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Widyawati, Dewi Kania
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Journal : Proceeding Applied Business and Engineering Conference

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Classification of Orchid Types using Random Forest Method with HOG Features Arifin, Oki; Widyawati, Dewi Kania; Zuriati, Zuriati; Maulini, Rima; Sahlinal, Dwirgo; Sylvia, Sylvia
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

Orchids are one of the Indonesian people's most widely cultivated ornamental plants. Orchids are a family ofplants in the Orchidaceae family that includes more than 700 genera and around 28,000 individual species. In terms ofplant morphology, orchids can be distinguished based on the morphology of flowers, leaves, fruits, stems, and roots.Orchid leaves have their characteristics for each type of orchid, such as long, round, or lanceolate. All orchids have leafveins that are parallel to the leaves. This makes it difficult to identify the type of orchid flower, especially for laypeopl ewho are new to orchid cultivation and do not yet know the characteristics of various kinds of orchids. The individualshapes of orchid leaves can be classified using Random Forest and Histogram of Oriented Gradients (HOG). In thisstudy, three types of orchids that are currently popular with orchid lovers were used, namely Cattleya, Phalaenopsis, andVanda orchids taken from public data. The accuracy of this method in classifying orchid types based on leaf morphologycan be measured using a confusion matrix that measures accuracy, precision, recall, and F1-score. The test results showthat this method successfully achieved an accuracy of 98%, with an average precision, recall, and F1-score of 0.98 each.These findings indicate that the model built can classify orchid species with a high level of accuracy based on leafmorphology.
Classification Of Nutrient Deficiency In Lettuce Plants (Lactuca Sativa ) Using Machine Learning Algorithm Zuriati , Zuriati; Widyawati, Dewi Kania; Saputra, Kurniawan; Arifin, Oki
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Plants require appropriate nutrients or nutrients for their growth and development. Inappropriate nutrient levelscan interfere with the plant growth process, resulting in less-than-optimal harvest results. Therefore, it is very importantfor farmers to know the nutrient levels of their plants, neither excessive nor lacking. Identification of nutrient deficienciesin plants such as Lettuce (Lactuca Sativa) traditionally requires careful observation of the physical characteristics of theplant, which is often long-drawn out and stand in need of a high level of accuracy. Leaf color is often used as an indication,for example if it is pale or yellow it can indicate a lack of nitrogen or iron. This requires expertise and experience incultivation for lettuce cultivators. So, a tool is needed that can identify nutrient deficiencies accurately, quickly, and easily.This study aims to overcome this challenge, namely identifying nutrient deficiencies in lettuce plants. This approach utilizesmachine learning technology to distinguish four main classes of deficiencies, namely: nitrogen (N), phosphorus (P), andpotassium (K), as well as normal or healthy lettuce leaf conditions. The proposed research method consists of the followingstages: 1). Lettuce leaf image dataset collection, 2). Preprocessing dataset, 3). Implementation of machine learning usingthe Support Vector Machine (SVM) algorithm. In the implementation of SVM, experiments were carried out by applyingvarious SVM kernel spesifically: Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid, 4). Evaluation of modelperformance. Model performance was evaluated by measuring its level of accuracy in classifying nutrient deficiencies inLettuce leaf image data. The results of the experiment showed that SVM with the RBF kernel had the best accuracy, namely:92%. The findings of this study provide valuable insights into the effectiveness of machine learning approaches inclassifying nutrient deficiencies in Lettuce plants. This study can help farmers to optimize their crop production moreefficiently and accurately.
Co-Authors Ambarwari, Agus Arifin, Oki Djangkaru, Elliana Dulbari, Dulbari Dwirgo Sahlinal, Dwirgo Fathurrahman Kurniawan Ikhsan Fitriani Fitriani Kurniawan Saputra mutiara mutiara Noer, Irmayani Nurkhotimah, Jihan Susan Nurul Qomariyah Putra, Septafiansyah Dwi Rima Maulini Romanda, Novandro Sembiring, Rinawati Sutarni, Sutarni Sylvia, Sylvia Unteawati, Bina Wibowo, Yusep Windhu Ari Yuniarti, Evi Zarnelly Zarnelly Zuriati , Zuriati