DETEKSI DAN KLASIFIKASI MANFAAT MORINGA OLIEVERA UNTUK KESEHATAN MENGGUNAKAN TEKNIK MACHINE LEARNING DAN IMAGE PROCESSING
DOI:
https://doi.org/10.54314/jssr.v8i4.5371Abstrak
Abstract: Moringa Olivera (MO) is a plant that offers numerous health benefits. In recent years, the leaves, seeds, and flowers of Moringa have been traditionally used in medicine. The leaves of Moringa Olivera contain various nutrients such as vitamins, minerals, amino acids, beta carotene, antioxidants, and other beneficial compounds. These compounds have the potential to help treat a range of diseases. Therefore, there is limited research available that classifies the specific content of these compounds in Moringa leaves. In this study, the traditional benefits of MO for treating diseases will be classified using Machine Learning (ML) techniques. Before classification, image processing steps such as preprocessing, segmentation, and feature extraction are performed to create an image dataset. As a result, 381 images of Moringa Olivera are categorized into two groups: 167 dry MO images and 214 wet MO images. The dataset is then organized based on the compound content and the diseases that can be treated. The next step involves using the SVM model to classify the images based on the benefit label, with 15 different disease categories. The SVM model achieves a training accuracy of 0.9078947368421053 and a testing accuracy of 0.8026315789473685, which are higher than those achieved by a regression classification model.
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Keywords: Detection, Classification, Moringa Olievera, Machine Learning, Image Procesing.
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Abstrak: Moringa Olivera (MO) merupakan tanaman yang memiliki banyak manfaat bagi dunia Kesehatan. Dalam beberapa dekade terakhir daun, biji, dan bunga MO telah banyak dimanfaatkan secara tradisional dalam melakukan pengobatan. Bagian daun MO mengandung Vitamin, Mineral, Asam Amino, Beta Karoten, Antioksidan, dan masih banyak lagi. Kandungan senyawa ini yang dapat dimanfaatkan untuk mengatasi berbagai penyakit. Namun, masih minim penelitian yang dilakukan untuk melakukan klasfikasi kandungan senyawa dari daun kelor. Oleh karena itu, pada penelitian ini akan dilakukan klasifikasi manfaat MO untuk mengobati penyakit secara tradisional menggunakan Teknik Machine Learning (ML). Pada penelitian ini sebelum dilakukan klasifikasi maka akan dilakukan pengolahan citra seperti preprocessing, segmentasi, dan ekstraksi untuk mendapatkan dataset citra. Hasilnya 381 citra MO dapat dibentuk menjadi suatu dataset berdasarkan dua kaegori yaitu 167 MO kering dan 214 MO basah. Dataset yang telah terbentuk kemudian dikumpulkan berdasarkan kandungan senyawa yang dimiliki dan penyakit yang dapat diobati. Tahapan selanjutnya menggunakan model SVM untuk melakukan klasifikasi berdasarkan label manfaat dengan 15 kelas penyakit. Hasilnya akurasi model pelatihan SVM sebesar 0,9078947368421053 dan pengujian sebesar 0,8026315789473685 lebih baik dari model klasifikasi regresi.
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Kata kunci: Deteksi, Klasifikasi, Moringa Olievera, Machine Learning, Image Procesing.
Unduhan
Referensi
Dwike, M., Santi, S., Yasa, G. T., & Nugraha, I. S. (n.d.). Volume 1 Nomor 2 (2022) Desember Pemanfaatan Daun Kelor (Moringa Oleifera Lam) sebagai Bahan Obat Tradisional Utilization of Moringa Leaf (Moringa Oleifera Lam) as A Traditional Medicine. https://ejournal.politeknikkesehatankartinibali.ac.id/index.php/pkm
Srirahayu, D., Atul Fuadah, Lu, Lestari, S., Purwati, Y., Harun, N., Kuniasih, N., & Muhammadiyah Ciamis, Stik. (N.D.). Benefits Of Moringa Leaves During The Covid-19 Pandemic.
Saputra, A., Arfi, F., & Yulian, M. (N.D.). LITERATURE REVIEW: ANALISIS FITOKIMIA DAN MANFAAT EKSTRAK DAUN KELOR (Moringa Oleifera).
