Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Malikussaleh Mengabdi Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Rizal Tjut Adek
Unknown Affiliation
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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

Found 2 Documents
Search

 1 
PKM Strategi Pemanfaatan Teknologi Informasi dalam Pencegahan Cyberbullying untuk Siswa Ula, Mutammimul; Fasdarsyah; Bustami; Rizal Tjut Adek; Fadlisyah; salahuddin
Jurnal Malikussaleh Mengabdi Vol. 4 No. 1 (2025): Jurnal Malikussaleh Mengabdi, April 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i1.24208

Abstract

PKM Strategi Pemanfaatan Teknologi Informasi dalam Pencegahan Cyberbullying untuk Siswa di SMK Negeri 3 kota lhokseumawe untuk mengantisipasi salah satu dampak negatif dari perkembangan teknologi digital yang memberikan ancaman serius bagi kesehatan mental dan sosial siswa. Fenomena ini menuntut adanya strategi pencegahan yang efektif melalui pemanfaatan teknologi informasi. Penelitian ini bertujuan untuk mengkaji strategi penggunaan teknologi informasi dalam upaya pencegahan cyberbullying di kalangan siswa. Metode yang digunakan adalah studi literatur terhadap berbagai penelitian terdahulu dan analisis praktik implementasi teknologi di bidang pendidikan. Hasil pengabdian ini menunjukkan bahwa pemanfaatan teknologi informasi dapat dilakukan melalui tiga pendekatan utama: (1) penggunaan aplikasi pengawasan dan pelaporan untuk mendeteksi serta menangani kasus cyberbullying secara cepat, (2) penguatan literasi digital siswa melalui platform e-learning dan konten edukatif interaktif, serta (3) kolaborasi sekolah, orang tua, dan penyedia layanan digital dalam menciptakan sosial secara online dan aman. Kesimpulan dari pengabdian ini adalah Pemanfaatan teknologi informasi dalam pencegahan cyberbullying bagi siswa terbukti efektif bila diarahkan pada tiga aspek utama: deteksi dini melalui aplikasi pengawasan, peningkatan literasi digital siswa, dan kolaborasi antara sekolah, orang tua, serta penyedia layanan digital. Strategi ini tidak hanya menekan potensi terjadinya cyberbullying, tetapi juga membangun budaya digital yang sehat dan aman bagi siswa. Dengan demikian, integrasi teknologi informasi dalam program pendidikan dan kebijakan sekolah menjadi langkah krusial dalam pencegahan cyberbullying.
Spammer Detection On Computer Networks Using Gaussian Naïve Bayes Classifier And K-Medoids As Acquisition Training Data OK Muhammad Majid Maulana Majid; Rizal Tjut Adek; Zara Yunizar
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research focuses on the implementation of the Gaussian Naïve Bayes algorithm for spammer detection in computer networks, leveraging K-Medoids clustering for training data acquisition. The increasing number of internet users, combined with the challenges of detecting spam activity on a network, has made manual detection ineffective. This study addresses the need for automated spam detection using machine learning algorithms. The Gaussian Naïve Bayes algorithm was chosen for its simplicity and effectiveness in handling continuous data, making it suitable for classifying network traffic as either normal or spammer. To acquire labeled training data, K-Medoids clustering was employed, offering robustness against outliers, which traditional clustering algorithms like K-Means often struggle with. The research involved collecting traffic data from a Mikrotik Routerboard at various intervals, followed by data preprocessing to remove irrelevant or null features. After preprocessing, the data was clustered using K-Medoids into two groups: spammer and normal. The Gaussian Naïve Bayes classifier was then applied to the clustered data, producing a model with high accuracy, precision, recall, and F1-score. Specifically, the model achieved 99.71% accuracy, 100% precision, 99.71% recall, and a 99.85% F1-score, indicating a well-balanced performance in spam detection. The results demonstrate that the Gaussian Naïve Bayes algorithm, combined with K-Medoids clustering, is effective for detecting spammers in computer networks. Future research could explore higher-layer network traffic and broader datasets, utilizing different routers for a more comprehensive evaluation. This approach provides a reliable solution for network administrators seeking to improve network security by detecting and mitigating spam activity.
Co-Authors Bustami Fadlisyah Fasdarsyah Mutammimul Ula OK Muhammad Majid Maulana Majid Salahuddin Zara Yunizar