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Bir kurum veya işletme için en önemli varlık sahip olduğu insan gücüdür. İnsan gücünün sağlam olabilmesi için en başta doğru personelin işe alınması gerekmektedir. Günümüzde kadro gereksinimleri, aday nitelikleri ve iş başvurusu sayıları artmıştır. Bu durum incelenmesi gereken veri miktarını çok yüksek boyutlara taşıyarak doğru adayı belirleme sürecini daha karmaşık ve zor hale getirmiştir. İşe alım kararı geri dönülmesi zor, uzun vadeli sonuçlar doğuran kritik bir karardır. Bu kritik kararın aynı anda birçok farklı işi takip etmek zorunda olan sınırlı sayıdaki personel tarafından verilmesi gerekmektedir. Yapay zeka yüksek boyuttaki verileri inceleyerek anlamlı çıktılar sunabilme yeteneği sayesinde yeni personel seçimine yardımcı olabilecek başlıca teknolojidir. Bu çalışma kapsamında insan kaynağı temin süreçlerinde yaşanan sorunları azaltmak ve daha kısa sürede doğru adaya ulaşılmasını sağlamak için başvuru formlarını makine öğrenmesi algoritmaları ile değerlendirerek ön eleme yapabilen bir uygulama geliştirilmiştir. İşe alım ekipleri normalde günlerce, haftalarca sürebilecek değerlendirme süreçlerini uygulama ile kısa sürelerde gerçekleştirebilecektir. Geçmişte yapılmış değerlendirmeler ile eğitilen makine öğrenmesi algoritmalarının yeni başvurular üzerinde yüksek doğruluk oranında tahminler yapabildiği gözlemlenmiştir.
The most important asset for an organization or business is the human power. In order for the human power to be strong, the right staff must be recruited first. Today, staff requirements, qualifications and number of job applications have increased. This situation has made the process of determining the right candidate more complex and difficult by moving the amount of data to be studied to very high dimensions. The recruitment decision is a critical decision that is difficult to return, with long-term results. This critical decision must be made by a limited number of staff who have to follow many different tasks at the same time. Artificial intelligence is the main technology that can help to select new staff through the ability to provide meaningful outcomes by studying high-dimensional data. In the framework of this study, a application has been developed that can be advanced by evaluating application forms with machine learning algorithms to reduce problems in human resources supply processes and ensure that the right island is reached in the shortest time. Employment teams will normally be able to carry out assessment processes that may take days, weeks, in short periods with implementation. It has been observed that machine learning algorithms trained with previous assessments have been able to make high accuracy forecasts on new applications.
The most significant asset for an institution or business is its manpower. For the manpower to be strong, the right staff should be hired first. Today, staff requirements, candidate qualifications and the number of job applications have increased. This situation made the process of identifiying the right candidate more complex and difficult by increasing the amount of data to be analyzed to very high dimensions. The recruitment decision is a critical decision which is hard to reverse and has long-term consequences. This critical decision has to be made by a limited number of staff who have to follow many different tasks at the same time. Artificial intelligence is the primary technology that can help new staff selection, thanks to its ability to provide meaningful outputs by examining large-scale data. Within the scope of this study, in order to reduce the problems in recruitment process and to reach the right candidate in a shorter time, an application which can pre-eliminate by evaluating application forms with machine learning algorithms has been developed. With the application, recruitment teams will be able to perform the evaluation processes which can normally take days or weeks in a short time. It has been observed that machine learning algorithms trained with the evaluations made in the past can make predictions with high accuracy on new job applications.
Field : Sosyal, Beşeri ve İdari Bilimler
Journal Type : Ulusal
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