—Pneumonia is an illness that affects practically everyone, from children to the elderly. Pneumonia is an infectious disease caused by viruses, bacteria, or fungi that affect the lungs. It is quite difficult to recognize someone who has pneumonia. This is because pneumonia has multiple levels of classification, and so the symptoms experienced may vary. The multilayer perceptron approach will be used in this study to categorize Pneumonia and determine the level of accuracy, which will contribute to scientific development. The Multilayer Perceptron is employed as the classification method with hyperparameter learning rate and momentum, while SURF is used to extract the features in this classification. Based on the experiments that have been carried out, in general, the learning rate value is not very influential in the learning process, both at the momentum values of 0.1, 0.3, 0.5, 0.7, and 0.9. The best desirable accuracy value for momentum 0.1 is at a learning rate of 0.05. The best desirable accuracy value for momentum 0.3 is at a learning rate of 0.09. The most desirable accuracy value for momentum 0.3 is at a learning rate of 0.05 and 0.07. At a learning rate of 0.03 the highest ideal accuracy value is obtained. The best desirable accuracy value for momentum 0.9 is at a learning rate of 0.09. this research should be redone using the number of hidden layers and nodes in each hidden layer. The addition of a hidden layer, as well as variations in the number of nodes in the hidden layer, will affect computation time and yield more optimal accuracy results.

/pdf/optimization-of-multilayer-perceptron-hyperparameter-in-3t3w59dv.pdf

Optimization of Multilayer Perceptron Hyperparameter in Classifying Pneumonia Disease Through X-Ray Images with Speeded-Up Robust Features Extraction Method

The condition, which has recently become a hot topic of debate among people all over the world, was first discovered in the city of Wuhan, Hubei Province, China, and was later reported to the World Health Organization, namely the WHO (World Health Organization), on December 31, 2019 as an outbreak of a respiratory disease. It is caused by the virus SARS Corona Virus 2. (SARS-CoV-2). X-ray images can be used to detect COVID-19-infected lungs using a Convolutional Neural Network (CNN). The resnet-50 architecture and Inception v3 deep learning model were used in this research. This study employs several parameters, including epoch, batch size, and optimizer. The resnet-50 and inception v-3 architectures were utilized to optimize results. According to the results, the resnet-50 and inception v-3 architectures had the best epochs of 25, the best batch was 200, and the best optimizer was Adam. Overall, the most optimal hyperparameter is in epoch 25, batch 200 optimizer Adam with 99% accuracy and a computation time of 6 h for Inception v-3 and 9 h 21 min for resnet-50. The most optimal architecture in the classification of covid-19 based on x-ray images is the Inception v-3 architecture. 

Convolutional Neural Network (CNN) of Resnet-50 with Inceptionv3 Architecture in Classification on X-Ray Image

Wayang, an ancient cultural tradition in Java, has been an integral part of Indonesian culture for 1500 years.Rooted in Hindu cultural influences, wayang has evolved into a highly esteemed and beloved performance art.In the form of wayang kulit, this tradition conveys profound philosophical messages and implicit meanings that resonate with Javanese society.This research aims to develop an artificial intelligence (AI) model using deep learning with the LeNet architecture to accurately classify wayang images.The model was tested with 2515 Punakawan wayang images, showing excellent performance with an accuracy of 80% to 85%.Although the model successfully recognizes and distinguishes wayang classes, it faces some challenges in classifying specific classes, particularly in scenarios 2 and 4. Nevertheless, this research has a positive impact on cultural preservation, as the developed AI model can be used for automatic wayang image recognition.These implications open opportunities to better understand and preserve this rich cultural heritage through AI technology.With further improvements, this model has the potential to become a valuable tool in the efforts to preserve and introduce wayang culture to future generations. 

Preserving Cultural Heritage Through AI: Developing LeNet Architecture for Wayang Image Classification

Spices are biological resources that have long played a very important role in everyday life. Spices have characteristics, shapes, and colors that are almost similar and it is difficult to distinguish one spice from another. To assist in recognizing the characteristics of existing spices, the author tries to do research with the title. "Spices Classification Using the K-Nearest Neighbor (K-NN) Method and Using Histogram Oriented Gradient Feature Extraction. The method used in this study is the K-Nearest Neighbor and uses the Histogram Of Oriented Gradient feature extraction. In this study, the dataset used was 2250 image samples and divided into two categories, namely training data and testing data with a ratio of 80%: 20%. The results of this study indicate that the most optimal testing distance formula, namely the Manhattan distance formula, obtained an average accuracy of 87%, 87% precision, 87% recall, 87% f1 score, 87% Fbeta score, and 77% Jaccard score. These results indicate that feature extraction greatly influences the number of types in extracting information, the Histogram of Oriented Gradient works optimally when the number of types extracted is small and not optimal when used in a large number of classification types.

A modification of the Distance Formula on the K-Nearest Neighbor Method is Examined in Order to Categorize Spices from Photo Using the Histogram of Oriented Gradient *

Spices are biological resources that have long played a very important role in everyday life. Spices have characteristics, shapes, and colors that are almost similar and it is difficult to distinguish one spice from another. To assist in recognizing the characteristics of existing spices, the author tries to do research with the title. "Spices Classification Using the K-Nearest Neighbor (K-NN) Method and Using Histogram Oriented Gradient Feature Extraction. The method used in this study is the K-Nearest Neighbor and uses the Histogram Of Oriented Gradient feature extraction. In this study, the dataset used was 2250 image samples and divided into two categories, namely training data and testing data with a ratio of 80%: 20%. The results of this study indicate that the most optimal testing distance formula, namely the Manhattan distance formula, obtained an average accuracy of 87%, 87% precision, 87% recall, 87% f1 score, 87% Fbeta score, and 77% Jaccard score. These results indicate that feature extraction greatly influences the number of types in extracting information, the Histogram of Oriented Gradient works optimally when the number of types extracted is small and not optimal when used in a large number of classification types. 

Support vector machine (SVM) is a method that is often used in various studies to do pattern recognition of objects in the form of images. SVM is also a classification technique which is a characteristic of carrying out the training process and testing process. In classifying with data in the form of images, SVM is assisted by a feature extract technique where the process normalizes data so that a good training process can be carried out. however, SVM generally uses a linear kernel function in the testing phase. So that there is interest in this paper by designing a thought to make comparisons with several other kernel function techniques such as the cubic kernel function and the quadratic kernel function in classifying the Indonesian Wayang images which are the legacy of Indonesian ancestors. The results of this paper by varying the kernel function in SVM have an accuracy of the cubic kernel function of 83.4%.

Measuring the Accuracy of SVM with Varying Kernel Function for Classification of Indonesian Wayang on Images

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Measuring the Accuracy of SVM with Varying Kernel Function for Classification of Indonesian Wayang on Images