Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal TEKNOLOGI INFORMASI Jurnal Informatika Dan Tekonologi Komputer
Rafael Austin
Universitas Esa Unggul
Computer Science & IT Control & Systems Engineering Other

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

Found 2 Documents
Search

 1 
Analisis Perbandingan Sensitivitas dan Kinerja K-Means dan DBSCAN Terhadap Variasi Noise Rafael Austin; Ricky Dwi Putra; Ardi Ardi; Fuzail Fazle Rabbi; Lucky Pujiono WS; Vitri Tundjungsari
Teknologi Informasi : Teori, Konsep, dan Implementasi : Jurnal Ilmiah Vol 17 No 1 (2026): JURNAL TEKNOLOGI INFORMASI: Teori, Konsep dan Implementasi
Publisher : STMIK PPKIA Pradnya Paramita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36382/qq0w1525

Abstract

Inaccurate or noisy data presents a significant challenge in machine learning, particularly in unsupervised clustering tasks. This study evaluates the robustness and performance of two popular clustering algorithms, K-Means and DBSCAN, against various levels of Gaussian noise (5%, 10%, 20%, and 30%) injected into a customer dataset. Evaluation was conducted using Silhouette Score and Davies-Bouldin Index (DBI). Initial results indicated that DBSCAN performed slightly better with a Silhouette Score of 0.4817 compared to K-Means at 0.4101. However, after noise injection, K-Means demonstrated superior stability by maintaining more consistent cluster memberships, whereas DBSCAN was more sensitive to distance variations, leading to significant fluctuations in cluster assignments. The study concludes that K-Means is more reliable for datasets where cluster integrity must be preserved despite minor data irregularities.
Optimalisasi Summarization Berita BBC dengan Metode BiLSTM-Transformer Rafael Austin; Andhika Dwi Rachmawanto; Michael Jeconiah Yonathan; M Naufal Arriz; Vitri Tundjungsari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10669

Abstract

The rapid growth of digital news, such as that from the BBC, presents challenges for readers in absorbing dense information within limited time. This research proposes an automated text summarization system using a hybrid BiLSTM Transformer architecture to produce concise yet contextually accurate summaries. The model integrates BiLSTM to capture local sequential relationships and Transformer’s self-attention mechanism to handle global context, overcoming the computational limitations of standalone Transformers. Utilizing a self-embedding approach, the system processes text in an unsupervised manner, making it suitable for datasets without ground truth summaries. Evaluation was conducted using 50 samples from the Xsum dataset and 25 live BBC news links, with performance measured via cosine similarity to assess contextual preservation. The results demonstrated a consistent average cosine similarity of 0.7959 for dataset samples and 0.7877 for new data. These findings indicate that the hybrid model effectively maintains semantic integrity and provides reliable summaries for complex news articles.
Co-Authors Andhika Dwi Rachmawanto Ardi Ardi Fuzail Fazle Rabbi Lucky Pujiono WS M Naufal Arriz Michael Jeconiah Yonathan Ricky Dwi Putra Vitri Tundjungsari