Kelor Terhadap Kesehatan Masyarakat Riza Adrianoor Saputra, M., Santoso, U., Heiriyani, T., Wahdah, R., Andi Syarifuddin, N., Aminda Putri, K., Navira, A., & Aisyah, N. (2021). The Miracle Tree. 1(2), 54–62. https://doi.org/10.20527/ilung.v1i2
Xu, Y., Chen, G., & Guo, M. (2022). Potential Anti-aging Components From Moringa oleifera Leaves Explored by Affinity Ultrafiltration With Multiple Drug Targets. Frontiers in Nutrition, 9. https://doi.org/10.3389/fnut.2022.854882.
Tafu, N. N., & Jideani, V. A. (2022). Proximate, Elemental, and Functional Properties of Novel Solid Dispersions of Moringa oleifera Leaf Powder. Molecules, 27(15). https://doi.org/10.3390/molecules27154935.
Shalaby, E. A., Shanab, S. M. M., El-Raheem, W. M. A., & Hanafy, E. A. (2022). Biological activities and antioxidant potential of different biosynthesized nanoparticles of Moringa oleifera. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-23164-2.
Susanti, R., Biologi, J., & Negeri Semarang Jl Raya Sekaran, U. (n.d.). IDENTIFIKASI SENYAWA BIOAKTIF Moringa oleifera SEBAGAI ANTIINFLAMASI MELALUI LIGAN PADA TOLL-LIKE RECEPTOR SIGNALING PATHWAY UNTUK PREDIKSI PENCEGAHAN STUNTING SECARA IN SILICO http://www.swisstargetprediction.ch.
Angelina, C., Swasti, Y. R., & Sinung Pranata, F. (2021). PENINGKATAN NILAI GIZI PRODUK PANGAN DENGAN PENAMBAHAN BUBUK DAUN KELOR (Moringa Oleifera): REVIEW Increased Nutritional Value Of Food Products With The Addition Of Moringa Leaf Powder: A Review (Vol. 15, Issue 01).
Luthfiyani, D., Pradana, C., Revina, R., Rifkia, V., & Kedokteran, F. (N.D.). Pelatihan Pembuatan Teh Daun Kelor Sebagai Antioksidan Dan Pencegah Diabetes Bagi Masyarakat Kampung Utan Depok.
Wen, Y., Li, W., Su, R., Yang, M., Zhang, N., Li, X., Li, L., Sheng, J., & Tian, Y. (2022). Multi-Target Antibacterial Mechanism of Moringin From Moringa oleifera Seeds Against Listeria monocytogenes. Frontiers in Microbiology, 13. https://doi.org/10.3389/fmicb.2022.925291.
Ennouni, A., Sihamman, N. O., Sabri, M. A., & Aarab, A. (2021). Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition. Journal of Computer Science, 17(3), 284–295. https://doi.org/10.3844/JCSSP.2021.284.295.
Ahmed, M., & Ahmed, A. (2023). Palm tree disease detection and classification using residual network and transfer learning of inception ResNet. PLoS ONE, 18(3 March). https://doi.org/10.1371/journal.pone.0282250.
Thimmegowda, T. G. M., & Jayaramaiah, C. (2023). Cluster-based segmentation for tobacco plant detection and classification. Bulletin of Electrical Engineering and Informatics, 12(1), 75–85. https://doi.org/10.11591/eei.v12i1.4388.
Rehman, H. U., Anwar, S., & Tufail, M. (2020). Machine vision based plant disease classification through leaf imagining. Ingenierie Des Systemes d’Information, 25(4), 437–444. https://doi.org/10.18280/isi.250405.
John, S., & Rose, A. L. (2021). MACHINE LEARNING TECHNIQUES IN PLANT DISEASE DETECTION AND CLASSIFICATION - A STATE OF THE ART. INMATEH - Agricultural Engineering, 65(3), 362–372. Https://Doi.Org/10.35633/INMATEH-65-38.
Ferdinand, Y., & al Maki, W. F. (2022). Broccoli leaf diseases classification using support vector machine with particle swarm optimization based on feature selection. International Journal of Advances in Intelligent Informatics, 8(3), 337–348. https://doi.org/10.26555/ijain.v8i3.951.
Raju, H., & Narasimhaiah, V. K. (2023). Insights on assessing image processing approaches towards health status of plant leaf using machine learning. IAES International Journal of Artificial Intelligence, 12(2), 884–891. https://doi.org/10.11591/ijai.v12.i2.pp884-891.
Orchi, H., Sadik, M., Khaldoun, M., & Sabir, E. (2023). Automation of Crop Disease Detection through Conventional Machine Learning and Deep Transfer Learning Approaches. Agriculture (Switzerland), 13(2). https://doi.org/10.3390/agriculture13020352.
