Pemanfaatan Mikrotik Dalam Manajemen Bandwidth Pada Jaringan Sekolah
DOI:
https://doi.org/10.55606/jitek.v5i1.5938Keywords:
utilization, mikrotik, management, bandwidth, networkAbstract
The preparation of this research is intended as an effort of Mikrotik in bandwidth management on school networks to improve the stability and efficiency of internet access in an educational environment. The main problem faced by schools is the imbalance of bandwidth distribution and the use of networks for non-educative activities. The research uses a qualitative descriptive approach with a few hands-on configuration experiments on Mikrotik RouterBoard devices. The results show that the Simple Queue, Queue Tree, and Layer 7 Protocol features successfully divide bandwidth proportionally based on user categories (teachers, students, and guests), block access to irrelevant sites, and increase connection sta-bility by 90%. Real-time network monitoring also enables more effective control and evaluation. However, it requires certain technical expertise and a regular maintenance system to maintain optimal performance. This research recommends further development in more complex networks and integra-tion with more advanced security systems. Overall, Mikrotik is an effective and economical bandwidth management solution for school environments.
References
[1] R. Saha et al., “A Blockchain Framework in Post-Quantum Decentralization,” IEEE Trans. Serv. Comput., vol. 16, no. 1, pp. 1–12, 2023, doi: 10.1109/TSC.2021.3116896.
[2] H. Zhou, K. Jiang, S. He, G. Min, and J. Wu, “Distributed Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching in Internet-of-Vehicles,” IEEE Trans. Wirel. Commun., vol. 22, no. 12, pp. 9595–9609, 2023, doi: 10.1109/TWC.2023.3272348.
[3] H. Gao, J. Xiao, Y. Yin, T. Liu, and J. Shi, “A Mutually Supervised Graph Attention Network for Few-Shot Segmentation: The Perspective of Fully Utilizing Limited Samples,” IEEE Trans. Neural Networks Learn. Syst., vol. 35, no. 4, pp. 4826–4838, 2024, doi: 10.1109/TNNLS.2022.3155486.
[4] Z. Soltani, S. Ma, M. Khorsand, and V. Vittal, “Simultaneous Robust State Estimation, Topology Error Processing, and Outage Detection for Unbalanced Distribution Systems,” IEEE Trans. Power Syst., vol. 38, no. 3, pp. 2018–2034, 2023, doi: 10.1109/TPWRS.2022.3181118.
[5] W. J. Tay, S. L. Lew, and S. Y. Ooi, “An IKEv2-based Approach for Remote Access VPN on MikroTik Router,” J. Inf. Sci. Eng., vol. 40, no. 5, pp. 1093–1114, 2024, doi: 10.6688/JISE.202409_40(5).0011.
[6] Y. Tan, J. Liu, X. Xie, E. Tian, and J. Liu, “Dynamic-Memory Event-Triggered Sliding-Mode Secure Control for Nonlinear Semi-Markov Jump Systems With Stochastic Cyber Attacks,” IEEE Trans. Autom. Sci. Eng., vol. 22, pp. 202–214, 2024, doi: 10.1109/TASE.2023.3349150.
[7] Y. Xia et al., “AI-Driven and MEC-Empowered Confident Information Coverage Hole Recovery in 6G-Enabled IoT,” IEEE Trans. Netw. Sci. Eng., vol. 10, no. 3, pp. 1256–1269, 2023, doi: 10.1109/TNSE.2022.3154760.
[8] J. S. Park et al., “A Multi-Mode 8k-MAC HW-Utilization-Aware Neural Processing Unit with a Unified Multi-Precision Datapath in 4-nm Flagship Mobile SoC,” IEEE J. Solid-State Circuits, vol. 58, no. 1, pp. 189–202, 2023, doi: 10.1109/JSSC.2022.3205713.
[9] M. Qaraqe et al., “PublicVision: A Secure Smart Surveillance System for Crowd Behavior Recognition,” IEEE Access, vol. 12, pp. 26474–26491, 2024, doi: 10.1109/ACCESS.2024.3366693.
[10] L. Yuanliang and J. Yan, “Cybersecurity of Smart Inverters in the Smart Grid: A Survey,” IEEE Trans. Power Electron., vol. 38, no. 2, pp. 2364–2383, 2023, doi: 10.1109/TPEL.2022.3206239.
[11] G. K. Chen, P. C. Knag, C. Tokunaga, and R. K. Krishnamurthy, “An Eight-Core RISC-V Processor With Compute Near Last Level Cache in Intel 4 CMOS,” IEEE J. Solid-State Circuits, vol. 58, no. 4, pp. 1117–1128, 2023, doi: 10.1109/JSSC.2022.3228765.
