Recurrent Neural Network to Deep Learn Conversation in Indonesian

Chowanda, Andry; Chowanda, Alan Darmasaputra

doi:10.1016/j.procs.2017.10.078

Cited by 17 publications

(3 citation statements)

References 3 publications

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“…The last deep learning method is RNN which able to process text data. RNN will produce outputs in the form of evaluating students' abilities based on general and specific abilities Yang et al (2018), predictions that a news story is fake news Sastrawan et al (2021), and good sentence design by paying attention to correlations and correct sentence synonyms Chowanda and Chowanda (2017). Further discussion of the cases solved by deep learning can be seen in Table 4.…”

Section: Depp Learning Methodsmentioning

confidence: 99%

“…Deep learning is a branch of Machine Learning and is an advancement from conventional ML Sastrawan et al (2021). ML still requires human assistance in extracting a feature, in contrast to DL, which is already automatically able to learn raw data from a part significantly to improve the learning process without human assistance Chowanda and Chowanda (2017). In the system, DL uses the back-propagation algorithm to make a machine able to calculate each layer's representation from the previous layer's model.…”

Section: Literature Review Deep Learning (Dl)mentioning

confidence: 99%

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How do we choose the appropriate deep learning method for our case? A systematic literature review

Saputra

Riza

Setiawan

et al. 2022

Preprint

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In the era of technology 4.0, there are many problems in multiple sectors of life that are difficult for humans to solve, ranging from issues in the education quality performance system, difficulties in disease diagnosis, problems in manufacturing systems, construction, food grading, quality control, Etc. Various efforts have been made to solve these problems, from the conventional method of manually retrieving data to obtain the best solution to using a big data-based approach with deep learning. Deep learning has successfully solved problems in various sectors, proving that using big data on deep learning algorithms gives significant results. This systematic review aims to review the studies that have been carried out on applying deep learning to solve or help problems in various sectors. This systematic review shows an overview of deep learning neural networks created in the completion process, the differences in the artificial intelligent methods used, and the advantages and disadvantages of deep learning in various models. It identifies challenges and recommendations for the future. The methods used in this systematic review include search strategies, selecting literature studies, and managing and extracting data. Based on the systematic review results, we know that Convolutional Neural Network (CNN) is the most widely used model for this deep learning algorithm to recognize the feature, along with the image-based data transformation strategy. Finally, deep learning has become very popular because it can transform various data types to get the desired result.

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Section: Depp Learning Methodsmentioning

confidence: 99%

Section: Literature Review Deep Learning (Dl)mentioning

confidence: 99%

How do we choose the appropriate deep learning method for our case? A systematic literature review

Saputra

Riza

Setiawan

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Deep learning architectures and algorithms have been implemented to build or train models in several areas [9][10][11][12][13][14][15]. Most of the deep learning architectures and algorithms applied to model automatic breast cancer classification are the VGG [12], Residual Network (ResNet) [11], and GoogleNet or InceptionNet [13].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Best Parameters of Deep Learning for Breast Cancer Classification System

Chowanda

2022

CommIT (Communication and Information Technology) Journal

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Breast cancer is one of the deadliest cancers in the world. It is essential to detect the signs of cancer as early as possible, to make the survival rate higher. However, detecting the signs of breast cancer using the machine or deep learning algorithms from the diagnostic imaging results is not trivial. Slight changes in the illumination of the scanned area can significantly affect the automatic breast cancer classification process. Hence, the research aims to propose an automatic classifier for breast cancer from digital medical imaging (e.g., Positron Emission Tomography or PET, X-Ray of Mammogram, and Magnetic Resonance Imaging (MRI) images). The research proposes modified deep learning architecture with five different settings to model automatic breast cancer classifiers. In addition, five machine learning algorithms are also explored to model the classifiers. The dataset used in the research is the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM). A total of 2,676 mammogram images are used in the research and are split into 80%:20% (2,141:535) for training and testing datasets. The results demonstrate that the model trained with eight layers of Convolutional Neural Networks (CNN) (SET-8) achieves the best accuracy score of 94.89% and 93.75% in the training and validation dataset, respectively.

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Modelling Sentiment Analysis on Indonesian Product Reviews Using Machine Learning

Chowanda

Lasmy

2023

Lecture Notes on Data Engineering and Communications Technologies

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Recurrent Neural Network to Deep Learn Conversation in Indonesian

Cited by 17 publications

References 3 publications

How do we choose the appropriate deep learning method for our case? A systematic literature review

How do we choose the appropriate deep learning method for our case? A systematic literature review

Exploring the Best Parameters of Deep Learning for Breast Cancer Classification System

Modelling Sentiment Analysis on Indonesian Product Reviews Using Machine Learning

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