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All Journal Jurnal Momentum Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Informasi dan Pendidikan JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Ipteks Terapan : research of applied science and education Jurnal Teknologi Jurnal Quancom: Jurnal Quantum Komputer The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Pustaka Robot Sister
Ruri Hartika Zain
Universitas Putra Indonesia YPTK Padang
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Multi-Process Data Mining with Clustering and Support Vector Machine for Corporate Recruitment Zain, Ruri Hartika; Randy Permana; Sarjon Defit
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6197

Abstract

Having an efficient and accurate recruitment process is very important for a company to attract candidates with professionalism, a high level of loyalty, and motivation. However, the current selection method often faces problems due to the subjectivity of assessing prospective employees and the long process of deciding on the best candidate. Therefore, this research aims to optimize the recruitment process by applying data mining techniques to improve efficiency and accuracy in candidate selection. The method used in this research utilizes a multi-process Data Mining approach, which is a combination of clustering and classification algorithms sequentially. In the initial stage, the K-Means algorithm is applied to cluster candidates based on administrative selection data, such as document completeness and reference support. Next, a classification model was built using a Support Vector Machine (SVM) to categorize the best candidates based on the results of psychological tests, medical tests, and interviews. The experimental results show that the SVM model produces high evaluation scores, with an AUC of 87%, Classification Accuracy (CA) of 90%, F1-score of 89%, Precision of 91%, and Recall of 90%. With these results, it can be concluded that this model is able to improve accuracy in the employee selection process and help companies make more measurable and data-based recruitment decisions.
Co-Authors Adhista Ricky Yatra Afira, Riandana Awal, Hasri Devita, Retno Firna Yenila Fitri Insani Hendrik, Billy Ipriadi Ipriadi - Ipriadi Ipriadi, Ipriadi Khadnova, Triuliamri Kurnia, Hezy Kurnia, Hezy Mahessya, Raja Ayu Mardhiah Masril Nasution, Ayu Lestiani Novi Yanti Okfalisa Okfalisa Putra, Ondra Eka Randy Permana Retno Devita Retno Devita Sarjon Defit Setiawan, Adil Sri Rahmawati Suci Wahyuni Suci Wahyuni Sumijan Surmayanti, Surmayanti Wiyandra, Yogi Yani, Zulfitri Yenila, Firna Yogi Wiyandra