[12] M. Ibrar, L. Wang, N. Shah, O. Rottenstreich, G. M. Muntean, and A. Akbar, “Reliability-Aware Flow Distribution Algorithm in SDN-Enabled Fog Computing for Smart Cities,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 573–588, 2023, doi: 10.1109/TVT.2022.3202195.
[13] A. Lakhan et al., “Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 2, pp. 664–672, 2023, doi: 10.1109/JBHI.2022.3165945.
[14] B. A. Prakosa, Y. Afrianto, S. Agustiyan, and I. H. Setiadi, “Evaluating Bandwidth Management Techniques on Mikrotik Routers: A Multiple Linear Regression Approach,” Ing. des Syst. d’Information, vol. 29, no. 4, pp. 1561–1572, 2024, doi: 10.18280/isi.290429.
[15] A. T. El-Toukhy, M. M. Badr, M. M. E. A. Mahmoud, G. Srivastava, M. M. Fouda, and M. Alsabaan, “Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids,” IEEE Access, vol. 11, pp. 59558–59574, 2023, doi: 10.1109/ACCESS.2023.3284681.
[16] S. M. Rajagopal, M. Supriya, and R. Buyya, “Resource Provisioning Using Meta-Heuristic Methods for IoT Microservices With Mobility Management,” IEEE Access, vol. 11, pp. 60915–60938, 2023, doi: 10.1109/ACCESS.2023.3281348.
[17] D. Wu, S. Zhao, J. Yang, and M. Sawan, “Software-Hardware Co-Design for Energy-Efficient Continuous Health Monitoring via Task-Aware Compression,” IEEE Trans. Biomed. Circuits Syst., vol. 17, no. 2, pp. 180–191, 2023, doi: 10.1109/TBCAS.2023.3238719.
[18] Z. A. Khan, S. Amjad, F. Ahmed, A. M. Almasoud, M. Imran, and N. Javaid, “A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks,” IEEE Access, vol. 11, pp. 31036–31051, 2023, doi: 10.1109/ACCESS.2023.3259982.
[19] K. Thangavel, D. Spiller, R. Sabatini, P. Marzocca, and M. Esposito, “Near Real-Time Wildfire Management Using Distributed Satellite System,” IEEE Geosci. Remote Sens. Lett., vol. 20, 2023, doi: 10.1109/LGRS.2022.3229173.
[20] J. An et al., “Stacked Intelligent Metasurface-Aided MIMO Transceiver Design,” IEEE Wirel. Commun., vol. 31, no. 4, pp. 123–131, 2024, doi: 10.1109/MWC.013.2300259.
[21] C. Gu, Y. Liu, J. Wang, Q. Li, and L. Wu, “Carbon-Oriented Planning of Distributed Generation and Energy Storage Assets in Power Distribution Network With Hydrogen-Based Microgrids,” IEEE Trans. Sustain. Energy, vol. 14, no. 2, pp. 790–802, 2023, doi: 10.1109/TSTE.2022.3225314.
[22] G. Interdonato and S. Buzzi, “Joint Optimization of Uplink Power and Computational Resources in Mobile Edge Computing-Enabled Cell-Free Massive MIMO,” IEEE Trans. Commun., vol. 72, no. 3, pp. 1804–1820, 2024, doi: 10.1109/TCOMM.2023.3336355.
[23] D. Qiu, J. Wang, Z. Dong, Y. Wang, and G. Strbac, “Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading,” IEEE Trans. Power Syst., vol. 38, no. 5, pp. 4853–4866, 2023, doi: 10.1109/TPWRS.2022.3217922.
[24] C. Chen, B. Da Silva, C. Yang, C. Ma, J. Li, and C. Liu, “AutoMLP: A Framework for the Acceleration of Multi-Layer Perceptron Models on FPGAs for Real-Time Atrial Fibrillation Disease Detection,” IEEE Trans. Biomed. Circuits Syst., vol. 17, no. 6, pp. 1371–1386, 2023, doi: 10.1109/TBCAS.2023.3299084.
[25] M. Gao, R. Shen, L. Shi, W. Qi, J. Li, and Y. Li, “Task Partitioning and Offloading in DNN-Task Enabled Mobile Edge Computing Networks,” IEEE Trans. Mob. Comput., vol. 22, no. 4, pp. 2435–2445, 2023, doi: 10.1109/TMC.2021.3114193.
[26] A. Alkhateeb, S. Jiang, and G. Charan, “Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond,” IEEE Commun. Mag., vol. 61, no. 11, pp. 128–134, 2023, doi: 10.1109/MCOM.001.2200866.
