Article Per Year (5 Year)
p-Index From 2021 - 2026
4.434
P-Index
This Author published in this journals
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

Published : 62 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 62 Documents
Search

 3   4   5   6   7 
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Konseling Manajemen Stres pada Santri Lembaga Tahfidz Qur’an Cahaya Mujahadah dengan Media Point of You Fida Nirmala Nugraha; Litasari Widyastuti Suwarsono; Atya Nur Aisha; Melly Uswatun Hasanah
Jurnal Pengabdian Masyarakat Bhinneka Vol. 5 No. 1 (2026): September
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v5i1.1685

Abstract

Pendidikan tahfidz bertujuan untuk mendukung kegiatan menghafal Al-Qur’an, sehingga dapat menghasilkan penghafal Al-Qur’an yang berakhlak baik. Namun demikian, proses menghafal Al-Qur’an tidaklah mudah dan disertai kegiataan yang padat, serta target yang tinggi, berdampak pada kondisi para santri. Tekanan, konflik, dan tanggung jawab moral sebagai penghafal AL-Qur’an memicu munculnya stres di kalangan santri. Survei awal yang dilakukan pada Lembaga Tahfidz Qur’an Cahaya Mujahadah menunjukan bahwa terdapat 32% santri yang merasa sedang mengalami stres dalam tiga bulan terakhir. Apabila stres tidak ditanggulangi dengan baik, dapat berdampak pada pencapaian target hafalan yang telah ditentukan. Sementara masih terdapat 29% santri yang belum mengetahui manajemen stres dengan tepat. Oleh karena itu, kegiatan pengabdian masyarakat yang dilaksanakan bertujuan untuk memberikan konseling terkait manajemen stres bagi para santri tahfidz melalui pemanfaatan media Point of You. Selain penggunaan media, diberikan pemahaman secara teori mengenai stres dan cara mengelola stres. Dari hasil pengukuran umpan balik pelaksanaan kegiatan, para santri dapat memahami mengenai gejala mengenai stres dan mengetahui hal yang perlu dilakukan untuk coping stress. Mayoritas peserta memberikan umpan balik positif terhadap kesesuaian materi dan manfaat dari kegiatan pengabdian masyarakat yang dilaksanakan. Melalui kegiatan pengabdian masyarakat ini, diharapkan dapat memberikan gambaran kepada para santri maupun pengelola Lembaga Tahfidz dalam manajemen stres, sehingga dapat memberikan lingkungan dan kegiatan operasional yang nyaman sejalan dengan tujuan program tahfidz yang direncanakan.
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