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All Journal JURNAL ELEKTRO Toba: Journal of Tourism, Hospitality, and Destination
Futri Zalzabilah Ray
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Social Sciences

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Deep Learning-Based Brain Tumor Classification Using Convolutional Neural Network Sasita, Naumi; Futri Zalzabilah Ray; M.Fauzanil Wildan A.R.; Tantowi Hutagalung; Rokhmat Febrianto
Jurnal Elektro Vol 18 No 1 (2025): Jurnal Elektro: April 2025
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v18i1.6658

Abstract

An essential noninvasive medical diagnostic technique is magnetic resonance imaging (MRI), which is particularly useful for identifying brain cancers. While earlier algorithms proved effective on smaller MRI datasets, their performance suffered on bigger datasets. This study addresses the need for a swift and reliable brain tumor classification system capable of sustaining optimal performance across comprehensive MRI datasets. The convolutional neural network is implemented using the Keras library, incorporating the ResNet50 architecture as a pre-trained model. The ResNet50 model is fine-tuned for the specific brain tumor classification task, with a Global Average Pooling layer, dropout, and a final dense layer with softmax activation. Data augmentation techniques are employed to enhance the model’s robustness, including rotation, width and height shifts, and horizontal flips. The training process involves optimizing the model using the Adam optimizer with a learning rate of 0.0001. Early stopping, learning rate reduction on plateau, and model checkpointing are implemented as callbacks to ensure efficient training and prevent overfitting. The proposed model achieves a remarkable accuracy of 99.28 percent after 15 epochs. The classification task involves distinguishing among four classes: glioma, meningioma, pituitary, and no tumor.
Peningkatan Minat dan Motivasi Siswa SMK terhadap Kompetensi Internet of Things (IoT) melalui Pelatihan: Mendorong Inovasi Bisnis dalam Sektor Pariwisata Digital Jessica Ignatia Tambunan; Syafrudi, Syafrudi; Putu Ketri Handayani; Futri Zalzabilah Ray
TOBA: Journal of Tourism, Hospitality, and Destination Vol. 4 No. 3 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/toba.v4i3.6160

Abstract

Vocational education, such as Vocational High Schools (SMK), faces challenges such as limited facilities and low digital literacy, particularly in developing technology-based competencies for the digital tourism sector. This study aims to determine the extent to which Internet of Things (IoT) training can increase vocational high school students' interest and motivation in developing competencies related to digital tourism innovation. This study uses a qualitative approach with data collection techniques through semi-structured interviews with vocational high school students directly involved in training activities. Focus Group Discussions (FGDs) were chosen to delve deeper into students' perceptions and experiences regarding their understanding and interest in the application of Internet of Things (IoT) technology in the tourism industry. This research instrument used semi-structured interviews, focus group discussions (FGDs), observation, and documentation. The results show that case study-based training helps students understand the application of IoT in a concrete manner that is relevant to their majors. Students showed increased enthusiasm, curiosity, and analytical skills in linking IoT to business innovation opportunities in the tourism sector. The conclusion of this study is that IoT training can be a strategy in preparing a skilled and adaptive workforce in the digital era.
Co-Authors Jessica Ignatia Tambunan M.Fauzanil Wildan A.R. Putu Ketri Handayani Rokhmat Febrianto Sasita, Naumi Syafrudi, Syafrudi Tantowi Hutagalung