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All Journal Journal of Innovative and Creativity Prosiding Seminar Nasional Kusuma
Anisah Nabilah
Universitas PGRI Jombang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Meningkatkan Kemampuan Berpendapat Siswa melalui Penerapan Perangkat Teknologi Argumen Matematis dengan Model Infusion Learning Lia Budi Tristanti; Wiwin Sri Hidayati; Anisah Nabilah; Farda Rahmawanda; Nur Wahida Putri
Prosiding Seminar Nasional Kusuma Vol 2 (2024): Prosiding Seminar Nasional Kusuma
Publisher : LPPM UWKS

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

Abstract

Latar Belakang: Permasalahan yang ditemui bahwa siswa yang langsung menyelesaikan soal matematika tanpa menyertakan argumen, padahal argumen memiliki peran penting. PkM ini bertujuan meningkatkan kemampuan berpendapat siswa melalui penerapan Perangkat Teknologi Argumen Matematis dengan model infusion learning. Dalam konteks pembelajaran matematika, kemampuan berargumen sangat penting untuk membantu siswa mengembangkan logika yang kuat, pandangan yang jelas, dan penjelasan yang rasional. Faktanya kemampuan argumentasi siswa masih rendah dan belum ada perangkat teknologi khusus yang digunakan untuk menyusun, menyajikan, atau mengevaluasi argumen berdasarkan komponen argumentasi Toulmin. Tujuan: Oleh karena itu, PkM ini bertujuan mengaplikasikan model infusion learning dan perangkat digital argumen untuk meningkatkan kemampuan argumentasi siswa kelas IX di SMPN 2 Jombang. Metode: pelaksanaan melibatkan ceramah, diskusi, demonstrasi, latihan, dan pendampingan. Evaluasi keberhasilan program dilakukan melalui pengukuran pemahaman peserta terhadap materi dan peningkatan kemampuan berpendapat secara matematis menggunakan N-Gain. Hasil: PkM menunjukkan bahwa penerapan teknologi argumen matematis dengan model infusion learning berhasil meningkatkan kemampuan berpendapat sebagian besar siswa. Sebanyak 36,4% siswa menunjukkan peningkatan pada kategori tinggi, sementara 63,6% lainnya berada pada kategori sedang. Kesimpulan: Ini mengindikasikan bahwa metode yang digunakan cukup efektif, meskipun perlu ada penyesuaian lebih lanjut untuk peningkatan yang lebih signifikan  
Optimizing Brain Tumor Detection from MRI Images Through Combined VGG16 and ResNet50V2 Models with Batch Normalization Anisah Nabilah; Nikko Riestian Putra Wardoyo
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 | DOI: 10.31004/joecy.v5i3.3630

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.
Co-Authors Farda Rahmawanda Lia Budi Tristanti Nikko Riestian Putra Wardoyo Nur Wahida Putri Wiwin Sri Hidayati