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Andi Rukmana
Universitas Insan Pembangunan Indonesia
Computer Science & IT

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KEAMANAN SIBER DALAM ERA INTERNET OF THINGS: TANTANGAN DAN SOLUSI TEKNOLOGI TERKINI Ferdi Kuswandi; Andi Rukmana; Adiyanto
IPSIKOM Vol. 13 No. 1 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i1.417

Abstract

The rapid expansion of the Internet of Things (IoT) has transformed industries but also heightened cybersecurity vulnerabilities. Cyber threats, including ransomware, data breaches, and distributed denial-of-service (DDoS) attacks, increasingly jeopardize critical infrastructure. Traditional security methods, such as encryption and firewalls, often fail to counter evolving AI-driven threats. This study introduces an AI-based security model that integrates deep learning and federated learning for real-time IoT threat detection and mitigation. The proposed system employs a hybrid CNN-LSTM architecture to analyze network traffic, while federated learning enhances detection accuracy and ensures data privacy. Experimental results demonstrate 92% detection accuracy, 4.2% false positive rate, and latency under 50 ms, outperforming conventional rule-based systems. Additionally, integrating AI with IoT protocols like MQTT and CoAP optimizes processing for low-power devices. The study highlights regulatory challenges, as 73% of industrial organizations lack AI-driven security policies. The proposed framework aligns with NIST SP 800-82 and GDPR, ensuring scalable and adaptive industrial cybersecurity solutions. These findings contribute to developing AI-driven security strategies, providing a foundation for enhancing IoT resilience against evolving cyber threats.
ANALISIS SENTIMEN MASYARAKAT TWITTER TERHADAP KEBIJAKAN EFISIENSI ANGGARAN KEMENTERIAN MENGGUNAKAN SVM Muhammad Arif Kurniawan; Samsul Makin; Angger Styo Yuniarti; Andi Rukmana
IPSIKOM Vol. 14 No. 1 (2026): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v14i1.452

Abstract

Sentiment analysis of the ministry's budget efficiency policy is crucial to understanding public responses to government policies. This study employs the support vector machine (SVM) method to classify positive and negative sentiments from 1,418 tweets collected through crawling using Twitter API v2 between February 10 and 22. The text processing steps include case folding, cleaning, tokenizing, stopword removal, stemming, and weighting using the term frequency-inverse document frequency (TF-IDF) method. The analysis results indicate that negative sentiment dominates over positive sentiment, reflecting public criticism and dissatisfaction with the policy. The SVM model was evaluated using k-fold cross-validation with k values ranging from 2 to 10, achieving the best accuracy of 94.76% with 10-fold validation. Evaluation using the confusion matrix showed a precision of 92.85%, a recall of 91.32%, and an AUC of 0.972, indicating excellent model performance in sentiment classification. These findings suggest that the SVM model is effective in analyzing public sentiment toward government policies and can be further developed by enriching features and comparing it with other algorithms to enhance prediction accuracy.
Co-Authors Adiyanto Angger Styo Yuniarti Ferdi Kuswandi Muhammad Arif Kurniawan Samsul Makin