<![CDATA[Abstrak - Efisiensi komputasi merupakan aspek krusial dalam penyelesaian Job Shop Scheduling Problem (JSSP) menggunakan Genetic Algorithm (GA), terutama pada sistem produksi berskala menengah hingga besar. Penelitian ini menganalisis performa GA dan Hybrid Genetic Algorithm (HGA) dalam tiga skenario beban kerja yang berbeda, yaitu 20, 40, dan 60 job. Eksperimen dilakukan melalui variasi parameter evolusioner utama, meliputi ukuran populasi 50–150 kromosom, tingkat mutasi 0,01–0,1, dan jumlah generasi 100–500. Simulasi berbasis Python dengan dukungan NumPy digunakan untuk mengevaluasi waktu eksekusi, kualitas makespan, stabilitas konvergensi, serta efisiensi komputasi secara keseluruhan. Hasil menunjukkan bahwa konfigurasi optimal (populasi 100, mutasi 0,05, dan 300 generasi) mampu meningkatkan efisiensi komputasi hingga 27,4% pada skenario terbesar (60 job). Selain itu, HGA menunjukkan pola konvergensi yang lebih stabil dan mampu mempercepat proses pencarian solusi hingga 40% dibandingkan GA standar. Temuan ini memperkuat pentingnya optimasi parameter evolusioner dalam meningkatkan performa algoritma metaheuristik untuk penjadwalan produksi job shop. Kata kunci: Job Shop Scheduling; Genetic Algorithm; Efisiensi Komputasi; Metaheuristik; Optimasi; Abstract - Computational efficiency is a critical factor in solving the Job Shop Scheduling Problem (JSSP) using a Genetic Algorithm (GA), particularly for medium- to large-scale production environments. This study evaluates the performance of the GA and the Hybrid Genetic Algorithm (HGA) across three workload scenarios consisting of 20, 40, and 60 jobs. Experiments were conducted by varying key evolutionary parameters, including population sizes ranging from 50 to 150 chromosomes, mutation rates from 0.01 to 0.1, and generation counts between 100 and 500. Python-based simulations powered by NumPy were employed to assess execution time, makespan quality, convergence stability, and overall computational efficiency. The results indicate that the optimal configuration—consisting of a population size of 100, a mutation rate of 0.05, and 300 generations—improves computational efficiency by up to 27.4% in the largest scenario (60 jobs). Furthermore, the HGA exhibits a more stable convergence trajectory and accelerates the search process by up to 40% compared to the standard GA. These findings highlight the importance of optimizing evolutionary parameters to enhance metaheuristic performance in job shop production scheduling. Keywords: Job Shop Scheduling; Genetic Algorithm; Computational Efficiency; Metaheuristics; Optimization;]]>
