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Journal : commit communication

Enhancing Competency Level Prediction Using Machine Learning: A Data-Driven Approach Based on Psychological Assessment Data Sinung Suakanto; Joko Siswanto; Jan M. Pawlowski; Muharman Lubis; Syfa Nur Lathifah; Litasari Widyastuti Suwarsono
CommIT (Communication and Information Technology) Journal Vol. 20 No. 1 (2026): CommIT Journal (in press)
Publisher : Bina Nusantara University

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Abstract

Competency level prediction plays a crucial role in competency-based human resource management such as talent management. Talent management is achieved by identifying individuals’ knowledge, skills, and attitudes through psychological assessment. Recognizing employees as a strategic asset by accurately predicting competencies supports targeted development, boosting individual and organizational performance. Current practices related to competency assessment require expert judgment from psychologists or assessors, which can be time-consuming. The research proposes a machine learning–based approach to predict competency levels using psychological assessment scores as input, designed to operate within digital, network-enabled interview platforms. Several machine learning methods, including Random Forests, k-Nearest Neighbors (KNN), and Support Vector Machines (SVMs), are applied to historical assessment datasets to identify patterns and relationships between psychological assessment scores and competency levels.The dataset comprises 1,220 records from a psychological assessment. The experimental results indicate that the Random Forest model achieves the highest accuracy of 81%, outperforming other models in competency level prediction. The key novelty lies in its data-driven methodology, which enhances the objectivity and efficiency of competency evaluation while reducing reliance on expert interpretation. By enabling automated competency prediction in network-enabled interview environments, the proposed approach supports more efficient talent decision-making, workforce development, and recruitment processes. The findings demonstrate that machine learning can accurately predict competency levels from a clean dataset of psychological assessment scores, achieving accuracy above 80%. Future research may enhance model robustness by incorporating additional assessment center criteria and real-world performance metrics.
Co-Authors A. Taupik Rahman A., Simon Filippus Abdulaziz, Rifqi Abdulaziz Adillah, Muhammad Fauzan Nur Adyartama, Arya Putra Agustien, Ferry Ahmad Musnansyah Ahmad Sidik Rofiudin Alaric Rasendriya Aniko Alaric Rasendriya Aniko Albert, Vincentius Alfi Zahra Hafizhah Andreas Andreas Andriana Andriana Angela, Dina Anggraeni Xena Paradita Ani Kartini Anis Farihan Mat Raffei Anis Farihan Mat Raffei Anisa, Gia Annastasia, Syifa Aprilita Firsty Hazdia Arifudin, Nanang Bagastio, Shobrun Jamil Basuki Rahmad Basuki Rahmad BASUKI RAHMAD Bayuwindra, Anggera Christy, Aldi Cristian Richardo Anin Daniel Hadi Wijaya Dimas Jaya Kusuma Dina Angela Echo, Ruth Edi Nuryatno Edi Triono Nuryatno Edi Triono Nuryatno Ekky Novriza Alam Ema Rachmawati Evan Reswara Fahrizky, Bimo Agung Faidatul Hikmah Faidatul Hikmah Faishal Mufied Al Anshary Fakhrurroja, Hanif Faqih Hamami Fauzi, Rokhman Febriyani, Widia Ferda Ernawan Firdaus, Taufiq Maulana Friska Andalusia Gamaliel, Yoyok Yusman Handoko, Mahardika Maulana Al Mahdi Hardiyanti, Margareta Hazdia, Aprilita Firsty Herry Imanta Sitepu Herry Sitepu Herry Sitepu Hikmah, Faidatul Hutagalung, Maclaurin Hutahaean, Bernad Robinson Ilham Perdana Ismail, Mohd Arfian Isnaeni, Rizqullah Maziyah Jan M. Pawlowski Jan M. Pawlowski Krisna Dwi Permana Litasari Widyastuti, Litasari Mahardika Maulana Al Mahdi Handoko Margareta Hardiyanti Mat Raffei, Anis Farihan Mifta Ardianti Mima Artamevia Muhammad Fahmi Hidayat Muhammad Fauzan Nur Adillah Muhammad Hanif Zahran Muhammad Haris Sitompul Muhammad Hibban Mikhail Muhammad Ivan Fadilah Muharman Lubis Mulyati, Rika Munansyah, Ahmad Navya Kirana Safitri Ni Ketut Caturwati Nia Ambarsari Nugroho, Tunggul Nugroho, Tunggul Arief Nur Ichsan Utama Nuraliza, Hilda Nuryanto, Edi Priyadi, Djoko Rachmadita Andreswari Rafi Adinegoro Raharjo, Adi Rahmat Fauzi Raina, Apriani Nur Raisyah Nurul Amanah Ramadhan, Yumna Zahran Randy Ferdiawan Revyolla Ananta Dila Rika Mulyati Rika Mulyati Rika Mulyati Riska Yanu Fa’rifah Riverinda Rijadi, Safara Cathasa Rivero Novelino Roberd Saragih et al., Roberd Rofiudin, Ahmad Sidik Ryan Muhammad Satria Sang Dara Parameswari Sang Dara Parameswari Sang Dara Parameswari Sang Dara Parameswari Satria , Ryan Muhammad Sayyid Taufiq Abdulhafizh Sebastian, Kelvin See, Tan Lian Seno Adi Putra SETYORINI Shaffiei, Zatul Alwani Siregar, Amril Mutoi Siswanto, Joko Suhardi Suhono H. Supangkat Sulingallo, Irwansa Ryan Syfa Nur Lathifah Thaha, Taufik Kemal Tiara Rahmania Hadiningrum Tien Fabrianti Kusumasari Tjong Wan Sen Ulinuha, Zulfa Ventje Jeremias Lewi Engel Warmiyana Zairi Absi Widyadhari, Dinda Putri Widyatasya Agustika Nurtrisha Wijaksana, Syifa Nuurunnisa Wijaya, Yohanes Rico Yoga Raditya Nugraha Sukma Pradana Yoyok Gamaliel Yudhi Widyatama Zalina Fatima Azzahra Zulkarnaen, Rizky Zaki