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All Journal Digital Transformation Technology (Digitech) Technologia Journal
Dwi Puspita Anggraeni
Universitas Budi Luhur
Computer Science & IT Library & Information Science

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Rancang Bangun Perangkat Lunak Screening Saham Undervalue Dengan Sorting Kriteria Saham Dwi Puspita Anggraeni
Digital Transformation Technology Vol. 3 No. 1 (2023): Artikel Periode Maret Tahun 2023
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v3i1.2771

Abstract

Pandemi COVID di Indonesia yang terjadi pada 2019 telah memaksa dialihkannya kegiatan menjadi serba daring. Salah satu dampak positif dari keterpaksaan daring ini adalah belajar tidak lagi terbatas pada ruang dan waktu, termasuk dalam mempelajari ilmu persahaman. Hal ini turut andil dalam meningkatkan minat masyarakat untuk diversifikasi investasi dalam bentuk saham yang berdampak pada pertumbuhan jumlah investor saham baru. Salah satu teknik investasi saham yang cukup diminati adalah value investing. Screening saham yang termasuk undervalue memerlukan effort dan ketelitian. Hal ini akan sedikit rumit jika dilakukan secara manual. Oleh sebab itu diperlukan suatu perangkat lunak yang dapat melakukan grep data dan sorting secara otomatis untuk memperoleh saham undervalue terbaik. Parameter yang digunakan adalah nilai ROE, PER, DER, tren EPS dan dividen. Grep data dilakukan dari situs https://finance.yahoo.com/ Perangkat lunak yang dibuat diaplikasikan untuk desktop PC dan menghasilkan output berupa tabel excel hasil sorting data yang dapat diolah kembali sesuai kebutuhan user.
The Role of Artificial Intelligence in Increasing Efficiency and Productivity in the World of Work Dwi Puspita Anggraeni
Technologia Journal Vol. 2 No. 2 (2025): Technologia Journal - May
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/hm2gjc54

Abstract

The development of artificial intelligence (AI) has brought significant changes to the world of work, particularly in terms of efficiency and productivity. This study aims to analyze the role of AI in supporting operational efficiency, increasing employee productivity, and identifying challenges and opportunities arising from its implementation. The research method used was qualitative with a descriptive approach, where data was collected through in-depth interviews, observations, and documentation in organizations that have implemented AI. Data analysis was conducted using thematic analysis techniques to identify patterns, themes, and implications of AI use. The results show that AI can improve work efficiency by automating routine tasks, reducing human error, and accelerating the decision-making process. Furthermore, labor productivity increases because workers can focus more on strategic and value-added activities. However, challenges were also identified in the form of workforce resistance due to concerns about job losses and the need to improve digital skills. This study concludes that AI impacts not only technical aspects but also social and managerial dimensions, thus requiring a balanced integration strategy between technology and human resource development.
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