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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sekretaris & Administrasi Bisnis
Yusuf Bayu Wicaksono
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Pemanfaatan Manajemen Pengetahuan untuk Membantu Persiapan Data pada Proses Data Mining Yusuf Bayu Wicaksono; Christina Juliane
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 1 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i1.2424

Abstract

The data mining process always involves a data preparation stage. Based on the experience of IBM data mining practitioners, 40-70% of data mining project time is spent on data preparation. This is because not everyone knows what the content of the available data is, so it will take time just to understand the data itself. The research method used adopts an information systems research framework, by comparing the knowledge base (data mining) with environmental facts (the duration of data preparation). Design/research is made using a knowledge management approach designed for data. Two qualitative and quantitative tables containing data related knowledge are used as an explicit form of data. With this knowledge the data preparation process can be shortened because miners are not mining data from zero knowledge.
PEMODELAN KOLABORASI MANUSIA-AI DAN KINERJA MANAGEMEN-EKONOMI: STUDI LINTAS SEKTOR BERBASIS DATA 2020–2025 Asep Amril Rudiyat; Yusuf Bayu Wicaksono
Jurnal Sekretaris dan Administrasi Bisnis Vol 9 No 2 (2025): Jurnal Sekretaris dan Administrasi Bisnis
Publisher : LPPM Universitas Taruna Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31104/jsab.v9i2.534

Abstract

This study aims to model the economic impact of human-AI collaboration using the "Global_AI_Content_Impact_Dataset.csv" spanning 2020–2025 across various countries and industries. The research employed regression models, including XGBoost, to assess the influence of the Human-AI Collaboration Rate (%) on Revenue Increase Due to AI (%) and Job Loss Due to AI (%). A key finding was the poor predictive performance of the models, evidenced by negative R² values, indicating that the selected features and models struggled to explain the variance in the economic outcomes. Consequently, interpretations of feature importance and observed sectoral paradoxes—such as high collaboration with low revenue increase in Marketing and Retail, or high collaboration with high job loss in Automotive and Manufacturing—are approached with extreme caution. These results are contextualized within the AI Productivity Paradox, suggesting that the observed period may represent an early phase where substantial complementary investments and organizational adjustments are still underway, masking immediate, quantifiable economic gains. The study underscores the limitations of current data in capturing the multifaceted nature of AI's economic integration and highlights the complex, evolving relationship between human-AI collaboration and economic performance, pointing towards the necessity for further research incorporating richer datasets, longitudinal analyses, and a deeper understanding of complementary organizational factors
Co-Authors Asep Amril Rudiyat Christina Juliane