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All Journal International Journal of Engineering, Science and Information Technology Adopsi Teknologi dan Sistem Informasi
Muhammad Fawaz Saputra
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Penerapan Metode Kanban Dalam Pengembangan Sistem Informasi Manajemen Dokumen Pratidina, Ummi; Vina Zahrotun Kamila; Islamiyah; Riftika Rizawanti; Muhammad Fawaz Saputra
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 5 No. 1 (2026): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v5i1.4391

Abstract

Badan Penanggulangan Bencana Daerah (BPBD) Provinsi Kalimantan Timur menerapkan sistem pengarsipan dokumen secara hibrida. Untuk pengarsipan dokumen secara digital, BPBD Provinsi Kalimantan Timur menggunakan Google Drive lalu didistribusikan melalui aplikasi pengirim pesan seperti Whatsapp. Mekanisme ini membuat proses pencarian dokumen memakan waktu lama dan distribusi dokumen digital menjadi tidak efisien. Maka dari itu dikembangkan Sistem Informasi Manajemen Dokumen (SIMD) yang merupakan sistem untuk penyimpanan, pengelolaan, dan pengarsipan dokumen berbasis digital. Metode pengembangan SIMD yang diterapkan dalam memenuhi kebutuhan sistem BPBD Provinsi Kalimantan Timur yaitu kanban. Pengembangan Sistem Informasi Manajemen Dokumen (SIMD) memanfaatkan kerangka kerja Laravel dan basis data berupa MySQL. Penelitian ini menghasilkan SIMD yang sesuai dengan kebutuhan utama BPBD Provinsi Kalimantan Timur, yaitu sistem pengarsipan digital yang mempermudah pencarian dokumen dan distribusi dokumen. Sistem ini dapat diakses oleh tiga jenis pengguna yang berbeda, yaitu super admin, pegawai, dan pengguna umum. Hasil akhir dari pengujian blackbox menunjukkan bahwa sistem yang dikembangkan ini berjalan sesuai dengan kebutuhan BPBD Provinsi Kalimantan Timur dalam pengelolaan dokumentasi kebencanaan secara digital.
ICTROPS2025_Deep Learning Methods for Pneumonia Detection Using ConvNeXt Architecture Akhmad Irsyad; Muhammad Bambang Firdaus; Gubtha Mahendra Putra; Putut Pamilih Widagdo; Hario Jati Setiady; Muhammad Fawaz Saputra; Muhammad Abdillah Rahmat
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1797

Abstract

Pneumonia remains a major respiratory infection with high mortality rates, especially in regions with limited access to medical specialists. Early and accurate diagnosis plays a critical role in reducing fatal outcomes and improving patient management. Chest X ray imaging is widely used as a primary diagnostic modality, yet interpretation relies heavily on experienced radiologists, whose availability is often insufficient to meet clinical demand. This condition motivates the development of automated pneumonia detection systems based on artificial intelligence. This study investigates the application of deep learning for pneumonia classification using chest X ray images, with a focus on the ConvNeXt architecture. ConvNeXt represents a modern neural network design that integrates structural advantages from Vision Transformers with the efficiency of traditional Convolutional Neural Networks, enabling strong feature extraction while maintaining computational efficiency. The research evaluates multiple ConvNeXt variants, including Tiny, Small, Base, and Large, to analyze the relationship between model complexity and classification performance. ResNet50 is employed as a baseline model to provide a fair comparative assessment against a widely used convolutional architecture. Model evaluation uses accuracy, precision, recall, and F measure to ensure balanced measurement across different classification outcomes and class distributions. Experimental results indicate that ConvNeXt Tiny achieves the highest overall performance, reaching an accuracy of 97.69 percent while using a relatively low number of parameters. This outcome highlights the efficiency of lightweight architectures for medical image analysis tasks. The findings demonstrate that ConvNeXt Tiny delivers strong discriminative capability with reduced computational requirements, making it suitable for deployment in resource constrained clinical environments. This study contributes evidence supporting the effectiveness of modern deep learning architectures for automated pneumonia detection and provides insight into model selection for practical medical imaging applications.
Co-Authors Gubtha Mahendra Putra Hario Jati Setiady Irsyad, Akhmad Islamiyah Muhammad Abdillah Rahmat Muhammad Bambang Firdaus Pratidina, Ummi Putut Pamilih Widagdo Riftika Rizawanti Vina Zahrotun Kamila