Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Komputasi dan Teknologi Informasi
Hamsar, Ali
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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

Found 2 Documents
Search

 1 
Frequent Pattern Mining for Cyberattack Detection Using FP-Growth on Network Traffic Logs Hamsar, Ali; Maulana, Fajar; Hendra, Yomei; Nasyuha, Asyahri Hadi; Aly, Moustafa H
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15221

Abstract

Cybersecurity threats have become increasingly complex, coordinated, and adaptive, creating significant challenges for traditional intrusion detection systems (IDS) that rely on static, signature-based mechanisms. These systems often fail to recognize novel, evolving, or multi-vector attacks that do not match predefined patterns. To overcome these limitations, this study proposes a data-driven framework that applies the Frequent Pattern Growth (FP-Growth) algorithm to analyze co-occurring events within network traffic logs. Using the CIC-IDS2017 benchmark dataset, which includes a wide range of real-world attack scenarios, network events were preprocessed and transformed into transactional data. This transformation enabled the efficient extraction of frequent itemsets and association rules without the computational burden of candidate generation. The experimental results show that the proposed method effectively uncovers meaningful attack correlations, such as brute force attempts preceding privilege escalation or malware infections leading to large-scale DDoS attacks. The model achieved a precision of 77.27%, recall of 70.83%, and F1-score of 73.91%, confirming its reliability in detecting sophisticated attack chains. A heatmap visualization was also generated to improve interpretability, allowing security analysts to quickly identify critical attack relationships. In conclusion, this research demonstrates that FP-Growth provides a scalable, interpretable, and computationally efficient approach to cyberattack detection, with potential integration into real-time IDS environments. Future work will focus on temporal sequence mining and hybrid models combining FP-Growth with machine learning to enhance adaptive, context-aware threat detection.
Implementasi Deep Learning untuk Deteksi Tanda Waqf Al-Quran : Studi Kasus dengan YOLOv11 Takyudin, Takyudin; Sihombing, Aland Polma Naek; Putra, Jonni Adi; Hamsar, Ali
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10438

Abstract

Abstrak - Tanda waqaf dalam Al-Qur’an memiliki peran penting dalam menjaga keutuhan makna ayat yang dibaca. Kesalahan dalam memahami tanda ini dapat menyebabkan perubahan makna yang signifikan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi otomatis tanda waqaf menggunakan algoritma deep learning YOLOv11. Dataset berupa 112 gambar mushaf yang berisi 819 anotasi dari 7 jenis tanda waqaf dikumpulkan dan dianotasi secara manual menggunakan Roboflow. Augmentasi data diterapkan untuk meningkatkan keragaman visual dataset, dilanjutkan dengan pelatihan model YOLOv11 pada platform yang sama. Evaluasi kinerja model dilakukan menggunakan metrik precision, recall, dan mean Average Precision (mAP). Hasilnya menunjukkan model memiliki nilai precision sebesar 0.97, recall sebesar 0.42, mAP@0.5 sebesar 0.54, dan mAP@0.5:0.95 sebesar 0.28. Meskipun tingkat presisi tinggi, masih terdapat tantangan dalam meningkatkan kemampuan model mendeteksi seluruh objek yang ada. Penelitian ini berkontribusi dalam bidang pengenalan pola berbasis kecerdasan buatan, khususnya untuk mendukung digitalisasi Al-Qur’an. Sistem yang dikembangkan juga dapat digunakan sebagai media pembelajaran interaktif bagi santri, pengajar, maupun masyarakat umum dalam memahami kaidah tajwid secara lebih praktis. Dengan pengembangan lanjutan, sistem ini berpotensi diintegrasikan ke aplikasi pembelajaran berbasis mobile atau web sehingga dapat menjangkau pengguna secara lebih luas. Secara keseluruhan, penelitian ini memberikan dasar penting bagi inovasi teknologi pendidikan Al-Qur’an berbasis AI.Kata kunci : Deep Learning; Tanda Waqaf; YOLOv11; Deteksi Objek; Al-Qur’an Digital; Abstract - Waqf signs in the Qur'an play a crucial role in preserving the integrity of the verses' meaning during recitation. Misinterpreting these signs can lead to significant changes in meaning. This study aims to develop an automatic waqf sign detection system using the YOLOv11 deep learning algorithm. A dataset of 112 mushaf images containing 819 annotations across 7 types of waqf signs was collected and manually labeled using Roboflow. Data augmentation was applied to enhance visual diversity, followed by model training using YOLOv11 on the same platform. The model's performance was evaluated using precision, recall, and mean Average Precision (mAP) metrics. The results show that the model achieved a precision of 0.97, a recall of 0.42, an mAP@0.5 of 0.54, and an mAP@0.5:0.95 of 0.28. Despite the high precision, challenges remain in improving the model's ability to detect all relevant objects. This research contributes to the field of pattern recognition and artificial intelligence by supporting the digitalization of the Qur’an. Furthermore, the developed system can serve as an interactive learning tool for students, teachers, and general users to better understand Tajweed rules in a more practical way. With further development, the system has the potential to be integrated into mobile or web-based learning applications, ensuring wider accessibility and promoting more accurate Qur’anic recitation in digital environments.Keywords: Deep Learning; Waqf Signs; YOLOv11; Object Detection; Digital Qur'an;
Co-Authors Aland Polma Naek Sihombing Aly, Moustafa H Asyahri Hadi Nasyuha Hendra, Yomei Maulana, Fajar Putra, Jonni Adi Takyudin, Takyudin