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All Journal Jurnal Akuntansi dan Ekonomika IT JOURNAL RESEARCH AND DEVELOPMENT
Aprilina Tarigan, Mida
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering

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Job Classification Based on Skills and Qualifications Using Natural Language Processing and Ensemble Learning Methods Oktasia Nasution, Hafiza; Ramadhani, Dian; Aprilina Tarigan, Mida; Andreas, Prima; Suryati Ningsih, Dewita; Pramadewi, Arwinence
IT Journal Research and Development Vol. 10 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.25550

Abstract

This study proposes a job classification framework using Natural Language Processing (NLP) and Ensemble Learning to classify job roles based on required skills and qualifications. A large-scale open-source dataset containing 1.048.576 job postings was utilized, with attributes such as job title, qualifications, skills, company profile, and role. Only relevant attributes were used: skills and qualifications as input features, and role as the target label. Data were filtered to focus on three major job roles—Management, IT, and Digital—resulting in 489,651 relevant entries. Skills were extracted and standardized using GROK AI before feature transformation with MultiLabelBinarizer for one-hot encoding. The XGBoost algorithm was applied for classification under multiple data split configurations (70:15:15, 80:10:10, 70:30, 80:20, 90:10) with random_state=42 and multi-class log loss evaluation. Results showed that the 90:10 configuration achieved the highest accuracy (74.18%), followed by 80:20 with 68.44%. This research demonstrates that ensemble learning effectively handles high-dimensional categorical job data and provides a foundation for automated job classification systems and labor market analysis.
Pengawasan Digital dan Sosial Media Tax Shaming dalam Membentuk Kepatuhan Pajak Sukarela Faradisty, Astrid; Ramaiyanti, Sinta; Humairoh, Fitri; Aprilina Tarigan, Mida
Akuntansi & Ekonomika Vol 16 No 1 (2026): Jurnal Akuntansi dan Ekonomika
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jae.v16i1.11453

Abstract

This research examines the influence of digital tax oversight and social media tax shaming on the voluntary tax compliance of Generation Z in IndonesiaEmploying a quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 207 Generation Z respondents via online questionnaires using a 1-4 Likert scale. The results indicate that digital tax oversight has a positive and significant effect on voluntary compliance. Crucially, social media tax shaming exhibits a higher t-statistic value than formal oversight, suggesting that Generation Z is more susceptible to social pressure from digital communities than to formal authority threats. Theoretically, this study extends the Slippery Slope Framework by integrating horizontal monitoring as a new determinant of tax morality. Practically, the tax authority is advised to adopt digital reputation-based communication strategies to engage younger taxpayers.
Co-Authors Andreas, Prima Arwinence Pramadewi Dewita Suryati Ningsih Faradisty, Astrid Humairoh, Fitri Oktasia Nasution, Hafiza Ramadhani, Dian Ramaiyanti, Sinta