Pengembangan RESTful API untuk aplikasi pengenalan jenis kendaraan berbasis komputasi awan = Development of RESTful API for cloud-based vehicle type recognition application

Untoro, Reinhart (2023) Pengembangan RESTful API untuk aplikasi pengenalan jenis kendaraan berbasis komputasi awan = Development of RESTful API for cloud-based vehicle type recognition application. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

The current advancement in technology enables individuals to access information on anything they wish to know through the internet, anytime and anywhere. People of all ages can benefit from this technological progress. Parents often provide gadgets to their young children, allowing them to access information on various subjects, which may pose a challenge as children can access age-inappropriate content, presenting a new challenge for parents. One solution to address this challenge is to build or develop an application to assist children in identifying objects around them, particularly different types of vehicles. In this paper, the author focuses on discussing the practical implementation of cloud computing in the development of this application. This research will use some of recent Bangkit Academy 2023 capstone project machine learning source. The implementation involves creating a RESTful Application Programming Interface (API), serving a machine learning model with Docker, and deploying the model on Cloud Run. It is anticipated that this implementation will result in a successful and tested RESTful Application Programming Interface (API). The implementation results in a tested RESTful Application Programming Interface (API), evaluated through unit testing on the API and workload modeling within Docker. Unit testing assesses the presence of appropriate files, the absence of files, and the correctness of files. The outcome of this testing is an API that passes these evaluations. Meanwhile, workload modeling testing on the API in Docker utilizes Apache JMeter to assess the API's response time. The results of this testing include the Latest Sample or response time in the last sample, which is 1606 ms, Average or the average response time at 1502 ms, and Deviation or the consistency of response time at 122 ms. The results of the workload modeling testing align with expectations. / Kemajuan teknologi saat ini telah memungkinkan orang untuk mencari informasi apa pun yang ingin diketahuinya melalui internet, kapan pun dan di mana pun. Orang-orang dari segala usia dapat menikmati kemajuan teknologi ini. Orang tua sering memberikan gadget kepada anak-anaknya yang masih kecil, membuat mereka dapat mengakses informasi tentang segala hal yang dapat menjadi masalah di mana anak-anak dapat mengakses informasi yang tidak sesuai rentang umurnya sehingga menjadi tantangan baru bagi orang tua. Muncul salah satu solusi untuk mengatasi tantangan tersebut, solusi tersebut adalah membangun atau mengembangkan aplikasi untuk membantu anak-anak dalam mengenal benda-benda di sekitar mereka, khususnya jenis kendaraan. Dalam karya tulis ini penulis akan memfokuskan dalam membahas praktik implementasi cloud computing dalam pembuatan aplikasi ini. Penelitian ini akan menggunakan sumber daya pembelajaran mesin yang sudah dikerjakan sebelumnya pada Bangkit Academy 2023. Implementasi cloud computing pada pembuatan aplikasi ini adalah membuat RESTful Application Programming Interface (API), melakukan serving model pembelajaran mesin dengan Docker, dan men-deploy model di Cloud Run. Diharapkan implementasi tersebut dapat memiliki hasil akhir, yaitu RESTful Application Programming Interface (API) yang berhasil dan teruji. Hasil implementasi merupakan RESTful Application Programming Interface (API) yang teruji dengan menggunakan pengujian unit test pada RESTful Application Programming Interface (API) dan workload model pada RESTful Application Programming Interface (API) di dalam Docker. Pengujian unit test menggambarkan jika terdapat file yang sesuai, tidak ada file, dan file tidak sesuai. Hasil dari pengujian tersebut merupakan API yang lolos dari pengujian tersebut. Sedangkan pengujian workload model pada API dalam Docker menggunakan Apache JMeter yang menguji response time dari API. Hasil dari pengujian tersebut merupakan Latest Sample atau response time pada sampel terakhir, yaitu 1606 ms, Average atau rata-rata response time, yaitu 1502 ms, dan Deviation atau kekonsistenan response time, yaitu 122 ms. Hasil pengujian workload model tersebut sesuai dengan ekspektasi.

Item Type: Thesis (Bachelor)
Creators:
CreatorsNIMEmail
Untoro, ReinhartNIM01082200026reinhart377@gmail.com
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorMurwantara, I MadeNIDN0302057305made.murwantara@uph.edu
Uncontrolled Keywords: RESTful API; pengenalan; komputasi awan
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > School of Information Science and Technology > Informatics
Current > Faculty/School - UPH Karawaci > School of Information Science and Technology > Informatics
Depositing User: REINHART REINHART
Date Deposited: 07 Feb 2024 02:27
Last Modified: 07 Feb 2024 02:27
URI: http://repository.uph.edu/id/eprint/61567

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