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All Journal JAIS (Journal of Applied Intelligent System) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Mnemonic
Ningrum, Amanda Prawita
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Computer Science & IT

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KOMPARASI WATERMARKING DENGAN LIFTING WAVELET TRANSFORM DAN DISCTERE WAVELET TRANSFORM Ningrum, Amanda Prawita; Praskatama, Vincentius; Sari, Christy Atika; Rachmawanto, Eko Hari
Jurnal Mnemonic Vol 7 No 1 (2024): Mnemonic Vol. 7 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i1.7779

Abstract

Privasi dan security merupakan hal yang sangat penting untuk dijaga pada era teknologi seperti sekarang. Cara untuk menjaga privasi dapat dilakukan dengan menggunakan pengamanan pada data. Data perlu dijaga karena didalam data tersebut terdapat informasi pribadi dan sangat bahaya apabila sampai disalahgunakan oleh pihak yang tidak bertanggung jawab. Watermarking merupakan proses yang dilakukan untuk melakukan pengamanan data dengan cara menyisipkan citra watermark ke dalam citra host atau utama. Tujuan dilakukannya watermarking yaitu untuk melakukan pengamanan pada citra. Pada penelitian ini akan dilakukan proses watermarking dengan menggunakan algoritma Lifting Wavelet Transform (LWT) dan Disctere Wavelet Transform (DWT). Tujuan dilakukannya penelitian ini yaitu untuk melakukan komparasi dari proses watermarking yang dimana nantinya dapat dilihat proses watermarking mana yang lebih baik untuk digunakan. Data yang digunakan pada penelitian ini yaitu citra host menggunakan citra Lena dan Baboon yang memiliki ukuran 512*512 pixel dan citra watermark dengan ukuran 64*64 pixel. Hasil yang didapatkan dari penelitian ini yaitu setelah dilakukan proses pengujian, dengan menggunakan citra lena, pada algoritma LWT mendapatkan nilai PSNR sebesar 47.5513 dB dan pada algoritma DWT mendapatkan PSNR sebesar 42.2207 dB
Vehicle Detection Using Image Conversion Percentage to Binary Method Based on K-Means Irawan, Candra; Ningrum, Amanda Prawita; Nohan, Rejendra
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11178

Abstract

Vehicle detection is the artificial intelligence that can help us in transportation highway systems like counter vehicles passing through the road on Eid Mubarak day etc. The object in this case is divided into six classifications there are car, motorbike, van, truck, and three-wheel. On the dataset vehicle is mostly an image of a car that we get from Kaggle. To solve vehicle detection problems such as poor vehicle detection and reduced detection accuracy, we provide a new vehicle detection with a dataset at kaggle. The clustering process consists of steps in which input images are transformed into morphometrics. The next step is to classify the image data using the K-Means algorithm. The images grouped by this detection are vehicles. The first step is to determine the randomly drawn mean or center point of two image data values ​​in the database. If there is no data transfer, the group is considered stable and group creation is completed. Seven vehicle image data are used to test this application. And the result of our experiment on vehicle detection is about 85.7 % accurate
Comparative Study: Flower Classification using Deep Learning, SMOTE and Fine-Tuning Praskatama, Vincentius; Shidik, Guruh Fajar; Ningrum, Amanda Prawita
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8730

Abstract

Deep learning is a technology that can be used to classify flowers. In this research, flower type classification using the CNN method with several existing CNN architectures will be discussed. The data consists of 4317 images in .jpg format, covering 5 classes that is sunflower, dandelion, daisy, tulip and rose. The distribution of data for each class is daisy with 764 pictures, dandelion with 1052 pictures, rose with 784 pictures, sunflower with 733 pictures, and tulip with 984 pictures. With total dataset of 4317 pictures is further split to training data with ratio of 60%, validation with ratio of 10%, and testing with ratio of 30% to process with the CNN method and CNN framework. Due to the imbalance data distribution, the SMOTE method is applied to balancing number of samples in each class. This research compares CNN architectures, including CNN, GoogleNet, DenseNet, and MobileNet, where each transfer learning model undergoes fine-tuning to improve performance. At the classification stage, performance will be measured based on model testing accuracy. The accuracy obtained using CNN is 74.61%, using GoogleNet is 87.45%, DenseNet is 93.92%, and MobileNet is 88.34%.
Co-Authors Candra Irawan Christy Atika Sari Eko Hari Rachmawanto Guruh Fajar Shidik Nohan, Rejendra Praskatama, Vincentius