dor_id: 4129222
506.#.#.a: Público
590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares
510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation
561.#.#.u: https://www.icat.unam.mx/
650.#.4.x: Ingenierías
336.#.#.b: article
336.#.#.3: Artículo de Investigación
336.#.#.a: Artículo
351.#.#.6: https://jart.icat.unam.mx/index.php/jart
351.#.#.b: Journal of Applied Research and Technology
351.#.#.a: Artículos
harvesting_group: RevistasUNAM
270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx
590.#.#.c: Open Journal Systems (OJS)
270.#.#.d: MX
270.1.#.d: México
590.#.#.b: Concentrador
883.#.#.u: https://revistas.unam.mx/catalogo/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1017/873
100.1.#.a: Mahmudy, Wayan Firdaus; Dewi, Candra; Arifando, Rio; Ahmadie, Beryl Labique; Rahman, Muh Arif
524.#.#.a: Mahmudy, Wayan Firdaus, et al. (2021). Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network. Journal of Applied Research and Technology; Vol. 19 Núm. 6, 2021; 633-643. Recuperado de https://repositorio.unam.mx/contenidos/4129222
245.1.0.a: Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM
264.#.0.c: 2021
264.#.1.c: 2021-12-31
653.#.#.a: morphology; wavelet; convex hull; artificial neural network; SMOTE; patchouli variety
506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx
884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/1017
001.#.#.#: 074.oai:ojs2.localhost:article/1017
041.#.7.h: eng
520.3.#.a: Patchouli plants are main raw materials for essential oils in Indonesia. Patchouli leaves have a very varied physical form based on the area planted, making it difficult to recognize the variety. This condition makes it difficult for farmers to recognize these varieties and they need experts’ advice. As there are few experts in this field, a technology for identifying the types of patchouli varieties is required. In this study, the identification model is constructed using a combination of leaf morphological features, texture features extracted with Wavelet and shape features extracted with convex hull. The results of feature extraction are used as input data for training of classification algorithms. The effectiveness of the input features is tested using three classification methods in class artificial neural network algorithms (1) feedforward neural networks with backpropagation algorithm for training, (2) learning vector quantization (LVQ), (3) extreme learning machine (ELM). Synthetic minority over-sampling technique (SMOTE) is applied to solve the problem of class imbalance in the patchouli variety dataset. The results of the patchouli variety identification system by combining these three features indicate the level of recognition with an average accuracy of 72.61%, accuracy with the combination of these three features is higher when compared to using only morphological features (58.68%) or using only Wavelet features (59.03 %) or both (67.25%). In this study also showed that the use of SMOTE in imbalance data increases the accuracy with the highest average accuracy of 88.56%.
773.1.#.t: Journal of Applied Research and Technology; Vol. 19 Núm. 6 (2021); 633-643
773.1.#.o: https://jart.icat.unam.mx/index.php/jart
022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423
310.#.#.a: Bimestral
300.#.#.a: Páginas: 633-643
264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM
doi: https://doi.org/10.22201/icat.24486736e.2021.19.6.1017
harvesting_date: 2023-11-08 13:10:00.0
856.#.0.q: application/pdf
file_creation_date: 2021-12-06 18:05:45.0
file_modification_date: 2021-12-06 18:05:45.0
file_creator: Yolanda G.G.
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license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
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