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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
Atribut Destinasi Berbasis Data untuk Mengoptimalkan Segmentasi Pasar Pariwisata Global Yusuf Bayu Wicaksono; Asep Amril Rudiyat
Jurnal Sekretaris dan Administrasi Bisnis Vol 10 No 1 (2026): Jurnal Sekretaris dan Administrasi Bisnis
Publisher : LPPM Universitas Taruna Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31104/jsab.v10i1.556

Abstract

Persaingan yang semakin ketat dalam industri pariwisata global menuntut pendekatan berbasis data untuk pengambilan keputusan strategis. Penelitian ini bertujuan menganalisis atribut kota wisata dunia guna mengidentifikasi faktor kunci yang memengaruhi potensi bisnis pariwisata serta menginformasikan strategi branding destinasi, segmentasi pasar, dan pengembangan produk. Dengan menggunakan analisis data sekunder kuantitatif terhadap "Worldwide Travel Cities Dataset (Ratings and Climate)" yang mencakup 560 kota secara global, penelitian ini menerapkan statistik deskriptif, analisis korelasi, klasterisasi K-means, dan analisis tematik deskripsi destinasi. Temuan menunjukkan segmen destinasi yang berbeda berdasarkan tingkat anggaran dan profil atribut: destinasi mewah unggul dalam atribut budaya, kuliner, dan urban; destinasi hemat menunjukkan kekuatan dalam alam, petualangan, dan ketenangan; sementara destinasi menengah menempati posisi seimbang. Musiman iklim berdampak signifikan terhadap peluang bisnis, menuntut strategi yield management yang dinamis. Empat klaster optimal teridentifikasi: Petualangan-Alam-Budaya, Kota Klasik Eropa, Urban-Kuliner-Budaya, dan Pelarian Iklim Hangat, masing-masing dengan implikasi strategis unik. Kesimpulan menunjukkan bahwa analisis atribut destinasi berbasis data memungkinkan pelaku bisnis pariwisata mengembangkan pemasaran yang terarget, penawaran produk yang dipersonalisasi, dan strategi penetapan harga yang adaptif. Temuan ini menggarisbawahi perlunya pemangku kepentingan pariwisata memanfaatkan analitik data untuk peningkatan daya saing, pertumbuhan berkelanjutan, dan pemenuhan preferensi konsumen yang terus berkembang di pasar pariwisata global
Co-Authors Asep Amril Rudiyat Christina Juliane