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All Journal J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence) Journal of Educational Management and Learning
Mursyida, Waliam
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Other

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Digital Transformations in Vocational High School: A Case Study of Management Information System Implementation in Banda Aceh, Indonesia Idroes, Rinaldi; Subianto, Muhammad; Zahriah, Zahriah; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Noviandy, Teuku Rizky; Sugara, Dimas Rendy; Mursyida, Waliam; Zhilalmuhana, Teuku; Idroes, Ghalieb Mutig; Maulana, Aga; Nurleila, Nurleila; Sufriani, Sufriani
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.128

Abstract

This study examines the digital transformation in vocational education through the implementation of a Management Information System (MIS) in Banda Aceh, Indonesia. Focused on enhancing educational administration and decision-making, the study provides insightful analysis on the integration of MIS in State Vocational High School (SMK), specifically SMKN 1 and SMKN 3 in Banda Aceh. A purposive sampling method was employed for usability testing. The questionnaire-based usability test revealed high reliability and positive user responses across multiple indicators. Data analysis affirmed the system's high user satisfaction, effectiveness, and ease of use. Despite limitations, the study highlights the significant potential of well-designed MIS in improving operational efficiency and user satisfaction in educational settings. Future research directions include expanding the sample size, conducting longitudinal studies, incorporating qualitative methods, and exploring the impact on educational outcomes, to enhance the generalizability and depth of understanding regarding the role of MIS in education.
Analisis Performa Segmentasi Citra MRI Tumor Otak dengan Arsitektur U-Net dan Res-UNet Misbullah, Alim; Mursyida, Waliam; Farsiah, Laina; Nazaruddin, Nazaruddin; Sukiakhy, Kikye Martiwi; Husaini, Husaini; Basrul, Basrul
J-SIGN (Journal of Informatics, Information System, and Artificial Intelligence) Vol 2, No 02 (2024): November
Publisher : Department of Informatics, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/j-sign.v2i02.41358

Abstract

Diagnosis tumor otak melalui MRI menghadapi tantangan akibat keterbatasan dalam visualisasi morfologi, lokasi, dan batas-batas tumor. Format MRI yang biasanya dua dimensi memerlukan interpretasi manual oleh radiolog, yang meningkatkan risiko kesalahan manusia. Untuk meningkatkan akurasi segmentasi MRI, pendekatan pembelajaran mendalam seperti Convolutional Neural Networks (CNN) telah diterapkan untuk menyoroti area-area penting. Studi ini membandingkan dua arsitektur CNN, U-Net dan Res-UNet, untuk segmentasi tumor otak menggunakan dataset Brain Tumor Segmentation Challenge (BraTS) 2020. Kedua model dilatih dengan pengaturan yang serupa dan dievaluasi berdasarkan kemampuannya mengidentifikasi area kunci, termasuk inti tumor, edema, dan area tumor yang mengalami peningkatan. Model ini menggunakan optimizer Adam dan fungsi loss categorical crossentropy, dengan metrik evaluasi termasuk akurasi. Hasil menunjukkan bahwa U-Net mencapai performa optimal pada 35 epoch dengan ukuran batch 64 dan learning rate 0,001, menghasilkan nilai loss terendah (0,0140) dan akurasi tertinggi (99,5%). Meskipun Res-UNet juga mencapai akurasi tinggi (99,3%), nilai loss yang lebih tinggi menunjukkan bahwa model ini kurang efektif dibandingkan U-Net.
Co-Authors Basrul, Basrul Farsiah, Laina Husaini Husaini Idroes, Ghalieb Mutig Irvanizam, Irvanizam Maulana, Aga Misbullah, Alim Muhammad Subianto N. Nazaruddin Nurleila, Nurleila Razief Perucha Fauzie Afidh Rinaldi Idroes sufriani, sufriani Sugara, Dimas Rendy sukiakhy, kikye martiwi Teuku Rizky Noviandy Zahriah, Zahriah Zhilalmuhana, Teuku