Health Detection of Betal Leaves Using Self-Organizing Map and Thresholding Algorithm
DOI:
https://doi.org/10.37385/jaets.v4i1.957Keywords:
Betel Leaf, Thresholding, Self-Organizing MapAbstract
Betel leaf is one of the plants that is widely used as a natural or traditional medicine by the community, natural treatment with the use of plants is relatively safer. But there is a problem when we choose healthy betel leaves because of our mistakes in choosing which betel leaves are healthy and which are not. With this research the authors aim to detect healthy and sick betel leaves using data collection. Feature extraction used is the value of Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV) to get the characteristics of the color image. Then the results of the feature extraction are used to classify the health of green betel leaves using the Self-Organizing Maps method. The green betel leaf data used is 1500 images for train data and 450 images for testing data are image test data, test data that produces an evaluation value with an accuracy value of 97.20% on the Self-Organizing Maps method.
Downloads
References
Adenugraha, S. P., Arinal, V., & Mulyana, D. I. (2022). Klasifikasi Kematangan Buah Pisang Ambon Menggunakan Metode KNN dan PCA Berdasarkan Citra RGB dan HSV. Jurnal Media Informatika Budidarma, 6(1), 9. https://doi.org/10.30865/mib.v6i1.3287
Angulo-Saucedo, G. A., Leon-Medina, J. X., Pineda-Muñoz, W. A., Torres-Arredondo, M. A., & Tibaduiza, D. A. (2022). Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring. Sensors, 22(4), 1484.
Ardiansah, F., Silawati, Yesi, Yuhani, & Fatayah Nur Isnani. (2021). Pelatihan Pembuatan Handsanitizer Alami Dengan Ekstrak Daun Sirih Dan Jeruk Nipis Untuk Mencegah Penyebaran Covid-19 Di Desa Cupat Parittiga. Jurnal Abdimas Bina Bangsa, 2(1), 198–203. https://doi.org/10.46306/jabb.v2i1.69
Ashari, I. F., Banjarnahor, R., Farida, D. R., Aisyah, S. P., Dewi, A. P., & Humaya, N. (2022). Application of Data Mining with the K-Means Clustering Method and Davies Bouldin Index for Grouping IMDB Movies. Journal of Applied Informatics and Computing, 6(1), 07-15.
Asri, P. P., & Wulanningrum, R. (2021). Implementasi SOM (Self Organizing Maps) untuk Identifikasi Kematangan Buah Tomat. JTECS?: Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem& Komputer, 1(2), 185–192.
Alazzam, M. B., Mohammad, W. T., Younis, M. B., Al Sayeh, A. M., Hajjej, F., AlGhamdi, A. S., & Rahman, M. A. (2022). Studying the effects of cold plasma phosphorus using physiological and digital image processing techniques. Computational and Mathematical Methods in Medicine, 2022.
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1), 40-55.
Halim, N. N., & Widodo, E. (2017). Clustering dampak gempa bumi di indonesia menggunakan kohonen self organizing maps. Prosiding SI MaNIS (Seminar Nasional Integrasi Matematika Dan Nilai Islami), 1(1), 188–194. http://conferences.uin-malang.ac.id/index.php/SIMANIS/article/view/62
Iswandana, R., & Sihombing, L. K. (2017). Formulasi, Uji Stabilitas Fisik, dan Uji Aktivitas Secara In Vitro Sediaan Spray Antibau Kaki yang Mengandung Ekstrak Etanol Daun Sirih (Piper betle L.). Pharmaceutical Sciences and Research, 4(3), 121–131.
Juliansa hengki, D. sarjon, & Sumijan. (2017). Uji Validasi Algoritma Self Organizing Map (SOM) dan K-Mens untuk Pengelompokan Pegawai. Resti, 1(1), 19–25.
Kania, S., Rachmatin, D., & Afgani Dahlan, J. (2019). Program Aplikasi Pengelompokan Objek Dengan Metode Self Organizing Map Menggunakan Bahasa R. Jurnal EurekaMatika, 7(2), 17–29.
Khotimah, T., & Darsin, D. (2020). Clustering Perkembangan Kasus Covid-19 Di Indonesia Menggunakan Self Organizing Map. Jurnal Dialektika Informatika (Detika), 1(1), 23–26. https://doi.org/10.24176/detika.v1i1.5596
Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 1906-1955.
Nafi’iyah, N., & Fatichah, C. (2017). Fuzzy self organizing map untuk proses thresholding pada citra dental panaromic. September, 511–524.
Nayak, P., Swetha, G. K., Kaushal, P., & Padhan, D. G. (2022). Cluster Formation Algorithm in WSNs to Optimize the Energy Consumption Using Self-Organizing Map. In IoT and Analytics for Sensor Networks (pp. 11-22). Springer, Singapore.
Utari, E. L., Listyalina, L., & Puspaningtyas, D. E. (2019). Aplikasi self-organizing mapping sebagai alat deteksi anemia pada citra sel darah merah. Jurnal Gizi Klinik Indonesia, 16(2), 64. https://doi.org/10.22146/ijcn.39560
Wang, R., Shi, T., Zhang, X., Wei, J., Lu, J., Zhu, J., ... & Liu, M. (2022). Implementing in-situ self-organizing maps with memristor crossbar arrays for data mining and optimization. Nature communications, 13(1), 1-10.
CITEDNESS IN SCOPUS
CITEDNESS IN WOS