Vishnoi, V. K., Kumar, K., Kumar, B., Mohan, S., & Khan, A. A. (2023). Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network. IEEE Access, 11, 6594–6609. https://doi.org/10.1109/ACCESS.2022.3232917.
Madhavan, M. V., Thanh, D. N. H., Khamparia, A., Pande, S., Malik, R., & Gupta, D. (2021). Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques. Computers, Materials and Continua, 66(3), 2939–2955. https://doi.org/10.32604/cmc.2021.012466.
Kethineni, K., & Pradeepini, G. (2023). Identification of Leaf Disease Using Machine Learning Algorithm for Improving the Agricultural System. Journal of Advances in Information Technology. https://doi.org/10.12720/jait.14.1.122-129.
Iparraguirre-Villanueva1, O. (n.d.). Jaringan Syaraf Konvolusional Identifikasi Penyakit pada Tanaman Pangan berdasarkan. www.ijacsa.thesai.org.
Abuhayi, B. M., & Mossa, A. A. (2023). Coffee disease classification using Convolutional Neural Network based on feature concatenation. Informatics in Medicine Unlocked, 39. https://doi.org/10.1016/j.imu.2023.101245.
Citra Rani, K., & Deasy Rosita Dewi, A. (2019). MODUL PELATIHAN KANDUNGAN NUTRISI TANAMAN KELOR Disusun oleh.
Firman Syahputra, A., Indra Prasetyo, B., & Elsa Shafira, A. (2023). Klasifikasi Tingkat Dehidrasi Berdasarkan Warna Urin Menggunakan Metode K-Nearest Neighbor. Jurnal Bangkit Indonesia, 12(01).
Setiawan, Y., & Basri, M. (2023). APLIKASI PENGENALAN BENTUK DAN SUARA HEWAN BERBASIS AUGMENTED REALITY. In JURNAL SINTAKS LOGIKA (Vol. 3, Issue 1). https://jurnal.umpar.ac.id/index.php/sylogâ–ª6.
Sidik, A. P., Amin, M., & Wilana, A. (n.d.). PERANCANGAN KLASIFIKASI KUALITAS BUAH JERUK BERDASARKAN WARNA.
Ode, W., Aulia, N., Hidayat, W., & Baso, F. (n.d.). Analisis Dan Perancangan Desain Aplikasi Keamanan Data Berbasis Teks Menggunakan Algoritma RSA. https://journal.diginus.id/index.php/decoding.
Kumar, M., Gupta, S., Gao, X. Z., & Singh, A. (2019). Plant Species Recognition Using Morphological Features and Adaptive Boosting Methodology. IEEE Access, 7, 163912–163918. https://doi.org/10.1109/ACCESS.2019.2952176.
Peng, Y., & Wang, Y. (2019). Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder. International Journal of Food Properties, 22(1), 1720–1732.
Adams, J., Qiu, Y., Xu, Y., & Schnable, J. C. (2020). Plant segmentation by supervised machine learning methods. Plant Phenome Journal, 3(1). https://doi.org/10.1002/ppj2.20001.
Andrews, S., Ramesh, K., Nandhakumar, T., Kamalraj, T., & Ragupathi, P. (2019). Plant variety and weed growth identification: Trending towards machine learning. International Journal of Recent Technology and Engineering, 8(2), 6232–6237. https://doi.org/10.35940/ijrte.B3293.078219.
Sugumar, D., Harshavarthan, V., Kavisri, S., Aezhisai Vallavi, M. S., & Vanathi, P. T. (2019). Citrus classification and grading using machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8(10), 2616–2621. https://doi.org/10.35940/ijitee.J9349.0881019.
Flor, J., Oraño, V., Maravillas, E. A., Jordan, C., & Aliac, G. (2020). Classification of Jackfruit Fruit Damage Using Color Texture Features and Backpropagation Neural Network. 10(5).
Shabari Shedthi, B., Siddappa, M., & Shetty, S. (2019). Identification of plant leaf disease using machine learning techniques. International Journal of Recent Technology and Engineering, 8(3), 6077–6081. https://doi.org/10.35940/ijrte.C5621.098319.
[John, S., & Joseph, A. L. R. P. (2023). Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique. IAES International Journal of Artificial Intelligence, 12(3), 1149–1157.
Raju, H., & Narasimhaiah, V. K. (2023). Insights on assessing image processing approaches towards health status of plant leaf using machine learning. IAES International Journal of Artificial Intelligence, 12(2), 884–891. https://doi.org/10.11591/ijai.v12.i2.pp884-891
