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All Journal Jurnal Ilmiah Teknologi Informasi dan Robotika KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika
Putri, Shintyadhita Wirawan
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Automotive Engineering Computer Science & IT Control & Systems Engineering Other

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Penerapan Convolutional Neural Network (CNN) dalam Klasifikasi Citra MRI untuk Deteksi Tumor Otak Manusia Dimara, Denis Lizard Sambawo; Putri, Shintyadhita Wirawan; Amelia, Rizky; Arishandy, Zalfa Ibtisamah; Rizki, Agung Mustika
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 4, No 2 (2023)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2023.v4i2.6960

Abstract

Brain tumors are deadly diseases with a high mortality rate, making early diagnosis crucial to improving patient survival rates. However, manual diagnosis through Magnetic Resonance Imaging (MRI) often requires significant time and is prone to errors. This study developed an MRI image classification method using the EfficientNetB3-based Convolutional Neural Network (CNN) architecture to detect brain tumors. The dataset used was obtained from Kaggle, consisting of 253 brain MRI images, including 98 normal and 155 abnormal images. The data were preprocessed through normalization and resizing to 224x224 pixels. The model employed transfer learning techniques using pretrained weights from ImageNet, enhanced with additional layers to improve performance. Evaluation was conducted using metrics such as accuracy, precision, recall, F1-score, AUC, as well as confusion matrix and classification report analyses. The results showed that the EfficientNetB3 model achieved an overall accuracy of 86%, demonstrating its capability to support brain tumor diagnosis processes quickly and accurately. This implementation is expected to provide a significant contribution to early detection of brain tumors and improve patient care quality in the medical field.
Implementasi Digital Image Processing Menggunakan Discrete Hermite Wavelet Filter Technique Dalam Pemberian Watermark Pada Citra Putri, Shintyadhita Wirawan; Siregar, Talitha Aurora Nadenggan; Salsabilah, Rafani Bardatus; Saputra, Gilang Enggar
Jurnal Ilmiah Teknologi Informasi dan Robotika Vol. 6 No. 2 (2024): Jurnal Ilmiah Teknologi Informasi dan Robotika
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifti.v6i2.160

Abstract

Keamanan data digital menjadi tantangan penting di era digital, terutama untuk melindungi konten multimedia seperti gambar, video, dan audio dari akses ilegal, duplikasi, dan penyebaran tanpa izin. Penelitian ini mengusulkan implementasi teknik Discrete Hermite Wavelet Filters Technique (DHWT) untuk meningkatkan keamanan data digital melalui watermarking. DHWT menggabungkan basis kernel hermite polynomial dan Discrete Wavelet Transform (DWT) untuk menyisipkan watermark secara efisien tanpa merusak kualitas citra asli. Metode ini melibatkan transformasi citra ke skala grayscale, analisis frekuensi menggunakan DHWT, dan dekomposisi citra menjadi empat sub-komponen frekuensi: LL, LH, HL, dan HH. Watermark biner disisipkan pada sub-komponen LH menggunakan dekomposisi QR dan logistic map, memastikan integrasi watermark yang halus namun tetap tahan terhadap serangan seperti kompresi, rotasi, dan noise. Hasil penelitian menunjukkan bahwa DHWT mempertahankan kualitas visual citra dengan peningkatan Peak Signal-to-Noise Ratio (PSNR) yang signifikan, menjadikannya teknik watermarking yang andal dan efisien. Penelitian ini menegaskan potensi DHWT dalam melindungi hak cipta dan integritas data digital, sekaligus menjawab tantangan keamanan data di era modern.
Co-Authors Agung Mustika Rizki, Agung Mustika Arishandy, Zalfa Ibtisamah Dimara, Denis Lizard Sambawo Rizky Amelia Salsabilah, Rafani Bardatus Saputra, Gilang Enggar Siregar, Talitha Aurora Nadenggan