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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) International Conference on Engineering and Technology Development (ICETD) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURNAL ILMIAH SIMANTEK Journal Of Information And Technology Unimor (JITU) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Pengabdian Masyarakat Nauli
Humuntal Rumapea
Universitas Methodist Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Implementasi Deep Learning untuk Deteksi Dini Bencana Cuaca Ekstrem Berbasis Analisis Citra Awan Humuntal Rumapea
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp313-318

Abstract

This study aims to implement Deep Learning methods for early detection of extreme weather disasters based on satellite cloud image analysis. The dataset consists of multi-spectral imagery obtained from the Himawari-8 satellite, covering various atmospheric conditions. The proposed approach employs two main models: Convolutional Neural Network as the baseline model and Vision Transformer as the comparative model. The research methodology includes data preprocessing, model training, evaluation using accuracy, precision, recall, and F1-score metrics, and model interpretation using Explainable AI techniques. The results indicate that the Vision Transformer outperforms the CNN model, achieving an accuracy of over 92%. Furthermore, Grad-CAM visualization demonstrates that the model effectively identifies cloud regions associated with extreme weather phenomena. This study contributes to the development of an accurate and interpretable cloud-based early warning system, with potential applications in disaster mitigation, particularly in regions prone to extreme weather such as Indonesia.
Evaluasi Kinerja CNN dan Vision Transformer pada Klasifikasi Citra Resolusi Tinggi Berbasis Deep Learning Humuntal Rumapea
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp372-379

Abstract

This study aims to evaluate and compare the performance of Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in high-resolution image classification based on deep learning. The dataset consists of high-resolution images that undergo preprocessing and data augmentation, and is divided into training, validation, and testing sets. The CNN models used include ResNet50 and EfficientNet as baselines, while Vision Transformer is employed as a comparative model utilizing a self-attention mechanism. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F1-score, as well as training and inference time. The results indicate that Vision Transformer achieves superior classification performance compared to CNN, with an accuracy of up to 93.85%. However, CNN demonstrates better computational efficiency with lower training and inference time. Furthermore, increasing image resolution improves the performance of both models, albeit at the cost of higher computational complexity, particularly for Vision Transformer. This study highlights a trade-off between accuracy and efficiency, suggesting that model selection should be aligned with specific application requirements.
Co-Authors Agus Syahputra Ana Yunita Aritonang, Mendarissan Darwis Robinson Manalu Dian Agaventa, Chrissandro Doli Hasibuan Edward Rajagukguk Efdi Sarjono Panjaitan Ely Sawato Zebua Fati Gratianus Nafiri Larosa Gea, Asa Z Gea, Asaziduhu Gortap Lumbantoruan Gresa Simarmata Harianja, Eva J.G Hasibuan, Doli Hasibuan, Doli Hutapea, Jhon M Hutapea, Marlina Hutapea, Melanthon Indra Kelana Jaya Indra M. Sarkis Indra M. Sarkis S. Indrayana Siboro Ingngarso Simanullang Jhoni Maslan Jhoni Maslan, Jhoni Jimmy F. Naibaho Krisna Diva Lumbanraja, Posma Marzuki Sinambela Milisi Sembiring Mufria J. Purba Naikson Fandier Saragih Nettina Samosir Nogar Silitonga Panjaitan, Efdi Sarjono Posma Lumbanraja Putra Palipi Rajagukguk, Edward Rajagukguk, Edward Resianta Parangin-angin Rijois Iboy E. Saragih Rumapea, Yolanda Y. P. Rut Natalina Nababan Samosir, Nettina Samuel Van Basten Halomoan Manurung Sarkis, Indra M. Siboro, Indrayana Silalahi, Arina Prima Simamora, Indra S Simanullang, Harlen Gilbert Sitinjak, Vivi Sri Agustina Rumapea Sufriaty Malau Surianto Sitepu Yolanda Y. P. Rumapea yolanda yulianti pratiwi rumapea Yunita, Ana