[27] Y. Guo, D. Zhou, P. Li, C. Li, and J. Cao, “Context-Aware Poly(A) Signal Prediction Model via Deep Spatial-Temporal Neural Networks,” IEEE Trans. Neural Networks Learn. Syst., vol. 35, no. 6, pp. 8241–8253, 2024, doi: 10.1109/TNNLS.2022.3226301.
[28] A. Samanta, T. G. Nguyen, T. Ha, and S. Mumtaz, “Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 1184–1195, 2023, doi: 10.1109/TVT.2022.3206137.
[29] M. Adam, M. Hammoudeh, R. Alrawashdeh, and B. Alsulaimy, “A Survey on Security, Privacy, Trust, and Architectural Challenges in IoT Systems,” IEEE Access, vol. 12, pp. 57128–57149, 2024, doi: 10.1109/ACCESS.2024.3382709.
[30] M. U. Farooq, X. Wang, A. Hawbani, L. Zhao, A. Al-Dubai, and O. Busaileh, “SDORP: SDN Based Opportunistic Routing for Asynchronous Wireless Sensor Networks,” IEEE Trans. Mob. Comput., vol. 22, no. 8, pp. 4912–4929, 2023, doi: 10.1109/TMC.2022.3158695.
[31] A. J. Garcia-Sanchez, R. Asorey-Cacheda, J. Garcia-Haro, and J. L. Gomez-Tornero, “Dynamic Multihop Routing in Terahertz Flow-Guided Nanosensor Networks: A Reinforcement Learning Approach,” IEEE Sens. J., vol. 23, no. 4, pp. 3408–3422, 2023, doi: 10.1109/JSEN.2023.3236394.
[32] R. Azad et al., “Medical Image Segmentation Review: The Success of U-Net,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 12, pp. 10076–10095, 2024, doi: 10.1109/TPAMI.2024.3435571.
[33] X. Li, S. Yu, Y. Lei, N. Li, and B. Yang, “Intelligent Machinery Fault Diagnosis With Event-Based Camera,” IEEE Trans. Ind. Informatics, vol. 20, no. 1, pp. 380–389, 2024, doi: 10.1109/TII.2023.3262854.
[34] F. Safavi and M. Rahnemoonfar, “Comparative Study of Real-Time Semantic Segmentation Networks in Aerial Images During Flooding Events,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, pp. 15–31, 2023, doi: 10.1109/JSTARS.2022.3219724.
[35] A. V. Savkin, C. Huang, and W. Ni, “Joint Multi-UAV Path Planning and LoS Communication for Mobile-Edge Computing in IoT Networks With RISs,” IEEE Internet Things J., vol. 10, no. 3, pp. 2720–2727, 2023, doi: 10.1109/JIOT.2022.3215255.
[36] M. Zhang, S. Dong, P. Shi, G. Chen, and X. Guan, “Distributed Observer-Based Event-Triggered Load Frequency Control of Multiarea Power Systems Under Cyber Attacks,” IEEE Trans. Autom. Sci. Eng., vol. 20, no. 4, pp. 2435–2444, 2023, doi: 10.1109/TASE.2022.3208016.
[37] U. Khayam, A. A. Suryandi, and Rachmawati, “Design of Bowtie Antenna With Rounded Edge and Middle-Sliced Modifications for UHF Partial Discharge Sensor,” IEEE Access, vol. 11, pp. 22822–22834, 2023, doi: 10.1109/ACCESS.2023.3252006.
[38] M. R. Rezaee, N. A. W. Abdul Hamid, M. Hussin, and Z. A. Zukarnain, “Fog Offloading and Task Management in IoT-Fog-Cloud Environment: Review of Algorithms, Networks, and SDN Application,” IEEE Access, vol. 12, pp. 39058–39080, 2024, doi: 10.1109/ACCESS.2024.3375368.
[39] M. A. Habibi et al., “Toward an Open, Intelligent, and End-to-End Architectural Framework for Network Slicing in 6G Communication Systems,” IEEE Open J. Commun. Soc., vol. 4, pp. 1615–1658, 2023, doi: 10.1109/OJCOMS.2023.3294445.
[40] L. Sava, V. Tîrșu, and C. Plămădeală, “Performance Evaluation of Microtik Routers According to Electromagnetic Compatibility Testing Standards,” EEA - Electroteh. Electron. Autom., vol. 72, no. 4, pp. 57–61, 2024, doi: 10.46904/eea.24.72.4.1108006.
[41] J. Ascenso, E. Alshina, T. Ebrahimi, and D. Grois, “The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption,” IEEE Multimed., vol. 30, no. 1, pp. 100–111, 2023, doi: 10.1109/MMUL.2023.3245919.
