Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science KLIK: Kajian Ilmiah Informatika dan Komputer
Ken Dhita Tania
Universitas Sriwijaya, Palembang
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan User Interface Pada Aplikasi Lapor Menggunakan Knowledge Management Muhammad Chandra Prawira; Ken Dhita Tania
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.968

Abstract

School is an educational institution that organizes the teaching and learning process. School activities for teachers and students also depend on good learning, collection and management of knowledge. Because the teacher is one of the key success factors for any educational endeavor, teachers are therefore expected to act as educators, instructors, and mentors to improve student learning outcomes and motivate students to learn. The role of a teacher is very influential in realizing the quality of education, especially teacher performance. Therefore, teacher performance is one of the most important conditions for successful learning. SMP Negeri 60 Palembang has problems handling student and teacher cases and this school is also a new school where there are constraints on inadequate and incomplete facilities with a large number of students, causing the teaching and learning process to be slightly hampered. The design thinking method can understand users, present evidence, and overcome problems in devising strategies to find solutions. Therefore, design thinking is considered capable of solving problems in creating solutions based on user needs. To support the process of information purposes at SMPN 60 Palembang without being limited by distance and time so that the information can be used optimally by both students and teachers.
Leakage-Aware Random Forest Regression for Predicting Job Automation Risk Using Structured Labor Market Data Alya Zalfa Chairunnisa; Nawirah Athqiyah; Vanisa Amalia Putri; Ken Dhita Tania; Allsela Meiriza
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9706

Abstract

This study aims to predict job automation risk in the era of artificial intelligence (AI) using a leakage-aware Random Forest Regression approach. The automation risk score, defined as a composite index derived from task exposure to AI, occupational routine intensity, and technological susceptibility indicators sourced from the AI Impact Jobs Dataset, serves as the target variable. The dataset comprises 5,000 job vacancy records from 44 countries across 9 industries spanning 2010 to 2025. A rigorous methodological framework is applied by systematically identifying and eliminating potential data leakage features, including ai_intensity_score, reskilling_required, and ai_mentioned, which were found to share mathematical or conceptual derivation paths with the target variable. The model is evaluated using R², RMSE, MAE, and MAPE with 5-fold cross-validation. The results show that the model achieves an R² score of 0.8087 on testing data, with RMSE of 0.1129 and MAE of 0.0893. Feature importance analysis reveals that salary_change_vs_prev_year_percent is the most influential predictor (55.85%), which, although indicative of dominance bias typical in synthetic datasets, aligns with economic theories linking wage dynamics to automation incentives. The findings demonstrate that leakage control significantly reduces inflated performance estimates (from R² = 0.8857 to 0.8087), and that Random Forest Regression provides a robust predictive framework for tabular socio-economic data when combined with rigorous preprocessing. This study contributes a methodological template for preventing data leakage in labor market prediction tasks.
Co-Authors Allsela Meiriza, Allsela Alya Zalfa Chairunnisa Muhammad Chandra Prawira Nawirah Athqiyah Vanisa Amalia Putri