Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Imajinasi: Jurnal Ilmu Pengetahuan, Seni, dan Teknologi Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Tiara Ayu Triarta Tambak
Unknown Affiliation
Arts Computer Science & IT

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

Found 2 Documents
Search

 1 
Analisis Sentimen Pengguna terhadap Integrasi AI pada Platform Pembelajaran Online Tiara Ayu Triarta Tambak
Imajinasi : Jurnal Ilmu Pengetahuan, Seni, dan Teknologi Vol. 2 No. 4 (2025): Desember : Imajinasi : Jurnal Ilmu Pengetahuan, Seni, dan Teknologi
Publisher : Asosiasi Seni Desain dan Komunikasi Visual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/imajinasi.v2i4.939

Abstract

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered
Pembatasan Laju Adaptif Berbasis Verifiable Delay Function untuk Mitigasi Penyalahgunaan API pada Gateway Edge Ringan Diah Putri Kartikasari; Tiara Ayu Triarta Tambak; Agung Nugroho; Ibnu Rusydi
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 1 (2026): Januari : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i1.931

Abstract

API abuse on lightweight edge gateways has intensified as microservice-based services expose many REST endpoints to heterogeneous clients. Conventional per-identity rate limiting, such as static token buckets, is frequently bypassed through distributed bots and identity rotation, while legitimate burst traffic may be rejected and degrade user experience. This study proposes Adaptive Rate Limiting with Verifiable Delay Functions (ARL-VDF), which couples a lightweight risk score with selective VDF challenges to impose a tunable sequential-computation cost on suspicious clients without forcing aggressive dropping for low-risk users. The gateway continuously derives a per-identity risk score from short-window request rate, error tendency, and identity freshness, then maps the score to a target delay bounded by  and . Evaluation uses a 600-second discrete-event simulation on a mixed workload consisting of normal clients, legitimate bursts, and distributed attackers. Compared with a static token bucket baseline, ARL-VDF maintains full success for legitimate traffic, reduces attacker throughput that passes the gateway, and keeps verification overhead within a fixed budget on the edge device. The results indicate that combining adaptive control with verifiable sequential cost can improve availability and fairness on resource-constrained edge gateways without resorting to aggressive dropping.
Co-Authors Agung Nugroho Diah Putri Kartikasari Ibnu Rusydi