Simulasi Agent-Based untuk Prediksi Beban Server Moodle pada Skala Massive Open Online Courses (MOOCs)
Simulasi Agent-Based untuk Prediksi Beban Server Moodle pada Skala Massive Open Online Courses (MOOCs)
https://doi.org/10.26594/teknologi.v16i1.6527
Abstract
Perkembangan Massive Open Online Courses (MOOCs) menuntut infrastruktur e-learning yang tangguh dalam menangani lonjakan pengguna secara masif. Metode pengujian beban konvensional seperti load testing dan stress testing memiliki keterbatasan karena bersifat statis, reaktif, serta berpotensi merusak infrastruktur fisik. Oleh karena itu, penelitian ini mengusulkan pendekatan Agent-Based Simulation (ABS) dengan skenario 2.000 agen untuk memprediksi dinamika beban server Moodle secara lebih realistis dan proaktif. Berdasarkan pemodelan regresi linier berganda (R2 = 87,27%), hasil pengujian membuktikan bahwa kelumpuhan sistem (bottleneck) tidak dipicu oleh tingginya lalu lintas akses pasif, melainkan oleh lonjakan aktivitas berkomputasi berat, khususnya pengumpulan tugas dan pengerjaan kuis yang menyumbang bobot beban tertinggi (0,0074 dan 0,0073 per permintaan). Lebih lanjut, degradasi kinerja infrastruktur terbukti tidak linier; utilisasi prosesor terkuras tajam mengikuti pola regresi logaritmik hingga menabrak titik jenuh (saturasi 100%)[1]. Saturasi tersebut secara mutlak meruntuhkan toleransi memori, memicu lonjakan keterlambatan waktu respons dan penumpukan antrean kueri secara eksponensial. Temuan empiris ini memberikan implikasi krusial bahwa perencanaan kapasitas server (capacity planning) dan strategi auto-scaling harus difokuskan untuk mengantisipasi beban komputasi spesifik pada jadwal-jadwal kritis, bukan sekadar bergantung pada metrik total pengguna aktif.
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