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All Journal BIOS: Jurnal Informatika dan Sains
Muhammad Iqbal
Pascasarjana, Magister Teknologi Informasi, Universitas Pembangunan Pancabudi, Medan, Indonesia
Computer Science & IT

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Journal : BIOS: Jurnal Informatika dan Sains

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Implementation of Paperless System for Documentation Process Efficiency at PT Everbright Irwan Syahputra; Muhammad Iqbal
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 02 (2024): BIOS: Jurnal Informatika dan Sains, October 2024
Publisher : Sean Institute

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Abstract

PT Everbright faces challenges in managing documentation that still relies on a paper-based system, resulting in inefficiencies in storing, searching, and distributing documents. This system also increases operational costs and increases the risk of document loss or damage. Therefore, this study aims to implement a paperless system at PT Everbright to improve the efficiency of the documentation process. By utilizing digital technology, such as document management software and electronic filing systems, companies can reduce dependence on paper, accelerate information access, and optimize internal workflows. This study evaluates the effectiveness of implementing a paperless system in reducing the time required for document searches, minimizing document management errors, and reducing operational costs. The expected results are increased efficiency, reduced costs, and contributions to environmental sustainability through reduced paper use. Thus, the paperless system is expected to provide a solution for PT Everbright to achieve a more efficient, safe, and environmentally friendly documentation process.
Career Pattern Analysis of SMKN 1 Stabat Graduates Using K-Means Clustering Algorithm on Tracer Study Dataset Ibrahim Ibrahim; Muhammad Iqbal
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 01 (2024): BIOS: Jurnal Informatika dan Sains, April 2024
Publisher : Sean Institute

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Abstract

Tracer study is a method commonly used to determine the condition of graduates of an educational institution, including the career patterns they pursue. This study aims to analyze the career patterns of SMKN 1 Stabat graduates by utilizing the K-Means clustering algorithm. The dataset was obtained from the results of a tracer study of 287 alumni of SMKN 1 Stabat. The dataset used came from a tracer study conducted on graduates in the last five years. By grouping data using K-Means, it is hoped that specific patterns can be found that can help schools improve the quality of learning and student work readiness.[4] The results of the analysis show several dominant career pattern groups, such as the industrial sector, entrepreneurship, and further education.
Co-Authors Ibrahim Ibrahim Irwan Syahputra