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All Journal JUTIK : Jurnal Teknologi Informasi dan Komputer
Neysa Talitha Jehian
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Social Sciences

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SIMULASI SERANGAN SIBER MAC ADDRESS DAN IP ADDRESS SPOOFING PADA JARINGAN HTTP DI KALI LINUX Muhammad Rizki Andrian Fitra; Neysa Talitha Jehian; Lastri Elisabet Butarbutar; Dedy Kiswanto
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3766

Abstract

The rapid development of information technology presents a big challenge in terms of network security, especially against cyber-attacks. This research simulates MAC Address Spoofing attack on HTTP network using Kali Linux operating system. The purpose of this research is to understand how the attack process is carried out and evaluate its impact on communication security in an unencrypted network. The attacker impersonated a gateway through ARP Spoofing technique and successfully infiltrated the communication path between the victim and the server. The captured data shows that sensitive information such as usernames and passwords can be easily retrieved when the victim accesses HTTP sites. The results show that spoofing has been successfully carried out, as evidenced by the acquisition of usernames and passwords when victims access HTTP sites.
PENGEMBANGAN APLIKASI BERBASIS ARTIFICIAL INTELLIGENCE DALAM REKOMENDASI JALUR PENDIDIKAN BERDASARKAN MINAT DAN KEMAMPUAN SISWA M. Rizki Andrian Fitra; Neysa Talitha Jehian; Delvita Aulia Artika; Bunga Dwi Febrianti; Adidtya Perdana
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3959

Abstract

Many high school and vocational students in Indonesia experience confusion when choosing a college major due to a lack of understanding of their own potential and limited access to relevant information. This study aims to develop an Artificial Intelligence (AI)-based major recommendation system that is personal, adaptive, and transparent. The system is designed using a Hybrid Recommendation System approach, combining Content-Based Filtering, Rule-Based System, and a Weighted Scoring Algorithm, with weights based on hobbies, academic grades, favorite subjects, personality, and career aspirations. The technologies used include Laravel (backend), Vue.js (frontend), and Python API for the AI component. Trial results with 15 students showed that over 60% of respondents found the system very helpful, while over 30% found it moderately helpful and felt the recommendations aligned with their interests and goals, indicating the system’s effectiveness in supporting educational decision-making. The system is also flexible for further development in terms of both datasets and algorithms. Future enhancements include the integration of personality tests such as MBTI, implementation of feedback-based machine learning, and cross-school testing for broader validation. This system is expected to become a data-driven educational solution that supports digital transformation in the education sector.
Co-Authors Adidtya Perdana, Adidtya Bunga Dwi Febrianti Dedy Kiswanto Delvita Aulia Artika Lastri Elisabet Butarbutar M. Rizki Andrian Fitra Muhammad Rizki Andrian Fitra