[42] J. D. Morillo Reina and T. J. Mateo Sanguino, “Cloud-Based Automatic Configuration and Disaster Recovery of Communication Systems Applied in Engineering Training,” Electron., vol. 13, no. 21, 2024, doi: 10.3390/electronics13214203.
[43] B. Yin, Z. Chen, and M. Tao, “Predictive GAN-Powered Multi-Objective Optimization for Hybrid Federated Split Learning,” IEEE Trans. Commun., vol. 71, no. 8, pp. 4544–4560, 2023, doi: 10.1109/TCOMM.2023.3277878.
[44] D. Saxena, I. Gupta, R. Gupta, A. K. Singh, and X. Wen, “An AI-Driven VM Threat Prediction Model for Multi-Risks Analysis-Based Cloud Cybersecurity,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 53, no. 11, pp. 6815–6827, 2023, doi: 10.1109/TSMC.2023.3288081.
[45] H. Altaheri, G. Muhammad, and M. Alsulaiman, “Physics-Informed Attention Temporal Convolutional Network for EEG-Based Motor Imagery Classification,” IEEE Trans. Ind. Informatics, vol. 19, no. 2, pp. 2249–2258, 2023, doi: 10.1109/TII.2022.3197419.
[46] H. Song, S. Gao, Y. Li, L. Liu, and H. Dong, “Train-Centric Communication Based Autonomous Train Control System,” IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 721–731, 2023, doi: 10.1109/TIV.2022.3192476.
[47] M. Y. Abdelsadek, G. Karabulut-Kurt, H. Yanikomeroglu, P. Hu, G. Lamontagne, and K. Ahmed, “Broadband Connectivity for Handheld Devices via LEO Satellites: Is Distributed Massive MIMO the Answer?,” IEEE Open J. Commun. Soc., vol. 4, pp. 713–726, 2023, doi: 10.1109/OJCOMS.2023.3253643.
[48] A. A. Khan et al., “GAN-IoTVS: A Novel Internet of Multimedia Things-Enabled Video Streaming Compression Model Using GAN and Fuzzy Logic,” IEEE Sens. J., vol. 23, no. 23, pp. 29434–29441, 2023, doi: 10.1109/JSEN.2023.3316088.
[49] M. Guri, “AIR-FI: Leaking Data From Air-Gapped Computers Using Wi-Fi Frequencies,” IEEE Trans. Dependable Secur. Comput., vol. 20, no. 3, pp. 2547–2564, 2023, doi: 10.1109/TDSC.2022.3186627.
[50] L. Li, Y. Liu, I. You, and F. Song, “A Smart Retransmission Mechanism for Ultra-Reliable Applications in Industrial Wireless Networks,” IEEE Trans. Ind. Informatics, vol. 19, no. 2, pp. 1988–1996, 2023, doi: 10.1109/TII.2022.3183221.
[51] D. K. Murala, S. K. Panda, and S. P. Dash, “MedMetaverse: Medical Care of Chronic Disease Patients and Managing Data Using Artificial Intelligence, Blockchain, and Wearable Devices State-of-the-Art Methodology,” IEEE Access, vol. 11, pp. 138954–138985, 2023, doi: 10.1109/ACCESS.2023.3340791.
[52] E. Suethanuwong, “An Effective Prevention Approach Against ARP Cache Poisoning Attacks in MikroTik-based Networks,” ECTI Trans. Comput. Inf. Technol., vol. 19, no. 1, pp. 1–12, 2025, doi: 10.37936/ecti-cit.2025191.256401.
[53] C. H. Rashid et al., “Software Cost and Effort Estimation: Current Approaches and Future Trends,” IEEE Access, vol. 11, pp. 99268–99288, 2023, doi: 10.1109/ACCESS.2023.3312716.
[54] Y. Wang, Z. Su, Q. Xu, R. Li, T. H. Luan, and P. Wang, “A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue,” IEEE/ACM Trans. Netw., vol. 31, no. 6, pp. 2422–2438, 2023, doi: 10.1109/TNET.2022.3226458.
[55] C. Ding, J. B. Wang, M. Cheng, M. Lin, and J. Cheng, “Dynamic Transmission and Computation Resource Optimization for Dense LEO Satellite Assisted Mobile-Edge Computing,” IEEE Trans. Commun., vol. 71, no. 5, pp. 3087–3102, 2023, doi: 10.1109/TCOMM.2023.3253721.
[56] M. Xu et al., “When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment,” IEEE Wirel. Commun., vol. 31, no. 6, pp. 1–9, 2024, doi: 10.1109/mwc.005.2400019.
