Monitoring Performa Jaringan Lokal Menggunakan Protokol SNMP dan Dashboard Grafana
DOI:
https://doi.org/10.31102/jatim.v7i2.4042Keywords:
Grafana, Pengelompokan K-Means, Machine Learning, Pemantauan Jaringan, Protokol Manajemen Jaringan SederhanaAbstract
Pemantauan jaringan berbasis SNMP konvensional hanya menyediakan data throughput dan latensi mentah yang masih memerlukan interpretasi manual, sehingga menyulitkan administrator jaringan untuk mendeteksi dan merespons anomali secara waktu nyata, karena ambang batas statis tidak dapat beradaptasi dengan pola lalu lintas yang dinamis. Penelitian ini mengatasi masalah tersebut dengan mengembangkan sistem pemantauan kinerja jaringan lokal waktu nyata yang mengintegrasikan Protokol Manajemen Jaringan Sederhana dengan algoritme pengelompokan K-Means berpanduan ambang batas yang mampu mengklasifikasikan kesehatan jaringan secara otonom ke dalam tiga status, yaitu SEHAT, WASPADA, dan KRITIS. Penelitian ini mengikuti Metodologi Penelitian Ilmu Desain, yang mencakup identifikasi masalah, tujuan solusi, perancangan dan pengembangan artefak, demonstrasi, evaluasi, dan komunikasi, serta menggunakan arsitektur klien-server yang disimulasikan dalam lingkungan Graphical Network Simulator menggunakan router Cisco virtual. Metodologi melibatkan pengambilan data throughput jaringan melalui basis data deret waktu Prometheus dan penerapan pendekatan pengelompokan K-Means dengan inisialisasi pusat massa berpanduan ambang batas yang berasal dari standar teknik yang telah ditetapkan, sementara eksportir pengelompokan berbasis Python khusus menjembatani model pembelajaran mesin terlatih dengan tumpukan observabilitas secara waktu nyata. Hasil penelitian menunjukkan bahwa sistem terintegrasi beroperasi dengan sukses dalam kondisi waktu nyata, dengan model pengelompokan mencapai Skor Siluet yang sangat baik sebesar 0,9055, dikonfirmasi melalui validasi silang lima lipatan, dan akurasi klasifikasi 100 persen jika dibandingkan dengan label ambang batas yang ditentukan secara manual. Keluaran akhir adalah dashboard Grafana tiga belas panel yang terdiri dari dua belas panel dinamis yang menampilkan metrik jaringan waktu nyata dan satu panel statis yang menunjukkan identifikasi kelompok penelitian. Secara keseluruhan, integrasi algoritme pengelompokan berpanduan ambang batas ke dalam alur pemantauan secara signifikan meningkatkan keteramatan jaringan, mendukung manajemen proaktif, dan mengurangi ketergantungan administrator jaringan pada inspeksi visual manual yang berkelanjutan, menawarkan referensi yang praktis dan dapat diinterpretasikan untuk kebutuhan pemantauan serupa di lingkungan jaringan kampus atau perusahaan kecil.
Downloads
References
[1] E. K. Silalahi, Y. C. Sitanggang, E. Suryaningsih, and D. Kiswanto, "Implementasi dan Analisis Protokol HSRP dan VRRP dalam Meningkatkan Redundansi Gateway pada Jaringan Virtual," JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 2, pp. 1431-1444, 2025. DOI: https://doi.org/10.23960/jitet.v13i2.6474
[2] D. Bhamare et al., "IntOpt: In-band Network Telemetry optimization framework to monitor network slices using P4," Computer Networks, vol. 216, p. 109214, 2022. DOI: https://doi.org/10.1016/j.comnet.2022.109214
[3] A. H. Alhilali, A. Al Farawn, and A. Y. Mjhool, "Design And Implement A Real-Time Network Traffic Management System Using Snmp Protocol," Eastern-European Journal of Enterprise Technologies, vol. 5, no. 9 (125), pp. 35-44, 2023. DOI: https://doi.org/10.15587/1729-4061.2023.286528
[4] A. M. T. I. Al-Naib, "Design and Implementation of a Real-Time Monitoring and Protection System," Journal of Positive Sciences, vol. 2023, no. 17, 2023. DOI: https://doi.org/10.52688/17
[5] J. Koumar, K. Hynek, J. Pesek, and T. Cejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification," Computer Networks, vol. 240, p. 110147, 2024. DOI: https://doi.org/10.1016/j.comnet.2023.110147
[6] L. Yu and Y. Bai, "Design of network security monitoring system based on K-means clustering algorithm," Intelligent Decision Technologies, vol. 18, no. 4, pp. 3105-3118, 2024. DOI: https://doi.org/10.3233/IDT-240185
[7] B. C. Pragathi, H. Maddirala, and M. Sneha, "Implementing an Effective Infrastructure Monitoring Solution with Prometheus and Grafana," International Journal of Computer Applications, vol. 186, no. 38, pp. 7–15, Sep. 2024. DOI: https://doi.org/10.5120/ijca2024923873
[8] S. Kamamura, Y. Takei, M. Nishiguchi, Y. Hayashi, and T. Fujiwara, "Network Anomaly Detection Through IP Traffic Analysis With Variable Granularity," IEEE Access, vol. 11, pp. 129818-129828, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3334212
