Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Jurnal Manajemen Bisnis Digital Terkini
Alfa Renaldo Aluska
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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

Found 2 Documents
Search

 1 
Analisis Faktor-Faktor Pemilihan Lokapasar Favorit Di Kalangan Mahasiswa ITS Jason Ho; Alfa Renaldo Aluska; Aulisa Rizki Amanda; Nur Aini Rakhmawati
Jurnal Manajemen Bisnis Digital Terkini Vol. 1 No. 3 (2024): Juli : Jurnal Manajemen Bisnis Digital Terkini (JUMBIDTER)
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jumbidter.v1i3.201

Abstract

In the midst of internet advancement, the presence of marketplaces brings many positive impacts. Local marketplace users are predominantly coming from Generation Z, and the majority of ITS students, as observed during this research, is part of the group. The purpose of this study is to identify the most favorite marketplace and the factors influencing the preference of these favorite marketplaces among ITS students. The preference factors are based on 13 factors that affect shopping behavior. The method employed is a survey-descriptive using 55 respondents in answering the questionnaire. The research results indicate that advertising and communication, certification and security, and warranty are the three factors with the highest urgency in choosing a favorite local market. Additionally, discount, website, and review and complain are the three factors with the lowest urgency. This study reveals that consumers or users prioritize the value offered by marketplaces over factors such as price and discounts.
Analisis Peran Gen AI dalam Penetration Testing: Studi Kasus Mesin VulnHub Menggunakan GPT-4.1 dan Kali Linux Ho, Jason; Ramadhan, Dimas Fajar; Aluska, Alfa Renaldo
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 3 No. 3 (2025): Agustus: Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v3i3.502

Abstract

Along with the increasing threat of cybercrime, which is predicted to cause losses of up to US$10.5 trillion by 2025 , penetration testing (pentest) has become a crucial strategy for identifying security vulnerabilities. However, the manual pentest process is often time-consuming. This research aims to analyze the role, effectiveness, and challenges of Generative AI (GenAI), specifically GPT-4.1, in accelerating and optimizing the penetration testing process. This research method uses a qualitative approach with a case study on the "PumpkinFestival" VulnHub machine , where GPT-4.1 is integrated into the Kali Linux environment through the ShellGPT tool. The results show that GPT-4.1 can significantly accelerate all stages of the pentest, from reconnaissance to exploitation. GenAI proved effective in analyzing scan results, composing specific payloads, and creating decryption scripts quickly and accurately , while also filling a research gap by evaluating a newer AI model compared to previous studies. The implication is that the integration of GenAI in cybersecurity has great potential to increase the efficiency and effectiveness of security teams in facing increasingly complex threats.
Co-Authors Aulisa Rizki Amanda Ho, Jason Jason Ho Nur Aini Rakhmawati Ramadhan, Dimas Fajar