Classification of mycobacterium tuberculosis based on color feature extraction using adaptive boosting method

Rachmad, Aeri; Chamidah, Nur; Rulaningtyas, Riries

doi:10.1063/5.0042283

Cited by 10 publications

(13 citation statements)

References 8 publications

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“…The confusion matrix is a method that can be used to measure the performance of the classification model. The confusion matrix displays the performance results of the classification model in the form of a matrix [23]. Based on the confusion matrix, there are 4 possible results from the comparison between the prediction class and the actual class, namely true positive (TP), false positive (FP), false negative (FN), and true negative (TN).…”

Section: Discussionmentioning

confidence: 99%

“…The ensemble learning used aims to overcome the problem of unstable classification by combining some basic learning to reduce errors during prediction. This method works by making modeling using several decision trees or in other words, namely a collection of several decision trees [15]. Where in each tree there is an estimated classification that we can consider (called a vote) to combine each possibility from each tree, then choose the classification that has the greatest number of classifications to produce optimal and stable predictions [16].…”

Section: Random Forestmentioning

confidence: 99%

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Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods

Ubaidillah

Rochman

Fatah

et al. 2022

J. Phys.: Conf. Ser.

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Corn is one of the staple foods consumed by many people after rice plants, especially in Indonesia. High consumer demand requires corn production in large quantities to meet these needs. However, corn production is not always in large quantities due to several factors, namely diseases in corn plants. Unhealthy corn plants can reduce the amount of production. Healthy and unhealthy corn plants can be identified manually, but this method is not efficient, so in this study, it is proposed to classify corn diseases using the Random Forest, Neural Network, and Nave Bayes methods. The dataset used is a collection of corn leaf images taken from farmers’ fields in the Madura Region with four target classes, namely healthy, gray leaf spot, blight, and common rust. Based on the test results, the classification using the Neural Network method provides a better accuracy value than the other two methods in classifying corn leaf datasets, namely the AUC value reaches 90.09%, classification accuracy is 74.44%, f1 score is 72.01%, precision is 74.14% and recall by 74.43%.

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Section: Discussionmentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods

Ubaidillah

Rochman

Fatah

et al. 2022

J. Phys.: Conf. Ser.

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“…The steps to be taken are 8, 16, 32 and 64 using a combination of Adaboost from decisionmaking to learning methods [15]. The results of this study obtained the best Tuberculosis bacteria classification accuracy value in the testing process using the Adaboost method, namely 81.7% [16].…”

Section: Figure 2 Estimated Impact Of the Covid-19 Pandemic On The Nu...mentioning

confidence: 94%

“…The extracted colors are HSV colors, where a collection of hue values forms a histogram cluster. In the classification method with AdaBoost and an expert system (random forest), the results of the Tuberculosis classification get an accuracy value of 85% [14].…”

Section: Figure 2 Estimated Impact Of the Covid-19 Pandemic On The Nu...mentioning

confidence: 99%

Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

Riza¹,

Na’am²

2022

TEM Journal

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Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting for a long time. Biopsy is one of the techniques used to take the patient's lung fluid and given Ziehl Neelsen chemical dye and then observed using a microscope to determine this TBEP disease. In this research, the TBEP detection process was developed using a classification method, namely CNN with feature extraction and feature selection. The feature uses 5 features where these features are a combination of shape features and texture features with the highest information gain value. From the results of research conducted through the training and testing stages of the classification method using feature selection, the accuracy rate is higher than not using feature selection with a comparison of the feature selection stage increasing 0.6536% for the training process, and 0.8942% for the testing process.

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“…Confusion Matrix is a table used for performance analysis that will facilitate visualization as shown in Table1. The matrix can distinguish between True Positive, False Negative, False Positive, and True Negative [14] [15] [16].…”

Section: Confusion Matrixmentioning

confidence: 99%

Corn Leaf Disease Classification Using Local Binary Patterns (LBP) Feature Extraction

Rachmad

Syarief

Rifka

et al. 2022

J. Phys.: Conf. Ser.

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Corn is a plant that is widely grown in developing countries such as Indonesia. To increase maize yields, researchers are always innovating on the current state of technology for classifying maize plant diseases. Three kinds of diseases attack corn leaves, namely Gray leaf Spot, Blight, and Common Rush. The amount of data that we use is 3500 data consisting of 500 Gray Leaf Spots, 1000 Blights, 1000 Common Rushes, and 1000 healthy leaves. This study aims to develop an artificial intelligence model. The artificial intelligence model that we developed uses LBP feature extraction combined with k-NN for the classifier. In addition to using the k-NN method, our tests were carried out using several classification methods such as Naïve Bayes and Adaboost. The result of our test is that the k-NN method has the highest value compared to the Naïve Bayes and Adaboost methods. The results of the performance using k-NN with k=5 resulted in a value of 81.1%, the AUC value of 94.1%, the F1-Score of 80.9%, Precision of 81.8%, and Recall of 81.1%.

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Classification of mycobacterium tuberculosis based on color feature extraction using adaptive boosting method

Cited by 10 publications

References 8 publications

Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods

Classification of Corn Diseases using Random Forest, Neural Network, and Naive Bayes Methods

Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

Corn Leaf Disease Classification Using Local Binary Patterns (LBP) Feature Extraction

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