[9] H. J. Santillan Carranza, J. J. Arellano Riera, B. L. Catota Morocho, and P. M. Wong Wong, "Integration of OSPF and SNMP for Network Monitoring and High Availability: Experimental Evaluation," InGenio Journal, vol. 9, no. 1, pp. 87-112, 2026. DOI: https://doi.org/10.18779/ingenio.v9i1.1102
[10] C. L. Aldea, R. Bocu, and R. N. Solca, "Real-Time Monitoring and Management of Hardware and Software Resources in Heterogeneous Computer Networks through an Integrated System Architecture," Symmetry, vol. 15, no. 6, Art. no. 1134, May 2023. DOI: https://doi.org/10.3390/sym15061134
[11] Q. Naseer Qureshi, G. Jeon, and F. Piccialli, "Anomaly Detection and Trust Authority in Artificial Intelligence and Cloud Computing," Computer Networks, vol. 184, p. 107647, 2021. DOI: https://doi.org/10.1016/j.comnet.2020.107647
[12] N. B. M. Faizu et al., "IoT-based real-time monitoring of agricultural wastewater using Raspberry Pi, Node-RED, and Grafana," Bulletin of Electrical Engineering and Informatics, vol. 14, no. 6, pp. 4665-4677, 2025. DOI: https://doi.org/10.11591/eei.v14i6.10170
[13] M. Zeeshan A., " M. B. Yel, "Analysis of Enterprise Network Performance Using the SNMP (Simple Network Management Protocol) Method," Journal Innovations Computer Science, vol. 4, no. 2, pp. 335–344, Nov. 2025. DOI:Network Monitoring Using Grafana: An Integrated Approach for Enterprise Infrastructure Management," International Journal of Engineering Research and Applications, vol. 15, no. 8, pp. 01–05, Aug. 2025. DOI: https://doi.org/10.9790/9622-15080105
[14] G. Calderon, G. del Campo, E. Saavedra, and A. Santamaria, "Monitoring Framework for the Performance Evaluation of an IoT Platform with Elasticsearch and Apache Kafka," Information Systems Frontiers, vol. 26, pp. 2373-2389, 2024. DOI: https://doi.org/10.1007/s10796-023-10409-2
[15] A. H. Sukma, D. Kiswanto, F. G. Sembiring, and S. Ashillah, "Rancang Bangun Sistem Logging Jaringan Berbasis Web dengan Visualisasi Interaktif Grafana untuk Analisis Aktivitas IP Mencurigakan," JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 14, no. 1, 2026. DOI: https://doi.org/10.23960/jitet.v14i1.8200
[16] I. Fosic, D. Zagar, K. Grgic, and V. Krizanovic, "Anomaly detection in NetFlow network traffic using supervised machine learning algorithms," Journal of Industrial Information Integration, vol. 33, p. 100466, 2023. DOI: https://doi.org/10.1016/j.jii.2023.100466
[17] T. Cattai, S. Sardellitti, F. Cuomo, and S. Barbarossa, "Joint Anomaly Detection and Topology Inference for Dynamic Water Distribution Networks Monitoring," Internet of Things, p. 101978, 2026. DOI: https://doi.org/10.1016/j.iot.2026.101978
[18] C. Wang, H. Zhou, Q. Xu, and Y. Zhang, "Network traffic analysis over clustering-based collective anomaly detection," Computer Networks, vol. 205, p. 108760, 2022. DOI: https://doi.org/10.1016/j.comnet.2022.108760
[19] J. J. Peralta Abadia et al., "Self-diagnosis service to support analysis of production performance, monitoring and optimisation activities," Journal of Manufacturing Systems, vol. 83, pp. 800-821, 2025. DOI: https://doi.org/10.1016/j.jmsy.2025.11.010
[20] A. Vargas-Arcila, J. C. Corrales, A. Sanchis, and A. Rendon, "SOFI dataset: Symptom-fault relationship for IP-network," Computer Networks, vol. 216, p. 109233, 2022. DOI: https://doi.org/10.1016/j.comnet.2022.109233
[21] I. Martinez-Yelmo, J. Alvarez-Horcajo, J. A. Carral, and D. Lopez-Pajares, "eHDDP: Enhanced Hybrid Domain Discovery Protocol for network topologies with both wired/wireless and SDN/non-SDN devices," Computer Networks, vol. 191, p. 107983, 2021. DOI: https://doi.org/10.1016/j.comnet.2021.107983
[22] L. Golightly, P. Modesti, and V. Chang, "Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator," Journal of Cybersecurity and Privacy, vol. 3, pp. 464-492, 2023. DOI: https://doi.org/10.3390/jcp3030024
[23] I. Nedyalkov, "Application of GNS3 to Study the Security of Data Exchange between Power Electronic Devices and Control Center," Computers, vol. 12, p. 101, 2023. DOI: https://doi.org/10.3390/computers12050101
[24] Z. Aziz and R. Bestak, "Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records," Sensors, vol. 24, no. 6, p. 1716, 2024. DOI: https://doi.org/10.3390/s24061716
[25] F. Graf, T. Watteyne, and M. Villnow, "Monitoring performance metrics in low-power wireless systems," ICT Express, vol. 10, pp. 989-1018, 2024. DOI: https://doi.org/10.1016/j.icte.2024.08.004
[26] I. Vingestin, T. U. Kalsum, and Y. Mardiana, "The Design Of Network Monitoring System Using SNMP Protocol With Telegram Notification," Jurnal Media Computer Science, vol. 2, no. 1, pp. 93-100, 2023. Available: https://jurnal.unived.ac.id/index.php/jmcs/article/view/3441/2842s
