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All Journal Jurnal Teknologi Informatika dan Komputer Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK)
Zega, Agusniaman
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Perbandingan Densitas Pencarian Internet Untuk Teknologi Terbaru Berdasarkan Data Google Trends Buulolo, Santi Trimurni; Zega, Agusniaman; Zendrato, Sinar Elsa
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 2 No. 1 (2025): IDENTIK - Januari
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v2i1.249

Abstract

This research aims to conduct a comprehensive comparative analysis of internet search densities for emerging technologies using Google Trends data. By examining search patterns across multiple cutting-edge technological domains, we explore the relative public interest and digital engagement with innovative technological developments. The study analyzes search trends for technologies such as artificial intelligence, quantum computing, blockchain, augmented reality, and renewable energy technologies between 2019 and 2024. Utilizing advanced data mining and statistical analysis techniques, we extract meaningful insights into technological awareness, global interest patterns, and potential correlations between search densities and technological advancement. The research methodology involves systematic data collection from Google Trends, comprehensive statistical processing, and in-depth comparative analysis. Results reveal significant variations in search densities across different technological domains, highlighting emerging technological landscapes and potential future innovation trajectories.
Implementation of the FIFO System and Its Impact on Asset Stock Accuracy at the Nias Regency Statistics Office Waruwu, Candry Yurlina; Telaumbanua, Rina Novianti; Zega, Agusniaman; Zega, Osadikman; Waruwu, Jurisman; Zai, Arliyanto
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i2.2728

Abstract

State Property (BMN) is a crucial asset that supports the operations of government agencies and reflects state assets that must be managed in an orderly, efficient, and accountable manner. BMN management encompasses recording, maintenance, data updating, and reporting, which require a fast, accurate, and easily accessible information system. In response to these challenges, this study aims to implement a web-based BMN recording and updating system using the FIFO (First In, First Out) method. This system is expected to improve recording accuracy, accelerate reporting, and support digital transformation in asset management within the Nias Regency Statistics Agency (BPS). Implementing the FIFO (First In, First Out) system is a strategic step to improve the accuracy of asset stock recording at the Nias Regency Statistics Agency (BPS) Office. Previously, manual spreadsheet-based asset management led to the risk of recording errors, reporting delays, and a lack of efficiency. This study aims to develop and implement a web-based system that supports the FIFO method to improve the accuracy and efficiency of asset stock management. Research data was collected through observation, interviews, and document analysis, using a waterfall software development approach. The results showed that implementing the FIFO system improved stock accuracy by up to 95%, accelerated the audit process, and simplified reporting. Further development, such as integration with cloud technology and mobile applications, is recommended to support sustainable and efficient asset management within the Nias Regency BPS.
Implementation of the FIFO System and Its Impact on Asset Stock Accuracy at the Nias Regency Statistics Office Waruwu, Candry Yurlina; Telaumbanua, Rina Novianti; Zega, Agusniaman; Zega, Osadikman; Waruwu, Jurisman; Zai, Arliyanto
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i2.2728

Abstract

State Property (BMN) is a crucial asset that supports the operations of government agencies and reflects state assets that must be managed in an orderly, efficient, and accountable manner. BMN management encompasses recording, maintenance, data updating, and reporting, which require a fast, accurate, and easily accessible information system. In response to these challenges, this study aims to implement a web-based BMN recording and updating system using the FIFO (First In, First Out) method. This system is expected to improve recording accuracy, accelerate reporting, and support digital transformation in asset management within the Nias Regency Statistics Agency (BPS). Implementing the FIFO (First In, First Out) system is a strategic step to improve the accuracy of asset stock recording at the Nias Regency Statistics Agency (BPS) Office. Previously, manual spreadsheet-based asset management led to the risk of recording errors, reporting delays, and a lack of efficiency. This study aims to develop and implement a web-based system that supports the FIFO method to improve the accuracy and efficiency of asset stock management. Research data was collected through observation, interviews, and document analysis, using a waterfall software development approach. The results showed that implementing the FIFO system improved stock accuracy by up to 95%, accelerated the audit process, and simplified reporting. Further development, such as integration with cloud technology and mobile applications, is recommended to support sustainable and efficient asset management within the Nias Regency BPS.
Co-Authors Arliyanto Zai Buulolo, Santi Trimurni Jurisman Waruwu Telaumbanua, Rina Novianti Waruwu, Candry Yurlina Zega, Osadikman Zendrato, Sinar Elsa