[57] Z. Sun, G. Sun, Y. Liu, J. Wang, and D. Cao, “BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge Computing Networks,” IEEE Trans. Mob. Comput., vol. 23, no. 2, pp. 1655–1673, 2024, doi: 10.1109/TMC.2023.3239339.
[58] M. Liu, Y. Li, Y. Chen, Y. Qi, and L. Jin, “A Distributed Competitive and Collaborative Coordination for Multirobot Systems,” IEEE Trans. Mob. Comput., vol. 23, no. 12, pp. 11436–11448, 2024, doi: 10.1109/TMC.2024.3397242.
[59] Z. Yang, M. Zolanvari, and R. Jain, “A Survey of Important Issues in Quantum Computing and Communications,” IEEE Commun. Surv. Tutorials, vol. 25, no. 2, pp. 1059–1094, 2023, doi: 10.1109/COMST.2023.3254481.
[60] H. Gao, W. Huang, T. Liu, Y. Yin, and Y. Li, “PPO2: Location Privacy-Oriented Task Offloading to Edge Computing Using Reinforcement Learning for Intelligent Autonomous Transport Systems,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 7, pp. 7599–7612, 2023, doi: 10.1109/TITS.2022.3169421.
[61] R. Liu, M. Xie, A. Liu, and H. Song, “Joint Optimization Risk Factor and Energy Consumption in IoT Networks with TinyML-Enabled Internet of UAVs,” IEEE Internet Things J., vol. 11, no. 12, pp. 20983–20994, 2024, doi: 10.1109/JIOT.2023.3348837.
[62] M. Ye, Q. L. Han, L. Ding, and S. Xu, “Distributed Nash Equilibrium Seeking in Games With Partial Decision Information: A Survey,” Proc. IEEE, vol. 111, no. 2, pp. 140–157, 2023, doi: 10.1109/JPROC.2023.3234687.
[63] F. P. Eka Putra, F. Muslim, N. Hasanah, Holipah, R. Paradina, and R. Alim, “Analisis Komparasi Protokol Websocket dan MQTT Dalam Proses Push Notification,” J. Sistim Inf. dan Teknol., pp. 63–72, 2024, doi: 10.60083/jsisfotek.v5i4.325.
[64] N. Muhammad Akbar, F. Prasetyo Eka Putra, K. Zulfana Imam, and M. Umar Mansyur, “Analisis Kinerja dan Interopabilitas STB Sebagai Server Penilaian Akhir Tahun,” J. Inf. dan Teknol., pp. 91–96, 2023, doi: 10.37034/jidt.v5i2.365.
[65] F. P. Eka Putra, F. I. Maulana, N. M. Akbar, and W. Febriantoro, “Twitter sentiment analysis about economic recession in indonesia,” Bull. Soc. Informatics Theory Appl., vol. 7, no. 1, pp. 1–7, 2023, doi: 10.31763/businta.v7i1.592.
[66] F. P. Eka Putra, Amir Hamzah, W. Agel, and R. O. Firmansyah Kusuma, “Impelementasi Sistem Keamanan Jaringan Mikrotik Menggunakan Firewall Filtering dan Port Knocking,” J. Sistim Inf. dan Teknol., pp. 82–87, 2024, doi: 10.60083/jsisfotek.v5i4.329.
[67] F. P. E. Putra, K. Mufidah, R. M. Ilhamsyah, S. A. Efendy, and S. N. R. Barokah, “Tinjauan Performa RouterOS Mikrotik dalam Jaringan Internet: Analisis Kinerja dan Kelayakan,” 2024. doi: 10.47709/digitech.v3i2.3446.
[68] F. P. E. Putra, U. Ubaidi, A. Hamzah, W. A. Pramadi, and A. Nuraini, “Systematic Literature Review: Security Gap Detection On Websites Using Owasp Zap,” 2024. doi: 10.47709/brilliance.v4i1.4227.
[69] F. P. E. Putra, F. Fauzan, S. Syirofi, M. Mursidi, D. Wahid, and A. Nuraini, “Sistem Pengendali Lingkungan Pertanian Dengan Wireless Sensor Network Untuk Mengoptimalkan Budidaya Hidroponik,” 2024. doi: 10.47709/digitech.v3i2.3461.
[70] N. Haidar Hari, F. P. Eka Putra, U. Hasanah, S. R. Sutarsih, and Riyan, “Transformasi Jaringan Telekomunikasi dengan Teknologi 5G: Tantangan, Potensi, dan Implikasi,” J. Inf. dan Teknol., pp. 146–150, 2023, doi: 10.37034/jidt.v5i2.357.
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