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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Malikussaleh Mengabdi Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Rizal Tjut Adek
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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

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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.
PENGEMBANGAN BOT KOMENTAR OTOMATIS DENGAN ANALISIS SENTIMEN BERBASIS BERT PADA TIKTOK UNTUK UMKM DI LHOKSEUMAWE Aqiilah Cahya Ningrum; Rizal Tjut Adek; Nunsina
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6152

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Lhokseumawe struggle to respond to customer comments on the TikTok platform due to limited human and technological resources. This study aims to develop an automatic TikTok comment classification system using a fine-tuned IndoBERT model. The system classifies comments into two main aspects: category (product, price, location, service, etc.) and sentiment (positive, negative, neutral). The dataset used includes 329 comments for category classification and 241 for sentiment classification, collected through web scraping using Selenium and enhanced using synonym-based data augmentation. Evaluation results show that the IndoBERT model achieves an accuracy of 82.07% for category classification and 97.51% for sentiment classification. In comparison, the LSTM model only achieves 59.09% and 73.47% on the same tasks. This system significantly reduces the average response time from 12–24 hours to under 5 minutes. The research contributes to improving customer service efficiency and strengthening digital marketing strategies for local MSMEs on social media platforms.
Perspektif Keamanan Pengguna Tentang Adopsi Dan Migrasi Teknologi Mobile Cloud Munirul ula; Rizal Tjut Adek; Bustami Bustami
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 5 No. 1 (2021): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2021
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v5i1.4856

Abstract

Keamanan adalah salah satu perhatian utama mereka yang ingin mengadopsi dan bermigrasi ke teknologi Cloud Computing. Masalah keamanan yang digunakan oleh teknologi cloud mengungkapkan bahwa mobile cloud computing meningkatkan masalah privasi dan keamanan seperti masalah identifikasi dan otentikasi, karena terkadang identitas dan otentikasi pemilik perangkat atau pemilik data yang terdapat di cloud dapat terdeteksi. Ini adalah beberapa contoh yang dapat dianggap sebagai kemunduran besar pada adaptasi Mobile Cloud Computing dan alasan mengapa beberapa perusahaan masih enggan merangkul, mengadopsi, dan bermigrasi ke teknologi ini. Penelitian ini mengulas fenomena mobile cloud computing, dan masalah keamanan dan privasi dalam area aplikasi mobile dan Cloud Computing dengan lebih menekankan pada pertimbangan keamanan dan privasi untuk merangkul dan bermigrasi ke mobile cloud computing. Kata    kunci:  Cloud Computing, Keamanan Informasi, Privasi, Mobile Cloud Computing
Co-Authors Aqiilah Cahya Ningrum Bustami Bustami Bustami Fadlisyah Fasdarsyah Munirul Ula Mutammimul Ula Nunsina, Nunsina OK Muhammad Majid Maulana Majid Salahuddin Salahuddin Zara Yunizar