Classification of Rare Mussaenda Species in Indonesia's Tropical Forests Using the CNN Algorithm

https://doi.org/10.35912/jatra.v2i2.5011
Published: Jul 2, 2025

Abstract:

Purpose: Mussaenda frondosa is a rare plant species native to Indonesia’s tropical forests, with limited research focused on its classification and identification, particularly using machine learning. This study aims to develop a classification model for Mussaenda species using a Convolutional Neural Network (CNN) approach to support the advancement of automated plant identification systems.

Methodology/approach: The dataset used consists of 650 labeled images, categorized into six primary parts of the plant: leaves, stems, twigs, fruits, flowers, and trees. A CNN model was developed and trained over 200 epochs to classify the images according to these categories. Preprocessing techniques such as resizing, normalization, and data augmentation were applied to enhance model performance.

Results/findings: The trained CNN model achieved an accuracy of 80%, demonstrating its ability to classify Mussaenda frondosa components despite the relatively small dataset. Visual inspection of prediction outputs showed consistent identification across several categories, particularly leaves and flowers.

Conclusion: The results suggest that CNN can be effectively used to classify rare plant species like Mussaenda frondosa. The model's performance also indicates that even a limited dataset, when properly processed, can yield promising classification results.

Limitations: The main limitation of this research is the small dataset size, which may restrict the model's generalizability to broader plant species or more diverse environmental conditions..

Contribution: This study contributes to the field of plant classification by providing a foundation dataset and a validated CNN model for rare tropical species. It opens pathways for further research in biodiversity monitoring and conservation using AI.

Keywords:
1. Cicarimanah Villag
2. Digital Marketing
3. Product marketing
4. TikTok
5. UMKM
Authors:
1 . H. F. Muchammad Raja
2 . Meizano Ardhi Muhammad
3 . Martinus Martinus
4 . W. Pandu
5 . A. Muhkito
6 . A. Muhammad
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How to Cite
Raja, H. F. M., Muhammad, M. A., Martinus, M., Pandu, W., Muhkito, A., & Muhammad, A. (2025). Classification of Rare Mussaenda Species in Indonesia’s Tropical Forests Using the CNN Algorithm. Jurnal Teknologi Riset Terapan, 2(2), 115–122. https://doi.org/10.35912/jatra.v2i2.5011
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Issue & Section
Vol. 2 No. 2 (2024): Juli
Articles
References

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    Shanthi, S., & Radha, R. (2020). Anti-microbial and phytochemical studies of Mussaenda frondosa Linn. Leaves. Pharmacognosy Journal, 12(3). DOI:10.5530/pj.2020.12.94

    Suciati, H., Simamora, A. W., Panusunan, P., & Fauzan, F. (2023). Analisa Campuran CPHMA terhadap Penambahan Variasi Aspal Penetrasi 60/70 pada Karakteristik Marshall. Jurnal Teknologi Riset Terapan, 1(2), 75-86. doi:10.35912/jatra.v1i2.2294

