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All Journal METIK JURNAL JUTIS : Jurnal Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Informatics and Digital Expert (INDEX) Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma
Nurul Sabrina, Puspita
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Mathematics

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Journal : Jurnal Algoritma

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Peningkatan Klasifikasi Serangan DDoS pada SDN Menggunakan XGBoost dan RAMOBoost Badar, Ahmad; Rakhmat Umbara, Fajri; Nurul Sabrina, Puspita
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2460

Abstract

The aim of this study is to detect Distributed Denial of Service (DDoS) attacks in Software Defined Networking (SDN) environments using the XGBoost algorithm and the RAMOBoost balancing technique to address the issue of data imbalance. SDN offers flexibility in network management but remains vulnerable to DDoS attacks. The dataset used in this research consists of two classes (normal and attack) with an imbalanced distribution. XGBoost was chosen for its ability to deliver accurate predictions, while RAMOBoost was employed to enhance data representation for the minority class. The results show that before balancing, the model achieved 100% precision for the majority class and 96% precision for the minority class, with recall values of 97% and 100%, respectively. After applying RAMOBoost, precision and recall became more balanced, ranging between 97%–99%, while maintaining a high overall accuracy of 98%. Grouped Feature Importance analysis revealed that randomizing important features reduced accuracy from 97.88% to 49.78%, whereas randomizing unimportant features only slightly decreased accuracy to 97.82%. The main contribution of this study lies in the combined application of RAMOBoost and XGBoost, which proved effective in improving classification performance on imbalanced datasets, and in emphasizing the critical role of feature selection in maintaining model stability. These findings provide valuable insights for network administrators in developing effective attack detection systems for SDN environments.
Analisis Sentimen Ulasan Aplikasi CapCut Menggunakan Model RoBERTa Dengan Fitur Ekstraksi Word2vec Budiman, Firman Nur; Witanti, Wina; Nurul Sabrina, Puspita
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2480

Abstract

To improve the accuracy of sentiment classification in CapCut app reviews, this study tested a hybrid model built from a combination of RoBERTa and Word2Vec. A total of 5,000 reviews from the Google Play Store were used as a dataset, which was then processed through data cleaning, tokenization, and stopword removal stages. Next, the EDA oversampling technique was used to address the issue of class distribution imbalance. The proposed model architecture works by combining the concatenation of vector features from Word2Vec for local word meaning representation and RoBERTa for overall sentence context understanding. Model evaluation showed an accuracy of 80%, a higher result compared to the 79% accuracy obtained by the single RoBERTa baseline model. This study concludes that combining contextual and semantic feature representations effectively results in better sentiment classification performance.
Co-Authors Aditya Bahrul 'Alam, Moch Andhika, Firza Rafiandi Ashaury, Herdi Badar, Ahmad Budiman, Firman Nur Devian, Devian Edvin Ramadhan Engko M, Galih Yuga Pangestu Fajri Rakhmat Umbara Gikaldi Arbiyan Septuri Hadiana, Asep Id Id Hidiana, Asep Khoirullah, Debri Komarudin, Agus Krishna Putra, Eddie Mushofy Anwary, Azy Nabiel Syahreza, Fabian Putra Permana, Daffa Raihan Martin Permana Ridwan Ilyas Rizki Kurniawan, Moch. Santana, Sony Tomagola, Inda Sari Wina Witanti