP LEARNING FLOWER SCANNER MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. Sebatik, 25(1), 124–130. Https://doi.org/10.46984/sebatik.v25i1.1297
  18. Riesna, D. M. R., Pujianto, D. E., Efendi, A. J. I., Nugroho, B. A., & Saputra, D. I. S. (2023). Identifikasi Platform dan Faktor Sukses dalam Manajemen Proyek Teknologi Informasi. Jurnal Teknologi Riset Terapan, 1(1), 1-9. doi:10.35912/jatra.v1i1.1458
  19. Sudeepa, KB, Aithal, Ganesh, Rajinikanth, V, & Satapathy, Suresh Chandra. (2020). Genetic algorithm based key sequence generation for cipher system. Pattern Recognition Letters, 133, 341-348. doi:https://doi.org/10.1016/j.patrec.2020.03.015
  20. Tama, A. M., & Santi, R. C. N. (2023). Klasifikasi Jenis Tanaman Hias Menggunakan Metode Convolutional Neural Network (CNN). INTECOMS: Journal of Information Technology and Computer Science, 6(2), 764–770. Https://doi.org/10.31539/intecoms.v6i2.7002
  21. Weny.J.A Musa. (2017). ISOLASI SENYAWA ANTIFEEDANT DARI TUMBUHAN CLERODENDRUM PANICULATUM. ZAHIR publishing.
  22. Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 79–91. Https://doi.org/10.18653/v1/2020.eval4nlp-1.9
  23. Yando, J. R., Panusunan, P., & Fauzan, F. (2023). Penggunaan Filler Tanah (Silt) sebagai Perencanaan Campuran Aspal Beton AC-WC. doi:https://doi.org/10.35912/jatra.v1i1.1873
  24. Zhou, Zheng, Majeed, Yaqoob, Naranjo, Geraldine Diverres, & Gambacorta, Elena MT. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers and Electronics in Agriculture, 182, 106019. doi:https://doi.org/10.1016/j.compag.2021.106019
  25. Zhu, Yueming, Abdalla, Alwaseela, Tang, Zheng, & Cen, Haiyan. (2022). Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning. Biosystems Engineering, 219, 165-176. doi:https://doi.org/10.1016/j.biosystemseng.2022.05.001