Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network

Ayu, Tsabitah; Dwi, Vizza; Minarno, Agus Eko

doi:10.24114/cess.v6i2.22857

Cited by 2 publications

(2 citation statements)

References 10 publications

(10 reference statements)

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“…The confusion matrix table is used to measure the performance of a classification model because it consists of many rows of test data that are predicted correctly and incorrectly by the classification model. Confusion matrix are a useful tool to analyze how well a clustering and classifier can recognize tuples from different classes [23].…”

Section: Confusion Matrixmentioning

confidence: 99%

Hoax News Detection Using Passive Aggressive Classifier And TfidfVectorizer

Lazuardi,

Hiunarto,

Ramadhani

et al. 2023

j. Teknik inform.

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Indonesia is one of the countries with the highest number of social media users. Million social media users in Indonesia reached 167 million in January 2023. These users are spread, across various social media, including Twitter with 24 million users. The high number of social media users on Twitter makes the information validation process even more neglected. Moreover, the trend of news interest read by social media users is only adjusted to their individual tastes. This phenomenon is evidenced by the large number of fake news (hoaxes) circulating in society which are spread through social media. Therefore, an accurate machine learning model is needed to classify "real" and "hoax" news. This study uses the TfidfVectorizer algorithm and Passive Aggressive Classifier for datasets that are shared through the Kaggle site. The contents of the dataset were sourced via social media Twitter over a span of 5 years, namely 2015-2020. At the preprocessing stage to making the Confusion Matrix, the machine learning model shows that it can work well as expected, namely getting Accuracy, Precision, and Recall scores of 82.44%, 80.66%, and 82.44%. In addition, the results of the confusion matrix show that in the dataset used, there is more "real" news than "hoaxes", that is, the model is able to predict 1059 real news and 211 hoax news, with actual conditions 1106 real news and 164 hoax news.

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Section: Confusion Matrixmentioning

confidence: 99%

Hoax News Detection Using Passive Aggressive Classifier And TfidfVectorizer

Lazuardi,

Hiunarto,

Ramadhani

et al. 2023

j. Teknik inform.

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show abstract

“…His further research was conducted by Kusrini, et diseases in mangoes, it was found that the accuracy results using the VVG16 model achieved top validation with accuracy of 89% and 90% respectively [9]. Another study conducted by Tsabitah Ayu, et al using a model (CNN) to classify the types of mango leaves affected by pests and healthy obtained accuracy results of 0.96% [10].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

Rinanda,

Aini,

Pertiwi

et al. 2024

PREDATECS

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Plant diseases pose a serious threat to a country's economy and food security. One way to identify diseases in plants is through the visible features on their leaves. Farmers need to conduct an active examination of the condition of the leaves of plants to eradicate this disease. In this case, automatic recognition and classification of diseases of leaf crops is required in order to obtain an accurate identification. Digital image processing technology can be used to solve this problem. One effective approach is the Convolutional Neural Network (CNN). The trial image used a dataset consisting of 4000 images of mango leaf disease, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould. This study aims to compare the accuracy of CNN, VGG16 and InceptionV3. Architectural modeling uses these drawings to train and test models in recognizing and classifying mango leaf diseases. The results of modeling trials in the three scenarios were most optimally obtained by VGG16 with an accuracy of 96.87%, then InceptionV3 with an acquisition of 96.50% and CNN by 81%.

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Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network

Cited by 2 publications

References 10 publications

Hoax News Detection Using Passive Aggressive Classifier And TfidfVectorizer

Hoax News Detection Using Passive Aggressive Classifier And TfidfVectorizer

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

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