Article Per Year (5 Year)
p-Index From 2021 - 2026
2.165
P-Index
This Author published in this journals
All Journal CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Jurnal Teknoinfo Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Journal of Computer System and Informatics (JoSYC) Operations Research: International Conference Series Prosiding Konferensi Nasional PKM-CSR Jurnal Aplikasi Bisnis dan Komputer Journal of Engineering and Information Technology for Community Service Smart Techno (Smart Technology, Informatic and Technopreneurship) J-Icon : Jurnal Komputer dan Informatika Jurnal Teknoinfo
Arie Qur’ania
Universitas Pakuan
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Nursing Public Health Social Sciences Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Teknoinfo

 1 
SISTEM ACCESS CONTROL RUANGAN LABORATORIUM DAN PERKULIAHAN MENGGUNAKAN RADIO FREQUENCY IDENTIFICATION Dian Kartika Utami; Akbar Sugih Miftahul Huda; Arie Qur’ania; Rizki Pratama
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.1897

Abstract

Access control adalah suatu cara untuk pengendalian terhadap akses seseorang dalam memasuki ruangan. Pengendalian ini berfungsi untuk membatasi seseorang yang memiliki hak atau tidak dalam memasuki sebuah ruangan. Resiko keamanan yang dapat terjadi adalah kerusakan aset didalam ruangan yang timbul karena ada orang yang dapat mengakses ruangan dan menggunakan aset didalamnya. Penyebab kerusakan atau kehilangan ini akan dapat dilacak dengan menggunakan log book yang diisi pada setiap mengakses ruangan. Dengan resiko kerusakan dan kehilangan akibat lemahnya pengendalian akses ruangan. Dibutuhkan sebuah sistem yang dapat mengunci dan terbuka secara otomatis dengan pengenal yang dimiliki oleh orang-orang yang berhak mengakses ruangan. Selain itu diperlukan sebuah sistem yang dapat mencatat akses setiap orang yang memiliki hak sehingga resiko kerusakan dan kehilangan aset dapat dilacak.
EKSTRAKSI CITRA MENGGUNAKAN METODE LAPLACIAN DAN SVM (SUPPORT VECTOR MACHINE) UNTUK IDENTIFIKASI JENIS TANAMAN PAKU BERDASARKAN CITRA SPORA Sufiatul Maryana; Herfina Herfina; Arie Qurania; Herlinda Herlinda
Jurnal Teknoinfo Vol 17, No 2 (2023): Vol 17, No 2 (2023) : JULI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v17i2.2579

Abstract

Spores are a breeding tool for ferns (Pteridophyta) which are generally found under the surface of each leaf. Research on the characteristics of Pteridophyta spores is generally done by observing their size and shape to see the type of spore. The method that will be carried out in this study requires a lot of understanding, experience, accuracy and time to achieve high accuracy in determining the type. Based on these reasons, it is necessary to develop another technique with modeling to help identify the type of Pteridophyta in the spore. This paper discusses image extraction for fern identification based on spore images using the Laplacian method and SVM. Laplacian method is used to extract features from spore images, while SVM is used to classify ferns. This method requires a lot of understanding, experience, accuracy and time to achieve high accuracy in determining the type. Based on these reasons, it is necessary to develop another technique with modeling to help identify the type of Pteridophyta in the spore. The purpose of this research is to make Support Vector Machine (SVM) modeling to identify fern species based on spore images in Eigen space. The data used are spore images with four classes, each of which has 24 data per class with a total of 96 images. The method used in this research is Laplacian method for feature extraction and SVM for data classification using Gaussian RBF and Polynomial kernel function experiments. The result of this research is the accuracy of each extraction that has been tried. The best result lies in the RBF kernel function with 70% feature extraction and 10 parameters with an accuracy of 98.96%.
Co-Authors Abhishek R Mehta Achmad Noerkhaerin Putra Adriana Sari Aryani Agung Prajuhana Putra Agung Prajuhana Putra Agus Ismangil Ahmad Ari Aldino Akbar Sugih Miftahul Huda Alexander A. S. Gunawan, Alexander A. S. Alexander Agung Santoso Gunawan Andi Chairunnas Anggraeni, Ade Irma Asep Saepulrohman Boldson H. Situmorang Dedi Darwis Dian Kartika Utami Dinar Munggaran Akhmad Erika Candra Erniyati Erniyati Gustian Rama Putra Gustian Rama Putra Hakiim, Reyhan Herfina Herfina Herlinda Herlinda Hermawan Hermawan Hidayat, Ari I Wayan Sriyasa Indra Fauzi Rahman Irma Anggraeni Iyan Mulyana Iyan Mulyana Kotim Subandi Lili Ayu Wulandhari Lita Karlitasari Lita Karlitasari M. Iqbal Suriansyah M. Rivan Padila Maryanto, Dani Aisak Mohamad Iqbal Mohamad Iqbal Suriansyah Iqbal Muhammad Zuhri Mulyati , Mulyati Mulyati Mulyati Mulyati Nazar Muhamad Ikhbal Perdi Yansyah perdi Prihastuti Harsani Puspa Citra Rahman, Indra Fauzi Rizki Pratama Saputri, Cita Suci Sesilia Novita R Setiawansyah Setiawansyah Siska Andriani Sitna Hajar Hadad Sudrajat, Cecep Sufiatul Maryana Syarif Hidayatullah Tjut Awaliyah Triasti nurmiatiningsih Triastinurmiatiningsih, Triastinurmiatiningsih Victor Ilyas Sugara Wulandhari, Lili Ayu Yuri Rahmanto Yusma Yanti Zolla Zolla