Optimizing Shortest Job First (SJF) Scheduling through Random Forest Regression for Accurate Job Execution Time Prediction

Aditya Putra Ramdani; Achmad Solichan; Basirudin Ansor; Muhammad Zainudin Al Amin; Nova Christina Sari; Kilala Mahadewi

doi:10.62951/ijies.v1i3.138

Authors

Aditya Putra Ramdani Universitas Muhammadiyah Semarang
Achmad Solichan Universitas Muhammadiyah Semarang
Basirudin Ansor Universitas Muhammadiyah Semarang
Muhammad Zainudin Al Amin Universitas Muhammadiyah Semarang
Nova Christina Sari Universitas Muhammadiyah Semarang
Kilala Mahadewi Universitas Muhammadiyah Semarang

DOI:

https://doi.org/10.62951/ijies.v1i3.138

Keywords:

CPU Scheduling, Execution Time Prediction, Machine Learning, Random Forest Regression, Shortest Job First

Abstract

One of the CPU scheduling methods that is frequently used to reduce waiting time and average execution time is Shortest Job First (SJF). However, this algorithm's accuracy is largelbravy dependent on how well the job execution time is predicted. The purpose of this study is to enhance work execution time estimates by optimizing the SJF algorithm through the use of the Random Forest Regression model. The model in this study is trained using historical job data. The test results demonstrate how Random Forest Regression may be included into SJF to greatly increase system efficiency, especially in terms of throughput and waiting time reduction.

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