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All Journal Insyst : Journal of Intelligent System and Computation Journal of Innovative and Creativity
Wardoyo, Nikko Riestian Putra
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Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Optimizing Brain Tumor Detection from MRI Images Through Combined VGG16 and ResNet50V2 Models with Batch Normalization Nabilah, Anisah; Wardoyo, Nikko Riestian Putra
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Brain tumors are one of the most critical and life-threatening health conditions requiring rapid and accurate diagnostic support. Early detection plays a crucial role in determining appropriate medical interventions and improving patient survival rates. With advances in artificial intelligence, particularly computer vision, medical image transmission has emerged as a promising field to address the challenges of manual diagnosis, which is often time-consuming and prone to human error. Magnetic resonance imaging (MRI) is widely used in brain imaging due to its ability to provide detailed structural information, making it an ideal modality for tumor detection and classification. This study employs a Convolutional Neural Network (CNN)-based approach that integrates two deep learning architectures: VGG16 and ResNet50V2, using batch normalization to improve feature extraction and reduce overfitting. Evaluation experiments were conducted on an MRI dataset of 1,311 brain tumor MRI images classified into pituitary, notoma, meningioma, and glioma classes. The aim of this study was to develop a fast, accurate, and efficient method for detecting brain tumors. The results show that the proposed hybrid architecture achieves 98% accuracy, outperforming each pretrained model when applied separately. This study demonstrates that combining multiple CNN architectures with batch normalization can significantly improve the precision and accuracy of brain tumor detection. This approach has the potential to become a valuable diagnostic tool for radiologists, enabling faster and more accurate clinical decision-making. Furthermore, the application of such deep learning models in medical practice could contribute to reducing diagnostic errors and improving patient care in the long term.
Klasifikasi Micro-Expression Menggunakan K-Nearest Neighbors Menggunakan Fitur CAS dan HOG Wardoyo, Nikko Riestian Putra; Santoso, Joan; Setiawan, Esther Irawati
Intelligent System and Computation Vol 5 No 2 (2023): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v5i2.346

Abstract

Micro-Expression adalah ekspresi yang muncul dalam waktu singkat, hanya berlangsung sepersekian detik. Hal ini mungkin merupakan akibat dari aktivitas komunikasi antar manusia selama interaksi sosial. Reaksi ekspresi mikro wajah terjadi secara alami dan segera, sehingga hanya menyisakan sedikit ruang untuk manipulasi. Namun, karena Micro-Expression bersifat sementara dan memiliki intensitas rendah, pengenalan dan pengenalannya sulit dan sangat bergantung pada pengalaman para ahli. Karena kekhususan dan kompleksitas intrinsiknya, klasifikasi Micro-Expression menggunakan 2 ekstraksi yaitu CAS dan HOG menarik tetapi menantang, dan baru-baru ini menjadi area penelitian yang aktif. context-aware saliency (CAS) yang bertujuan untuk mendeteksi wilayah gambar yang mewakili pemandangan. Tutujuannya adalah untuk mendeteksi objek dominan. Histogram Oriented Gradient (HOG) Bertujuan sebagai deskriptor yang efektif untuk pengenalan dan deteksi objek. Metode K-Nearest Neighbors (K-NN) digunakan untuk klasifikasi Micro-Expression berdasarkan fitur HOG dari citra saliency. Dataset yang digunakan pada penelitian ini dari data sampel siswa SMK Ma’arif NU Prambon jurusan Multimedia sebanyak 45 siswa dan ditambahkan dataset dari affecnet. Hasil yang didapatkan dari total dataset sebanyak 4116 citra yang dibagi menjadi 6 Micro-Expression yaitu anger, disgust, fear, happy, sad dan surprise, mendapatkan hasil akurasi diatas 80% dari perbandingan dataset sejumlah 4116 terbagi menjadi 2 dengan persentase 70% training dan 30% data testing.
Co-Authors Esther Irawati Setiawan Joan Santoso Nabilah, Anisah