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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF)
Praskatama, Vincentius
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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
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%.
COMPARATIVE ANALYSIS OF LSTM, BILSTM, GRU, CNN, AND RNN FOR DEPRESSION DETECTION IN SOCIAL MEDIA Muhammad Huda, Alam; Shidik, Guruh Fajar; Praskatama, Vincentius
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4060

Abstract

The prevalence of mental health issues and the increasing use of social media provide an opportunity to leverage technology for early detection of depression. This study evaluates and compares five deep learning models, LSTM, BiLSTM, GRU, CNN, and RNN for detecting depressive tendencies from over 10,000 annotated social media messages. These models were trained on preprocessed data using standard techniques, including cleansing, tokenization, and padding. Evaluation metrics such as accuracy, precision, recall, and F1-score were utilized. BiLSTM emerged as the best-performing model with an accuracy of 98.45% and an F1-score of 96.37%, attributed to its bidirectional architecture for contextual analysis. In contrast, CNN achieved high precision (98.55%) but struggled with recall (15.14%), while RNN and GRU exhibited limitations in capturing complex patterns, with GRU showing no measurable performance. These findings establish BiLSTM as a robust tool for mental health monitoring. Future research could explore transformer-based models such as BERT or multilingual datasets for enhanced applicability.
PNEUMONIA PREDICTION USING CONVOLUTIONAL NEURAL NETWORK Praskatama, Vincentius; Sari, Christy Atika; Rachmawanto, Eko Hari; Mohd Yaacob, Noorayisahbe
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1353

Abstract

Pneumonia is condition which our lungs become inflamed due to infection from viruses, bacteria, or fungi. Pneumonia can affect anyone, both adults and children. Because of this, prevention of pneumonia is important. Prevention can be done by the process of maintain our immunity and lungs. In this study, had been done classify pneumonia based on X-ray images. This study using X-ray images dataset with total data is 5840 images in .jpg extensions. With a total number of images from training data is 5216 images and number of images from the test data is 624 images. The dataset that used in this research has 2 main classes, namely class normal and pneumonia. Normal class indicates that the X-Ray results are not detected with pneumonia. While the pneumonia class indicates that the processed X-Ray results are diagnose affected by pneumonia. The purpose of this research is building model that can be used to classify pneumonia based on X-Ray images. The classification process carried out in this study uses the Convolutional Neural Network method. The purpose of using the CNN method in the classification process of this research is because, in the process, CNN can extract features automatically and independently, so that the data provided does not need to be preprocessing first, but the data still produces good extraction features and can provide accurate classification results. The results from the testing process is carried out to run or perform in the pneumonia classification process, the CNN model built obtained a classification test accuracy of 87.82051205635071%.
Co-Authors Christy Atika Sari Eko Hari Rachmawanto Guruh Fajar Shidik Mohd Yaacob, Noorayisahbe Muhammad Huda, Alam Ningrum, Amanda Prawita