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All Journal Data Sciences Indonesia (DSI) Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Luftia Rahma Nasution
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Computer Science & IT Education Physics

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Deteksi Berita Hoax Pada Platform X Menggunakan Pendekatan Text Mining dan Algoritma Machine Learning Dewi, Aulia Kartika; Noni Fauzia Rahmadani; Syahputri, Rifdah; Luftia Rahma Nasution; Furqon, Mhd
Data Sciences Indonesia (DSI) Vol. 5 No. 1 (2025): Article Research Volume 5 Issue 1, June 2025
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v5i1.6011

Abstract

The spread of hoax news on digital platforms is increasingly becoming a serious concern as it can trigger mass disinformation and potentially cause widespread social disruption. Platform X, as one of the most widely used social media, is often used as a means of spreading unverified information. Therefore, an automatic detection system is needed that is able to identify hoax news effectively and efficiently. This research aims to build a text-based hoax news classification model by applying a text mining approach and the Support Vector Machine (SVM) algorithm. The dataset used comes from Platform X and has gone through a series of preprocessing stages, including case folding, tokenization, stopword removal, and stemming. The feature extraction process is performed using the Term Frequency-Inverse Document Frequency (TF-IDF) method to convert text into numerical representations that can be processed by the SVM algorithm. The built model is then evaluated using several performance metrics, such as accuracy, precision, recall, and F1-score. The evaluation results show that the SVM model is able to classify hoax news with 83.2% accuracy, 81.5% precision, 84.7% recall, and 83.0% F1-score. This finding shows that the SVM algorithm is quite reliable in detecting text-based hoax news and has the potential to be implemented as a solution to mitigate the spread of false information on digital platforms more broadly.
Perbandingan Waktu Pemecahan Password Menggunakan Algoritma Hash MD5, SHA-256, dan SHA-512 pada Serangan Brute Force Nur Bainatun Nisa; Noni Fauzia Rahmadani; Aulia Kartika Dewi; Luftia Rahma Nasution; Dzilhulaifa Siregara; Rifdah Syahputri; 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.v4i1.926

Abstract

Password security is a critical component in protecting information systems, as passwords are often the primary target of various attacks, particularly brute force attacks. A brute force attack works by systematically attempting all possible character combinations until the correct password corresponding to a stored hash value is found. Therefore, the choice of an appropriate hash algorithm plays a significant role in determining a system’s resistance to such attacks. This study aims to analyze and compare the password cracking time of MD5, SHA-256, and SHA-512 hash algorithms under brute force attack scenarios. The research methodology involves generating hash values from a set of test passwords using each hash algorithm, followed by conducting brute force attacks to recover the original passwords based on the generated hash values. The collected data are analyzed by measuring the time required to crack passwords for each algorithm. The results indicate that MD5 has the fastest cracking time compared to SHA-256 and SHA-512, indicating a lower level of resistance to brute force attacks. SHA-256 demonstrates better security than MD5 but remains less resistant when compared to SHA-512. The SHA-512 algorithm requires the longest cracking time, reflecting the highest level of resistance to brute force attacks among the tested algorithms. In conclusion, hash algorithms with larger bit lengths provide stronger protection against brute force attacks and are more suitable for secure password storage in information systems.
Co-Authors Aulia Kartika Dewi Dewi, Aulia Kartika Dzilhulaifa Siregara Furqon, Mhd Ibnu Rusydi Noni Fauzia Rahmadani Nur Bainatun Nisa Rifdah Syahputri Syahputri, Rifdah