Komparasi Random Forest Dan Logistic Regression Dalam Klasifikasi Penderita Covid-19 Berdasarkan Gejalanya

Rice is an intriguing research topic, particularly in computer vision fields, because it is a staple food consumed in many parts of the world. Different rice varieties can be classified using the rice grain image based on their textures, sizes, and colors. To extract features from rice images, we used two popular pre trained convolutional neural network models, Inception V3 and VGG 16. The extracted features are then used as transfer learning in six variations of multilayer perceptron models, using rectified linear units as the activation function and adaptive moments as the loss function. The results show that the VGG 16 network performs better than the Inception V3, with 0.5% higher accuracy, precision, and recall value. Also, using the VGG 16 network produces a lower misclassification percentage, compared to the Inception V3 network, with a difference of 2.6%.

Section: Imentioning

confidence: 99%

Inception-V3 Versus VGG-16: in Rice Classification Using Multilayer Perceptron

Firmansyah¹,

Rosnelly

Wanayumini³

2023

“…AdaBoost is an algorithm that trains a classifier by selecting crucial features from weak classifier linear combinations [25]. The formulas in equations ( 6) to (8) show the calculation to evaluate a model's performance using the accuracy, precision, and recall values [26]. AdaBoost uses the combination of ht(x) (a based classifier), αt (a learning rate), and F(x) (a strong classifier) in the formula, as shown in equation (2).…”

Section: Adaboostmentioning

confidence: 99%

Comparison Of K-nearest Neighbor (KNN) And Linear Discriminant Analysis (LDA) Algorithms For Mature Ajwa Date Fruit Classification

Risna,

Amanda,

Uyun

2023

Currently, many applications of artificial intelligence in various fields of life, especially in image data, require digital image processing. One example of the use of digital images often encountered is image processing of fruit ripeness. Dates are a fruit in great demand by the people of Indonesia, and one of the most popular dates is the Ajwa date. The author is interested in developing previous research regarding identifying the ripeness of Ajwa Dates, where previous research used RGB color image processing with the HIS method. Therefore, the authors want to apply a different method, namely the K-Nearest Neighbor (K-NN) method and Linear Discriminant Analysis (LDA), in classifying the ripeness of the Ajwa Dates by applying a statistical feature algorithm. This research aims to develop a classification model for the maturity level of Ajwa Dates. Furthermore, it is expected to provide better classification results than the previous algorithm. The test results using the KNN method can produce higher accuracy than the LDA, where the KNN method is obtained from the calculation of the Euclidean distance k = 1 100% and Manhattan with a value of k = 1 and k = 2 worth 100%, but the minimum accuracy value is 53.33 % is found at k = 9 in the Euclidean distance calculation, while the LDA accuracy value can reach 93.33%.

“…Using flood incidents and water level data from DKI Jakarta as a source of knowledge, data mining is a suitable tool to categorize the flood status by classifying the data into specified categories [3]. Data mining analyzes and extracts knowledge from a dataset and uses that knowledge to solve problems such as association, clustering, prediction, estimation, and classification [4]. Some popular data mining algorithms used in classification problems are K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) for their high processing speed, easy implementation, and good performance [5].…”

Section: Imentioning

confidence: 99%

Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

Batubara¹,

Zarlis²,

Rosnelly³

2023

In the era of technology 4.0, a system is needed to support the development of a company, both in industry, education, and others to help solve problems. In this study, the authors used the Backpropagation Neural Network Algorithm in recognizing hyperthyroid disease patterns. In this study, in the recognition of hyperthyroid disease patterns. The author uses 11 data variables that will be trained using the back-propagation algorithm where the weighting is done randomly and the second data is trained using the backpropagation algorithm. In this study using Matlab application for processing. From the results of testing data derived from kaggle, namely hyperthyroid disease data above, we can see in the 2-2-1 architecture which shows that the target is reduced by the jst output that the SSE is 0.06571 which indicates that there is an increase in hyperthyroid disease activity in humans. From the data obtained, that the performance of artificial neural network calculations with the Backpropagation Algorithm is 86%. Can be seen by comparing the desired target with the prediction target.