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All Journal Perfecting a Video Game with Game Metrics Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science WIDYA LAKSANA Logic : Jurnal Rancang Bangun dan Teknologi Dharma: Jurnal Pengabdian Masyarakat
I Nyoman Gede Arya Astawa
Politeknik Negeri Bali
Computer Science & IT Education Engineering Social Sciences Other

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Journal : Knowledge Engineering and Data Science

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Face Images Classification using VGG-CNN I Nyoman Gede Arya Astawa; Made Leo Radhitya; I Wayan Raka Ardana; Felix Andika Dwiyanto
Knowledge Engineering and Data Science Vol 4, No 1 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i12021p49-54

Abstract

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image.  The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).
Social Media Mining with Fuzzy Text Matching: A Knowledge Extraction on Tourism After COVID-19 Pandemic Ida Bagus Putra Manuaba; I Wayan Budi Sentana; I Nyoman Gede Arya Astawa; I Wayan Suasnawa; I Putu Bagus Arya Pradnyana
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p143-149

Abstract

Social media mining is an emerging technique for analyzing data to extract valuable knowledge related to various domains. However, traditional text matching techniques, such as exact matching, are not always suitable for social media data, which can contain spelling mistakes, abbreviations, and variations in the use of words. Fuzzy matching is a text matching technique that can handle such variations and identify similarities between two texts, even if there are differences in spelling or phrasing. The gap in existing research is the limited use of fuzzy matching in social media mining for tourism recovery analysis. By applying fuzzy matching to social media data related to COVID-19 and tourism recovery, this research seeks to bridge this gap and extract valuable insights related to the impact of the pandemic on tourism recovery. We manually retrieved 19,462 Twitter records and differentiated the data sources using four diver parameters to indicate data related to the impact of COVID-19 on the tourism industry, such as the economy, restrictions, government policies, and vaccination. We conducted text mining analysis on the collected 7,352 words and identified 25 highly recommended words that indicated COVID-19 recovery from a tourism perspective. We separated the four words representing the tourism perspective to perform fuzzy matching as a dataset. We then used the inbound dataset on the fuzzy matching process, with the 7,352-word data collected from the text mining process. The matching process resulted in 18 words representing COVID-19 recovery from a tourism perspective.
Co-Authors Aji Prasetya Wibawa Atmaja, I Made Ari Dwi Suta Dessy Hariyanti, NK Fachrul Kurniawan Fajar Purnama Felipe P. Vista IV Felix Andika Dwiyanto Gusti Nyoman Ayu Sukerti I Dewa Gede Ary Subagia I ketut Gede Darma Putra I MADE SUDARMA I Putu Bagus Arya Pradnaya I Putu Bagus Arya Pradnyana I Putu Gde Sukarata I Putu Sutawinaya I Wayan Budi Sentana I Wayan Raka Ardana I Wayan Suasnawa I Wayan Suasnawa Ida Bagus Putra Manuaba IGAK Cathur Adhi Luther Latumakulita Nida Jabari Noorul Islam Putu Gde Sukarata Radhitya, Made Leo Rukmi Sari Hartati Vitrail Gloria Mairi