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Muhammad Irfan Syarif
Universitas Pembangunan Panca Budi
Computer Science & IT Decision Sciences, Operations Research & Management

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Employee Recruitment Decision Support System at Royal Prima Hospital Using the SAW Method Muhammad Abduh Al-Bathani; Irfan Nainggolan; Lilis Suryani; Achmad Riyan Hadiyatma; Muhammad Irfan Syarif
Bahasa Indonesia Vol 18 No 5 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i5.527

Abstract

The employee recruitment and selection process is a strategic stage in human resource management because it directly influences the quality and performance of the organization. Problems that often arise in this process are subjectivity of assessment, inaccuracy of candidate selection, and limitations in processing multiple criteria simultaneously. This study aims to analyze and support the decision-making process in employee recruitment and selection by utilizing a competency-based decision support system. The method used is a descriptive-analytical approach with the application of a multi-criteria decision-making model to evaluate prospective employees based on the criteria of education, work experience, skills, and work attitudes. The results of the study indicate that the application of a decision support system can assist management in producing more objective, systematic, and transparent decisions. Thus, this system is expected to improve the effectiveness and efficiency of the employee recruitment and selection process in the organization.
Analysis and Classification of Emergency Conditions Endangering Humans Based on Operational Data Using Random Forest and Support Vector Machine Methods (Case Study: UPT Basarnas Medan) Dwika Ardya; Muhammad Iqbal; Muhammad Irfan Syarif
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.532

Abstract

Operation Search and Rescue (SAR) in phase DETRESFA demands fast and accurate decision-making because it involves real, life-threatening situations. The Medan Basarnas UPT faces challenges in classifying four main categories of incidents: ship accidents (Y1), accidents requiring special handling (Y2), natural disasters (Y3), and conditions endangering humans (Y4), which have so far been done manually and subjectively. This study aims to build a decision support system based on data collection. machine learning to improve the efficiency of resource deployment through objective classification of emergency conditions. Performance comparisons were conducted between the algorithms Random ForestAnd Support Vector Machine(SVM) based on operational features such asresponse time, number of victims, number of personnel, and distance of the incident. The test results show thatRandom Forestprovides superior performance compared to SVM across all evaluation metrics, with accuracy 86.4%, AUC value 93.9%, F1-score 85.5%, And Matthews Correlation Coefficient (MCC) 0.759. AnalysisConfusion Matrixconfirm that Random Foresthas better stability in recognizing operational feature patterns for most target categories, including its more consistent ability in handling less dominant classes than SVM. Although the Y2 category is still a challenge for both models, Random Forestproven to be much more robust with an accuracy of 49.6% compared to SVM which only achieved 16.5%. This research proves that Random Forestis a more reliable and consistent model to support SAR practitioners in improving the accuracy of field responses, efficiency of resource deployment, and minimizing the risk of loss of life.
Co-Authors Achmad Riyan Hadiyatma Dwika Ardya Irfan Nainggolan Lilis Suryani Muhammad Abduh Al-Bathani Muhammad Iqbal