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All Journal Deli Sains Informatika Jurnal Riset Multidisiplin Edukasi
Aulia Ichsan
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PERPADUAN METODE QUEUE TREE DAN FL7 DALAM EFISIENSI PENGGUNAAN BANDWIDTH BERBASIS ROUTER Aulia Ichsan; Muhammad Zulfansyuri Siambaton
Deli Sains Informatika Vol. 1 No. 2 (2022): Artikel Riset Juni 2022
Publisher : LPPM Universitas Deli Sumatera

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

Abstract

The availability of internet bandwidth management on the UISU campus is considered necessary for further design, because the available bandwidth management has a simple concept and the use of bandwidth is deemed to be more efficient. Some of the impacts are that the distribution of bandwidth is not more structured (each client does not have a definite bandwidth guarantee when the number of bandwidth users/traffic is dense, fellow internet users overlap each other resulting in a tendency to gain more bandwidth on certain clients only (not equally), pattern filtering data such as streaming traffic or certain websites are easy to access, resulting in no opportunity to save bandwidth usage. The solutions for some of these indications have been carried out by previous researchers but are not efficient enough if applied to large campus networks such as UISU. Therefore this research will develop using a combination of methods, namely Queue Tree by utilizing traffic priority, Commited Information Rate/Maximum Information Rate and the FL7 method to define certain data patterns in the text url so that the use of bandwidth availability is more efficient. using the average value parameter (avg rate) using a mikrotik OS router and packet ICMP parameters using throughput, delay, jitter and packet loss. Keywords: The combination of Queue Tree and FL7 methods; bandwidth efficiency; parameter.
PEMINDAI KERENTANAN APLIKASI WEB DINAS KEARSIPAN DAN PERPUSTAKAAN DAERAH KABUPATEN SEMARANG MENGGUNAKAN INFORMATION SYSTEM SECURITY ASSESSMENT FRAMEWORK (ISSAF) Muhammad Fauzan Rifqi; Oris Krianto Sulaiman; Aulia Ichsan
Jurnal Riset Multidisiplin Edukasi Vol. 2 No. 7 (2025): Jurnal Riset Multidisiplin Edukasi (Edisi Juli 2025)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v2i7.720

Abstract

Dalam era digital saat ini, website instansi pemerintah menjadi media utama dalam menyampaikan informasi kepada masyarakat. Namun, peningkatan penggunaan teknologi juga dibarengi dengan meningkatnya ancaman siber. Penelitian ini bertujuan untuk mengidentifikasi dan menguji kerentanan keamanan pada website Dinas Kearsipan dan Perpustakaan Daerah Kabupaten Semarang dengan fokus terhadap serangan SQL Injection dan Cross-Site Scripting (XSS). Penelitian menggunakan metode Information System Security Assessment Framework (ISSAF) yang meliputi tahap planning, assessment, dan reporting. Proses pengujian dilakukan dalam lingkungan terkendali menggunakan berbagai tools seperti WhatWeb, WHOIS, NMAP, Arachni, dan SQLMap. Hasil penelitian menemukan lima kerentanan, di antaranya empat dengan tingkat risiko tinggi dan satu berisiko rendah. Dua di antaranya berhasil dieksploitasi, yaitu SQL Injection dan XSS Reflected. Temuan ini menunjukkan bahwa website tersebut masih rentan terhadap serangan siber dan memerlukan perbaikan keamanan segera. Penelitian ini juga memberikan rekomendasi mitigasi untuk setiap kerentanan yang ditemukan guna meningkatkan perlindungan sistem.
Implementasi Algoritma Deep Learning Pada Aplikasi Penerjemah Suara Otomatis Indonesia-Jepang Online Vega Fajar Habibi; Khairuddin Nasution; Aulia Ichsan
Jurnal Riset Multidisiplin Edukasi Vol. 2 No. 7 (2025): Jurnal Riset Multidisiplin Edukasi (Edisi Juli 2025)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v2i7.723

Abstract

In the era of globalization, information and communication technology has rapidly developed, driving changes in various aspects of life, including the way people communicate. One of the main challenges is cross-linguistic communication, particularly in understanding foreign languages such as Japanese, which uses characters that are different from the Latin alphabet. This study develops a web-based automatic voice translation application that can recognize speech, translate it automatically, and generate human-like speech based on the translation results. The application utilizes three main technologies: Speech to Text, Machine Translation, and Text to Speech. Speech to Text and Text to Speech are implemented using the Web Speech API, while Machine Translation is implemented using the Google Translation API. The Web Speech API uses Recurrent Neural Network (RNN), and the Google Translate API uses Transformer, both of which are methods from Deep Learning algorithms. This application is designed to facilitate cross-lingual communication without the need for typing, manually translating, or directly speaking in a foreign language.
Co-Authors Khairuddin Nasution Muhammad Fauzan Rifqi Muhammad Zulfansyuri Siambaton Oris Krianto Sulaiman Vega Fajar Habibi