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  1. Albakia, S. A. E., & Saputra, R. A. (2023). Identifikasi Jenis Daun Tanaman Obat Menggunakan Metode Convolutional Neural Network (CNN) Dengan Model VGG16. Jurnal Informatika Polinema, 9(4), 451-460. Https://doi.org/10.33795/jip.v9i4.1420
  2. Anisman, H. B. (2021). Analisis Faktor – Faktor yang Memengaruhi Kinerja Keuangan pada Pusat Pendapatan Pemerintah Daerah Kabupaten Tulang Bawang. Reviu Akuntansi, Manajemen, dan Bisnis, 1(2), 77-90. doi:10.35912/rambis.v1i2.408
  3. Antika, Z. R., Rusmana, O., & Widianingsih, R. (2023). Analisis Determinasi Minat dan Penggunaan Financial Technology Payment Menggunakan Theory of Planned Behavior: Studi pada Mahasiswa Unsosed. Jurnal Ilmu Siber dan Teknologi Digital, 1(2), 111-124. doi:10.35912/jisted.v1i2.2097
  4. Arrofiqoh, E. N., & Harintaka, H. (2018). Implementasi metode convolutional neural network untuk klasifikasi tanaman pada citra resolusi tinggi. Geomatika, 24(2), 61. 10.24895/JIG.2018.24-2.810
  5. Biswas, P., Anand, U., Saha, S. C., Kant, N., Mishra, T., Masih, H., . . . Majumder, M. (2022). Betelvine (Piper betle L.): A comprehensive insight into its ethnopharmacology, phytochemistry, and pharmacological, biomedical and therapeutic attributes. Journal of cellular and molecular medicine, 26(11), 3083-3119. DOI: 10.1111/jcmm.17323
  6. Boulent, J., Foucher, S., Théau, J., & St-Charles, P.-L. (2019). Convolutional neural networks for the automatic identification of plant diseases. Frontiers in plant science, 10, 941. Https://doi.org/10.3389/fpls.2019.00941
  7. Chompookham, T., & Surinta, O. (2021). Ensemble methods with deep convolutional neural networks for plant leaf recognition. ICIC Express Letters, 15(6), 553-565. DOI:10.24507/icicel.15.06.553
  8. Dewi, N. P. D. A. S., Kesiman, M. W. A., Sunarya, I. M. G., Indradewi, G. A. A. D., & Andika, I. G. (2024). Klasifikasi Jenis Daun Tumbuhan Herbal Berdasarkan Lontar Usada Taru Pramana Menggunakan CNN. Techno. Com, 23(1), 271-283. Https://doi.org/10.62411/tc.v23i1.9510
  9. Diwedi, H. K., Misra, A., & Tiwari, A. K. (2024). CNN-based medicinal plant identification and classification using optimized SVM. Multimedia Tools and Applications, 83(11), 33823-33853. DOI:10.1007/s11042-023-16733-8
  10. Dung, K. D. (2024). Leadership, proactive behavior and innovative work behaviors of teachers in Barkin-Ladi. Annals of Management and Organization Research, 6(1), 13-24. doi:10.35912/amor.v6i1.1867
  11. Faeni, D. P., Puspitaningtyas, R., & Safitra, R. (2021). Work Life Balance, Peningkatan Karir dan Tekanan Kerja terhadap Produktivitas: Kasus pada Lembaga Sertifikasi Profesi P3 Pembangun Penyuluh Integritas Bangsa. Studi Akuntansi, Keuangan, dan Manajemen, 1(1), 45-57. doi:10.35912/sakman.v1i1.602
  12. Faisol, A., Paujiah, S., Russel, E., & Ramelan, M. R. (2022). Pelatihan dan Pendampingan Penggunaan Aplikasi Digital dalam Perencanaan Bisnis dan Keuangan BUMDes. Jurnal Abdimas Multidisiplin, 1(1), 35-40. doi:10.35912/jamu.v1i1.1438
  13. Fatchurrohman, M., & Saputri, P. L. (2023). Limitation of Non-Halal Income (Interest) in The Criteria of Sharia Securities List in Indonesia Stock Exchange. Bukhori: Kajian Ekonomi dan Keuangan Islam, 2(1), 29-38. doi:10.35912/bukhori.v2i1.1708
  14. Hajam, M. A., Arif, T., Khanday, A. M. U. D., & Neshat, M. (2023). An effective ensemble convolutional learning model with fine-tuning for medicinal plant leaf identification. Information, 14(11), 618. Https://doi.org/10.3390/info14110618
  15. Hu, J., Chen, Z., Yang, M., Zhang, R., & Cui, Y. (2018). A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Processing Letters, 25(6), 853-857. DOI:10.1109/LSP.2018.2809688
  16. Larese, M. G., Namías, R., Craviotto, R. M., Arango, M. R., Gallo, C., & Granitto, P. M. (2014). Automatic classification of legumes using leaf vein image features. Pattern Recognition, 47(1), 158-168. DOI:10.1016/j.patcog.2013.06.012
  17. Lee, C. P., Lim, K. M., Song, Y. X., & Alqahtani, A. (2023). Plant-CNN-ViT: plant classification with ensemble of convolutional neural networks and vision transformer. Plants, 12(14), 2642. Https://doi.org/10.3390/plants12142642
  18. Makur, B., Karta, N. L. P. A., & Oktaviani, L. (2023). Pengaruh Electronic Word of Mouth terhadap Kepercayaan dan Keputusan Pembelian pada Aplikasi Shopee Mahasiswa Universitas Triatma Mulya. Jurnal Bisnis Dan Pemasaran Digital, 2(1), 25-38. doi:10.35912/jbpd.v2i1.2255
  19. Safitri, L. I., Husniati, R., & Permadhy, Y. T. (2021). Pengaruh Teamwork, Disiplin Kerja, dan Iklim Organisasi terhadap Kinerja Karyawan: Studi di Rumah Sakit X Jakarta Selatan. Studi Ilmu Manajemen Dan Organisasi, 2(2), 125-137. doi:10.35912/simo.v2i2.806
  20. Shanthi, S., & Radha, R. (2020). Anti-microbial and phytochemical studies of Mussaenda frondosa Linn. Leaves. Pharmacognosy Journal, 12(3). DOI:10.5530/pj.2020.12.94
  21. Suciati, H., Simamora, A. W., Panusunan, P., & Fauzan, F. (2023). Analisa Campuran CPHMA terhadap Penambahan Variasi Aspal Penetrasi 60/70 pada Karakteristik Marshall. Jurnal Teknologi Riset Terapan, 1(2), 75-86. doi:10.35912/jatra.v1i2.2294
  22. Sutrisno, P., Debora, D., Destriana, N., Putri, A. T. K. P. S., Marlinah, A., Wijaya, N., & Lekok, W. (2023). Pendampingan Pelatihan Software Akuntansi Accurate dalam Membantu Guru & Siswa-Siswi Smk untuk Meningkatkan Kompetensi dan Profesionalisme. Jurnal Pemberdayaan Ekonomi, 2(1), 29-37. doi:10.35912/jpe.v2i1.716
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