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All Journal JUTIK : Jurnal Teknologi Informasi dan Komputer
Bunga Dwi Febrianti
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Social Sciences

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IMPLEMENTASI VPN DIATAS ROUTING OSPF DALAM MEMBANGUN SIMULASI JARINGAN MAN Nezza Anggraini Yolandari; Delvita Aulia Artika; Bunga Dwi Febrianti; 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.v11i2.3773

Abstract

This study aims to compare the effectiveness of the Virtual Private Network (VPN) Ipsec and GRE tunnel protocols in establishing site-to-site VPN connections on a Metropolitan Area Network (MAN) optimized with Open Shortest Path First (OSPF) routing. Using Cisco packet tracer software, simulations were conducted involving seven routers as representations of different locations, connected via VPN connections. The simulation results show that although Ipsec offers significant security through data encryption, its limitations in multicast delivery pose challenges in OSPF communications. In contrast, GRE tunnel supports multicast route delivery required for OSPF, making it a more efficient choice in this context. This study provides insight into the advantages and disadvantages of each protocol for implementing a secure and efficient network.
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 Dedy Kiswanto Delvita Aulia Artika M. Rizki Andrian Fitra Neysa Talitha Jehian Nezza Anggraini Yolandari