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All Journal Jurnal Ilmiah Matrik JOURNAL OF APPLIED INFORMATICS AND COMPUTING JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Teknologi Terpadu Jurnal Tekno Kompak Building of Informatics, Technology and Science JIKA (Jurnal Informatika) Informatics and Digital Expert (INDEX) Jurnal Ilmiah Wahana Pendidikan Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Enrichment: Journal of Multidisciplinary Research and Development
Puspita Nurul Sabrina, Puspita Nurul
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Religion Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Klasifikasi Website Phishing Menggunakan Metode X-Gboost dengan Teknik Penyeimbang Data Radial Based Undersampling Yoga, Yoga; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7920

Abstract

Phishing websites are one of the most prevalent forms of cyberattacks and have the potential to cause significant losses, both financially and non-financially. Automatic phishing detection using machine learning algorithms has become an effective solution to address this threat. This study aims to classify phishing websites using the Extreme Gradient Boosting (XGBoost) algorithm and to address the issue of class imbalance by applying the Radial Based Undersampling (RBU) method. In addition, hyperparameter tuning was performed using the Random Search method to optimize the model's performance. The dataset used was obtained from the Kaggle platform and exhibits an imbalanced class distribution, where the number of non-phishing instances far exceeds phishing instances. This imbalance can lead to a biased model and reduce its ability to detect minority class patterns. Based on the evaluation results, the application of RBU significantly improved the model’s capability in detecting phishing instances, while hyperparameter tuning further enhanced its accuracy. The best model was achieved through a combination of RBU and Random Search, reaching an accuracy of 90.39% on the test data. These findings indicate that the combined approach of data balancing and model optimization provides an effective solution for phishing website classification and can be applied to similar cases in the field of cybersecurity.
Analisis Sentimen Terhadap Cyberbullying di Twitter (X) Menggunakan Improved Word Vectors dan Bert Nusantara, Madya Dharma; Umbara, Fajri Rakhmat; Sabrina, Puspita Nurul
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7968

Abstract

Text mining is an important approach in analyzing text data, particularly for detecting negative sentiments such as cyberbullying on social media. Twitter (X), as an open platform, often serves as a space for the proliferation of hate speech and abusive behavior recorded in text form. This study aims to improve the performance of sentiment classification models on Twitter (X) data by combining the Improved Word Vector (IWV) and Bidirectional Encoder Representations from Transformers (BERT) methods, evaluated using precision, recall, and F1-score metrics. The dataset used consists of 9,874 Indonesian-language tweets labeled into three categories: Hate Speech (HS), Abusive, and Neutral. This data is sourced from previous research and is the result of re-annotation of the original dataset of 13,169 tweets. IWV is formed from a combination of Word2Vec, GloVe, POS tagging, and emotion lexicon features designed to enrich word representation semantically. The preprocessing process is carried out through several important stages, namely tokenization, filtering, stemming/lemmatization, and normalization. The IWV extraction results were then combined with BERT embedding through concatenation to produce high-dimensional vector representations. Evaluation was performed using precision, recall, and F1-score metrics. The test results showed that the combined IWV+BERT model was able to produce better performance than BERT alone. The use of data that has been balanced through balancing techniques also contributed to the improvement in accuracy, with the highest accuracy value reaching 91%. This finding indicates that the integration of word representation features from IWV and sentence context from BERT can improve the effectiveness of text mining in sentiment analysis related to cyberbullying on social media
Co-Authors Aditya, Aldy Ashaury, Herdi Athallah, Sulthan Edvin Ramadhan Fajri Rakhmat Umbara Firmansyah, Rolan Hasna, Aisyah Nur Ibadirachman, Rifqi Karunia Junior, Rifqi Pratama Mubarak, Muhammad Munzir Rizkya Nurhaliza, Nabillah Nusantara, Madya Dharma Pikriyansah, Reji Rahmah, Revina Nur Ramadhani, Desnantia Eka Ria Andriani Santikarama, Irma Syauqi, Rofiq Muhammad Umbara, Fajri Rahmat Wirianto, Rahmadi Dimas Yazid, Rija Muhamad Yoga, Yoga Yulison Herry Chrisnanto