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All Journal IPTEK Journal of Proceedings Series CommIT (Communication & Information Technology) JISI UMJ (Jurnal Integrasi Sistem Industri UMJ) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JRSI (Jurnal Rekayasa Sistem dan Industri) IJIES (International Journal of Innovation in Enterprise System) Abdimasku : Jurnal Pengabdian Masyarakat Journal of Entrepreneurship & Business Charity : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Metris: Jurnal Sains dan Teknologi Jurnal Sistem Informasi dan Manajemen SWAGATI: Journal of Community Service JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal bahasa, sastra, seni, dan pengajarannya Proceeding of Community Service and Engagement
Litasari Widyastuti, Litasari
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : commit communication

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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 Afifah, Farah Agilhandani, Astri Dewi Aisha, Atya N. Amelia Kurniawati Andre Kharis Sianipar Annisa Ufaira Astri Dewi Agilhandani Atya Nur Aisha Bagus Adiib Al-Haq Bayu Satria Yudha Berliana, Regine Rahmada Bewana, Zanuar Galang Budhi Yogaswara Budi Praptono Budiarto, Sulistyo Chanita , Olivia Akma Christanto Triwibisono Danang Triantoro Murdiansyah Devi Pratami Dian Kristiana Dita Ayu Wandira Dwi Wahyuni Eggy Caesario Elma Anggraini Setyaningrum Endang Chumaidiyah Erif Rifki Rediana Fachri Husaini Fachrul Rozi Farda Hasun Favian Dewanta Fida Nirmala Nugraha Hasna Fitri Nur’aini Herbayu, Vivaldy Izza Naufalindra Ida Ayu Utari Ananda Putri Ika Arum Puspita Ima Normalia Kusmayanti Imam Haryono Indarti , Oktavicha Salsabila Jan M. Pawlowski Jannah, Putri Miftahul Judi Alhilman, Judi Leonardo Fredy Putra Lestari, Ni Made Ayu Aghata Widya Luciana Andrawina Maulana, Fahrul Anam Melly Uswatun Hasanah Muhammad Fadli Auliyufliha Muhammad Ridwan Triantoro Muharman Lubis Mulyadin, Revin Naima, Nisrina Danin Nindytha Salsabila Kara Noor Novita Permata Sari Nur Falah Sofiatul Jannah Nurdinintya Athari Supratman Nurul Ikhsan Permata Syafira Aulia Permatasari, Tiara Dewi Pramesti Intan Meuthia Prandika Pratanto Prantia Amanda Putra Fajar Alam Putra Fajar Alam Putri Miftahul Jannah Putri Nandari Elman Astadipura Rachmat, Mochamad Daffa Praditia Radityatama Abiyoga Rahmat Rezki Ratu Nabila Octavia Pratama Refa Meirano Aldilla Retno Hendryanti Reza Rendian Septiawan Safinah Rizkiyani Safitri , Anissa Sari Wulandari Sinung Suakanto Siswanto, Joko Sitti Nur Azmi F. Sofi Hanifah Hermawan Suprana, Zidan Akhsan Syfa Nur Lathifah Tiara Ayu Lestari Umar Ali Ahmad Utari Wilan Utari Wilan, Utari Wimbajaya Hamukti Yesica Berliana Chrismadora Yogasswara, Budhi ZK Abdurahman Baizal