Evaluasi AWS CodeDeploy dalam Meningkatkan Efisiensi Deployment Website Personal Image Studio (PIS) Japan Career Berbasis Cloud

This study evaluates the effectiveness of AWS CodeDeploy in enhancing the efficiency and reliability of website deployment for Personal Image Studio (PIS) Japan Career by utilizing cloud-based infrastructure. Manual deployment processes often involve multiple steps, are time-consuming, and highly susceptible to human error, which can lead to service disruption and operational inefficiencies. To address this issue, a quantitative experimental method was conducted by comparing two deployment approaches: traditional manual deployment and automated deployment using AWS CodeDeploy. The evaluation focused on two primary indicators: deployment time and system reliability. The results revealed a significant improvement in time efficiency, where automated deployment reduced the average deployment duration from 240 minutes to just 33 minutes — an efficiency gain of 86.25%. In terms of reliability, automated deployment achieved an 80% success rate, doubling the 40% success rate observed in manual deployment processes. Moreover, AWS CodeDeploy offers several advanced features such as automatic rollback on failure, seamless integration with monitoring services like AWS CloudWatch, and support for continuous deployment pipelines. These capabilities not only minimize downtime and configuration errors but also ensure consistent and scalable application delivery. Overall, the findings demonstrate that AWS CodeDeploy is a powerful tool that significantly improves the performance and dependability of cloud-based deployment workflows for modern web applications.

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Publication Details

Journal
Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Published
2025-09-29
DOI
https://doi.org/10.55606/jupsim.v4i3.5398
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Evaluasi AWS CodeDeploy dalam Meningkatkan Efisiensi Deployment Website Personal Image Studio (PIS) Japan Career Berbasis Cloud

Maria Amsilia Koza
Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Data Mining and Machine Learning Applications
article

Evaluasi AWS CodeDeploy dalam Meningkatkan Efisiensi Deployment Website Personal Image Studio (PIS) Japan Career Berbasis Cloud

Maria Amsilia Koza
article en

Abstract

This study evaluates the effectiveness of AWS CodeDeploy in enhancing the efficiency and reliability of website deployment for Personal Image Studio (PIS) Japan Career by utilizing cloud-based infrastructure. Manual deployment processes often involve multiple steps, are time-consuming, and highly susceptible to human error, which can lead to service disruption and operational inefficiencies. To address this issue, a quantitative experimental method was conducted by comparing two deployment approaches: traditional manual deployment and automated deployment using AWS CodeDeploy. The evaluation focused on two primary indicators: deployment time and system reliability. The results revealed a significant improvement in time efficiency, where automated deployment reduced the average deployment duration from 240 minutes to just 33 minutes — an efficiency gain of 86.25%. In terms of reliability, automated deployment achieved an 80% success rate, doubling the 40% success rate observed in manual deployment processes. Moreover, AWS CodeDeploy offers several advanced features such as automatic rollback on failure, seamless integration with monitoring services like AWS CloudWatch, and support for continuous deployment pipelines. These capabilities not only minimize downtime and configuration errors but also ensure consistent and scalable application delivery. Overall, the findings demonstrate that AWS CodeDeploy is a powerful tool that significantly improves the performance and dependability of cloud-based deployment workflows for modern web applications.

Jurnal Publikasi Sistem Informasi dan Manajemen BisnisVol. 4(3)
Universitas Flores (ID)
Openalex Percentile: Top 61%
Data Mining and Machine Learning Applications
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