Treffer: Analisis Perbandingan Kinerja Backend API Menggunakan PHP, Golang, dan JavaScript.

Efficient backend API development is crucial for supporting modern web applications. However, selecting the optimal programming language and query method remains a challenge for developers. This study aims to compare the performance of RESTful API backends built using three programming languages (Go, PHP, and JavaScript) and four data retrieval methods (Raw SQL, ORM, Query Builder, and Stored Procedure). The research method employed is quantitative true experimental, utilizing Load Testing, Spike Testing, and Stress Testing to evaluate successful request rates, CPU usage, and memory consumption. The test results show that Go with Raw SQL delivers the highest performance in terms of request handling, response time, and load management, followed by Node.js, while PHP has the lowest performance. [ABSTRACT FROM AUTHOR]

Pengembangan backend API yang efisien sangat penting dalam mendukung aplikasi web modern. Namun, pemilihan bahasa pemrograman dan metode query yang optimal masih menjadi tantangan bagi pengembang. Penelitian ini bertujuan untuk membandingkan kinerja backend RESTful API yang dibangun menggunakan tiga bahasa pemrograman (Go, PHP, dan JavaScript) serta empat metode pengambilan data (Raw SQL, ORM, Query Builder, dan Stored Procedure). Metode penelitian yang digunakan adalah kuantitatif true-experimental, dengan pengujian Load Testing, Spike Testing, dan Stress Testing untuk mengevaluasi jumlah permintaan yang berhasil, penggunaan CPU, dan penggunaan memori. Hasil pengujian menunjukkan bahwa Go dengan Raw SQL memiliki kinerja tertinggi dalam jumlah permintaan, waktu respons, dan penanganan beban, diikuti oleh Node.js, sementara PHP memiliki kinerja terendah. [ABSTRACT FROM AUTHOR]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)