Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah KOMPUTASI J-Intech (Journal of Information and Technology)
Sinaga, Elya Juni Arta
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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

Found 2 Documents
Search

 1 
Sistem Pendukung Keputusan Pemilihan Laptop dengan Menggunakan Metode Analytical Hierarchy Process Mualo, Alfandi; Sinaga, Elya Juni Arta; Ikbar, Fawwaz
Jurnal Ilmiah Komputasi Vol. 23 No. 3 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 3, September 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.3.3608

Abstract

Kebutuhan akan laptop terus meningkat sehingga menyebabkan banyak produk  dan merek laptop beredar dan berlomba-lomba menjadi yang terbaik. Dengan memperhatikan spesifikasi laptop yang ada seperti Monitor, Tipe Penyimpanan, RAM, CPU, dan Grafic Card kita ingin menemukan laptop dengan spesifikasi terbaik. Dengan menggunakan sistem pendukung keputusan dan  metode AHP kita dapat menyelesaikan permasalahan tersebut. Dari kasus ini, dengan 5 alternatif fan kriteria yang digunakan, didapat hasil yaitu Acer Aspire Vero AV15-51 dengan nilai 0,2595 menjadi ranking pertama dalam penelitian ini. Selanjutnya disusul oleh Asus K513 dengan nilai 0,1973, Hp pavilion dengan nilai 0,1971, Acer Swift dengan nilai 0,1919 dan Asus Vivobook menjadi ranking terakhir dengan nilai 0,1539.
Identifikasi Tanda Tangan Dengan Menggunakan Metode Convolution Neural Network (CNN) Indriani.S, Dechy Deswita; Sinaga, Elya Juni Arta; Oktavia, Grace; Syahputra, Hermawan; Ramadhani, Fanny
J-INTECH (Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1273

Abstract

This research aims to develop and evaluate a Convolutional Neural Network (CNN) model for signature identification. The CNN method is chosen for its capability to extract and analyze complex visual features from signature images. The data used in this study consists of a collection of signature images divided into training and testing sets. The proposed CNN model comprises several convolutional, pooling, and fully connected layers optimized for classification tasks. Evaluation results indicate that the CNN model achieves excellent performance with an accuracy of 0.97, demonstrating high accuracy and precision in signature recognition. With these results, CNN proves to be an effective and reliable method for signature identification, making a significant contribution to the field of biometric identity verification. These findings open opportunities for further applications in security and authentication systems requiring automatic signature recognition.
Co-Authors Hermawan Syahputra Ikbar, Fawwaz Indriani.S, Dechy Deswita Mualo, Alfandi Oktavia, Grace Ramadhani, Fanny