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All Journal Dunia Teknologi Informasi - Jurnal Online Data Science: Journal of Computing and Applied Informatics
Maya Silvi Lidya
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Computer Science & IT

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Perancangan Perangkat Lunak Steganografi Audio MP3 Menggunakan Metode Least Significant Bit (LSB) Dengan Visual Basic 6.0 Aminah Rizki Lubis; Maya Silvi Lidya; Muhammad Andri Budiman
Dunia Teknologi Informasi - Jurnal Online Vol 1, No 1 (2012): Jurnal Dunia Teknologi Informasi
Publisher : Universitas Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (466.335 KB)

Abstract

Begitu banyak tindak kejahatan di dunia maya yang dilakukan oleh manusia-manusia tidak bertanggung jawab. Tindakan ini banyak merugikan  pihak dari kalangan masyarakat, walaupun tidak sedikit orang yang turut menikmati keuntungan dari tindakan ini. Sebut saja, seperti maraknya pornografi, pembajakan hak cipta, tindakan kriminal di dunia maya, dan lain sebagainya. Hal ini membuat keamanan di dunia maya tidak aman. Untuk itu perlu adanya pengamanan di dunia maya. Steganografi merupakan ilmu dan seni menyembunyikan data pada suatu media. Adapun wadah yang dijadikan tempat untuk menyembunyikan data tersebut berupa file audio mp3. File audio mp3 sangat populer di kalangan masyarakat. Oleh karena itu, dalam tugas akhir ini penulis merancang perangkat lunak steganografi audio mp3 menggunakan metode LSB yaitu dengan mengubah bit yang kurang significant pada data yang terdapat dalam audio mp3. Perancangan perangkat lunak ini dapat menyisipkan pesan ke dalam audio mp3 dan pesan tersebut dapat diamankan sedemikian mestinya. 
Experimenting Diabetic Retinopathy Classification Using Retinal Images Muhammad Fermi Pasha; Mark Dhruba Sikder; Asif Rana; Maya Silvi Lidya; Ronsen Purba; Rahmat Budiarto
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 1 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i1-5232

Abstract

Along with many complications, diabetic patients have a high chance to suffer from critical level vision loss and in worst case permanent blindness due to Diabetic Retinopathy (DR). Detecting DR in the early stages is a challenge, since it has no visual indication of this disease in its preliminary stage, thus becomes an important task to accomplish in the health sector. Currently, there have been many proposed DR classifier models but there is a lot of room to improve in terms of efficiency and accuracy. Despite having strong computational power, current deep learning algorithm is not able to gain the trust of the medical experts in classifying DR. In this work, we investigate the possibility of classifying DR using deep learning with Convolutional Neural Network (CNN). We implement preprocessing combined with InceptionV3 and VGG16 models. Experimental results show that InceptionV3 outperforms VGG16. InceptionV3 model achieves an average training accuracy of 73.5 % with a validation accuracy of 68.7%. VGG16 model achieves an average training accuracy of 66.4% with a validation accuracy of 63.13%. The highest training accuracy for InceptionV3 and VGG16 is 79% and 81.2%, respectively. Overall, we achieve an accuracy of 66.6% on 52 images from 3 different classes.
Co-Authors Aminah Rizki Lubis Asif Rana Mark Dhruba Sikder Muhammad Andri Budiman Muhammad Fermi Pasha Ronsen Purba Yundari, Yundari