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( Rifaldy Fajar ),( Nana Indri Kurniastuti ),( Prihantini Jupri ) 대한결핵 및 호흡기학회 2020 대한결핵 및 호흡기학회 추계학술대회 초록집 Vol.128 No.-
Background/Objective In this study, an Obstructive Sleep Apnea (OSA) disease detection system was created using the RR interval parameter. The design of this detection system uses backpropagation Artificial Neural Network (ANN) which is implemented using MATLAB software as a Method in the classification of OSA determination. Method The steps taken to design an OSA disease detection system in this study include data collection, feature extraction, ANN training, ANN testing and performance determination. The feature extraction stage is performed using the Fast Fourier Transform (FFT) mathematical algorithm process. The Result of feature extraction is then carried out ANN training using 10% of the entire data and ANN testing using 90% of the total data. To get the best performance Results, variations in segment length features, variations in OSA definition features and variations in frequency composition features are performed. Result The best performance Results in this OSA disease detection system design are features that use a combination of frequency components 2, 5 and 6 with an OSA definition of 5% in the 90-segment length. This is shown from the Results of ANN performance in the form of specialization, sensitivity and best accuracy, with successive values of 79.3%, 84.6% and 81.6%. Conclusion In this research, a system design has been made to detect OSA which is implemented in MATLAB software. The feature used in this detection system is the RR interval feature that has been transformed using the Fast Fourier Transform (FFT) operation. Based on the Results of performance calculations, all values indicate a number exceeding 75% so that a system that can be said to be good in detecting is obtained.
( Rifaldy Fajar ),( Dewi Mustika Sari ),( Nana Indri Kurniastuti ),( Prihantini Jupri ) 대한간학회 2020 춘·추계 학술대회 (KASL) Vol.2020 No.1
Aims: Currently there are 10 types of hepatitis B virus genotypes, from A to J, and 4 types of serotypes namely adw, adr, ayw, and ayr. This study aims to determine the genotype and serotype of the hepatitis B virus that has potential as a hepatitis B vaccine candidate. One effective method at present is bioinformatics, a multidisciplinary web-based biological science that can explore various sequences and see phylogeny. Methods: The first stage is the collection and selection of nucleotide DNA sequences or hepatitis B virus amino acids. All data on nucleotide DNA sequences and hepatitis B virus amino acids with the target genotype and serotype are accessed and collected from Genbank. Next, a kinship tree is made. This kinship tree is designed with multiple alignments, phylogeny, and tree viewers using phylogeny.fr. Results: The data obtained shows that there are 43 sequences with the same subtype, Adw, but the genotype and distribution of the spread of the hepatitis B virus are different. Genotype A originates from Somalia (Africa), and the Philippines (Asia), genotype B originates from Indonesia and China. Genotype C explains that genotype C is found around South Asia and East Asia, genotype H obtained information from America and Mexico, and genotype I originates from China. Conclusions: Sequence data that can be candidates for hepatitis B vaccine design are hepatitis B virus genotype B with subgenotype B3, genotype C with subgenotype C6 for the scope of Indonesia, while for the scope of the world obtained the potential of the Adw serotype.
Classification of Pancreatic Cancer Stadium Using Recurrent Neural Network (RNN) Model Algorithm
( Rifaldy Fajar ),( Nana Indri Kurniastuti ),( Dewi Mustika Sari ),( Prihantini Jupri ) 대한간학회 2020 춘·추계 학술대회 (KASL) Vol.2020 No.1
Aims: One way to detect the presence of pancreatic cancer is by examining it using Computed tomography (CT) scan. After a pancreatic cancer is detected, classification is done to determine the stage of cancer. In this study, we used the RNN model for the classification of Pancreatic cancer stadium. This study aimed to explain the procedure and the accuracy of the Elman tissue RNN modeling in pancreatic cancer stadium classification from the CT scan. Methods: The process carried out is to convert the image of red green blue (rgb) to a grayscale image on the CT scan data. After that the image was extracted with Gray Level Co-occurrence Matrix which was designed using Graphical User Interface with Matlab. There are 14 features, namely energy, contrast, correlation, Sum of Square, Inverse Different Moment, sum average, sum variance, sum entropy, entropy, differential variance, differential entropy, maximum probability, homogeneity, and dissimilarity. The feature is used as input, which is then divided into training data and testing data. After that, Elman network RNN modeling was carried out with data normalization, best model design, and data denormalization. The best model design was done by finding the number of hidden neurons and eliminating network inputs using the backpropagation algorithm. Results: The results of the best model training data and testing data were measured using sensitivity, specificity, and accuracy. So that from 74 training data obtained 92% accuracy rate, 96% sensitivity level as a reliable indicator when the results show pancreatic cancer, and 79% level of specificity as a good indicator when the results show normal pancreatic. While in 18 data testing showed 94% accuracy, 100% level of sensitivity, and 80% level of specificity. Conclusions: The conclusion in this study can be said that good classification results are obtained.
( Rifaldy Fajar ),( Nana Indri Kurniastuti ),( Dewi Mustika Sari ),( Prihantini Jupri ) 대한결핵 및 호흡기학회 2020 대한결핵 및 호흡기학회 추계학술대회 초록집 Vol.128 No.0
This study aims to explain the procedure, application, and accuracy of Recurrent Neural Network modeling and Recurrent Neuro-Fuzzy modeling for lung cancer nodule classification from lung photo images. Recurrent Neural Network and Recurrent Neuro-Fuzzy modeling steps are defining input and target variables, dividing data into training data and testing data, data normalization, designing the best model, and data denormalization. The input variable used is the feature of the lung photo image extraction, while the target tissue is the description of the condition from the lung photo image, namely normal lung, benign lung tumor, or malignant lung tumor. The image extraction step begins with an image transformation, namely from the original lung image (gray image) to a binary image, followed by extracting the transformed image using the Gray Level Co-occurrence Matrix Method. The design steps for the best Recurrent Neuro-Fuzzy model begin with the design steps for the best Recurrent Neural Network model followed by data clustering steps using the Fuzzy C-Means Method, learning Recurrent Neural Networks related to antecedents to fuzzy inference rules and consequent fuzzy inference rules, and Simplifying the consequent part by eliminating input and finding the consequent coefficient value of each cluster using the Least Square Estimator (LSE) Method. The Results obtained indicate that the classification of lung cancer nodules using the Recurrent Neural Network model gives better Results than the Recurrent Neuro-Fuzzy model. The sensitivity, specificity, and accuracy values of the Recurrent Neural Network model were 94%, 56%, and 81.33% for training data and 80%, 40%, and 64% for testing data, respectively. The Conclusion in this study is that the Recurrent Neural Network model is able to classify quite well compared to the Recurrent Neuro-Fuzzy model.