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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal ULTIMATICS Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Penelitian, Pendidikan dan Pengajaran: JPPP Djtechno: Jurnal Teknologi Informasi Ar-Rasyid: Jurnal Pendidikan Agama Islam Educenter: Jurnal Ilmiah Pendidikan sudo Jurnal Teknik Informatika Hello World Journal of General Education and Humanities Blend Sains Jurnal Teknik Factory Jurnal Industri, Manajemen dan Rekayasa Sistem Industri Jurnal Ilmu Komputer dan Sistem Informasi Hanif Journal of Information Systems EDUCENTER JURNAL PENDIDIKAN Tsabit Jurnal Informatika Dan Tekonologi Komputer
Mhd. Basri
Universitas Muhammadiyah Sumatera Utara
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Other

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Design and Implementation of Multi-Segment LAN Infrastructure for Computer Laboratories Andi Zulherry; Muhammad Gunawan; Mhd. Basri
Tsabit Journal of Computer Science Vol. 2 No. 2 (2025): December Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit91

Abstract

Educational computer laboratories require a reliable and well-structured network infrastructure to support learning activities and efficient resource management. However, many laboratory networks are still implemented using a single network segment, which can lead to high broadcast traffic and reduced network performance as the number of connected devices increases. This study proposes the design and implementation of a multi-segment Local Area Network (LAN) infrastructure based on institutional needs in an educational computer laboratory environment. The proposed network architecture consists of four laboratory rooms with a total of 160 computers, where each laboratory operates within a different IP network segment while remaining interconnected through routing mechanisms. Network devices such as the MikroTik RB750Gr3 hEX router are used to manage gateway functions, DHCP services, and network address translation (NAT) for internet connectivity. The implementation is evaluated through connectivity tests between laboratory networks and internet access tests. The results show that all laboratory networks successfully communicate with each other without packet loss and demonstrate low latency values, indicating stable network performance. In addition, internet connectivity tests confirm that all laboratory networks can access external resources reliably. These findings demonstrate that the proposed multi-segment LAN infrastructure improves network organization, scalability, and manageability within educational computer laboratory environments.
Anomaly Detection in Electrical Energy Consumption Using Long Short-Term Memory (LSTM) Andi Zulherry; Mhd. Basri; muhammad Gunawan
Tsabit Journal of Computer Science Vol. 3 No. 1 (2026): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit123

Abstract

The increasing deployment of smart meters has generated large volumes of electrical energy consumption data, creating new opportunities for intelligent anomaly detection to reduce non-technical losses, equipment failures, and abnormal consumption patterns. Conventional statistical and rule-based approaches often struggle to capture complex temporal dependencies in sequential electricity usage data. This study proposes a Long Short-Term Memory (LSTM)-based anomaly detection model to identify abnormal electricity consumption patterns with high accuracy. A time-series dataset consisting of historical hourly electricity consumption records was collected from smart metering systems and preprocessed through missing-value imputation, normalization using Min-Max Scaling, and sequence windowing. The proposed LSTM model was trained to learn normal consumption behavior and detect anomalies based on prediction error using an adaptive threshold determined from reconstruction residuals. Model performance was evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC), and compared with conventional Machine Learning methods, including Support Vector Machine (SVM) and Isolation Forest. Experimental results demonstrate that the proposed LSTM model achieved an accuracy of 97.3%, precision of 96.8%, recall of 97.9%, F1-score of 97.3%, and an AUC of 0.985, outperforming the baseline models in detecting anomalous electricity consumption patterns. The superior performance is attributed to the LSTM architecture's ability to model long-term temporal dependencies and nonlinear consumption behaviors. These findings indicate that LSTM provides an effective and reliable approach for real-time anomaly detection in smart energy systems, supporting intelligent energy management, reducing power losses, and improving the operational reliability of modern electrical distribution networks.
Co-Authors Abdul Fadlil Al Hamidy Al Khowarizmi Andi Zulherry Andi Zulherry Andi Zulherry Andy Satria Anis Badriah Anton Yudhana Baehaqi Barelvi, Ahmad Defri Aldi Dicky Apdilah Dina Salsabila Edy Rahman Syahputra Fadhlurrohman, Dimas Farid Akbar Siregar Fatma Sari Hutagalung G. Gunawan Halim Maulana Hasibuan, Wilda Rina Hayani, Wirda Hazidar, Al Hamidy Indah Purnama Sari Indah Purnama Sari Ismail Hanif Batubara Krisna Aditya Lutfi Basit M. Rizki Adhari Mahardika Abdi Prawira Manurung, Asrar Aspia Muhammad Gunawan Muhammad Zulfahmi Khairullah Nasution, Rahmad Kurniawan Nurul Hakiki Lubis Ok Muhammad Rehansyah Putra rahayu, Intan dwi Ramadhani, Fanny Raushan Dhamir Rosaldi Alfarizi RR. Ella Evrita Hestiandari Salsabila Humairoh Sihombing, Maissy Masitoh Syafrayani, Putri Rizki Syahri, Alfi Zaky Soleh Wirawan Zaky, Naufal Zein, Liza Azzahra