Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Akuntansi Bisnis Eka Prasetya : Penelitian Ilmu Akuntansi INOVTEK Polbeng - Seri Informatika
Yohannes
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Faktor-Faktor Yang Mempengaruhi Ketepatan Waktu Pelaporan Keuangan Pada Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia Arniman Zebua; Selfie Gultom; Yohannes
Jurnal Akuntansi Bisnis Eka Prasetya : Penelitian Ilmu Akuntansi Vol 6 No 1 (2020): Edisi Maret
Publisher : lppm.eka-prasetya.ac.id

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

Abstract

Penelitian ini bertujuan untuk menemukan bukti empiris tentang faktor-faktor yang mempengaruhi ketepatan waktu pelaporan keuangan perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia. Faktor-faktor yang diuji dalam penelitian ini yaitu debt to equity ratio, dan profitabilitas. Populasi dari penelitian ini menggunakan 168 perusahaan manufaktur yang konsisten terdaftar di Bursa Efek Indonesia periode tahun 2015-2017 yang diambil dengan menggunakan metode purposive sampling. Faktor-faktor tersebut kemudian diuji dengan menggunakan regresi logistic pada tingkat signifikansi 5 persen. Hasil penelitian mengidentifikasi bahwa debt to equity ratio, profitabilitas tidak berpengaruh pada ketepatan waktu pelaporan keuangan perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia.
Residual-Gated Attention U-Net with Channel Recalibration for Polyp Segmentation in Colonoscopy Images Tanuwijaya, William; Yohannes
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/4qmfa987

Abstract

This study proposed a modification to the Attention U-Net architecture by integrating a Residual-Gated mechanism and Squeeze-and-Excitation (SE) Block-based channel recalibration within the Attention Gate to enhance feature selectivity in polyp segmentation. This integration reinforces both spatial and channel attention, enabling the model to better highlight polyp regions while suppressing irrelevant background features. Experiments were conducted on three colonoscopy datasets, CVC-ClinicDB, CVC-ColonDB, and CVC-300, using IoU and DSC metrics. Compared to the Attention U-Net baseline, the proposed model achieves noticeable improvements, with performance gains of mIoU 0.0043 and mDSC 0.0094 on CVC-ClinicDB, mIoU 0.0012 on CVC-ColonDB, and a larger margin of mIoU 0.0224 and mDSC 0.0127 on CVC-300. The best results were obtained on CVC-ClinicDB (mIoU 0.8889, mDSC 0.9412). Although the absolute scores on CVC-ClinicDB and CVC-ColonDB are lower than those reported in several recent studies, these datasets contain higher variability in polyp size, boundary ambiguity, and illumination, contributing to more challenging segmentation conditions. Visual evaluation further shows smoother and more coherent boundaries, especially on small or low-contrast polyps. Overall, the integration of the residual-gated mechanism and SE block within the attention gate effectively improves model accuracy and generalization, particularly in challenging scenarios.
Co-Authors Arniman Zebua Selfie Gultom Tanuwijaya, William