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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Gede Susrama Masdiyasa
Institut Teknologi Sepuluh November Surabaya
Computer Science & IT

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Graph-QL Responsibility Analysis at Integrated Competency Certification Test System Base on Web Service I Gede Susrama Masdiyasa; Gideon Setya Budiwitjaksono; Hafidz Amarul M; Ilham Ade Widya Sampurno; Ni Made Ika Marini Mandenni
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 11 No 2 (2020): Vol. 11, No. 2 August 2020
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2020.v11.i02.p05

Abstract

Graph-QL (Query Language) is a new concept in the Application Programming Interface (API). Graph-QL was developed by Facebook which is implemented on the server-side. Although it is a query language, Graph-QL is not directly related to the database, in other words, Graph-QL is not limited to certain databases, either SQL or NoSQL. The position of Graph-QL is on the client and server-side that access an API. One of the objectives of developing this query language is to facilitate data communication between the backend and frontend / mobile applications. For this reason, this paper will examine the responsibility of Graph-QL in terms of response time and response size in the development of an integrated competency certification test system based on web service and compared with efficiency and flexibility using the REST API. From the test results, it was found that Graph-QL provided some advantages compare to REST API. It give more flexibility for the clients to access the data and solve the most typical problem that was over or under fetching cause by fixed data given by REST API endpoints.
Modified Background Subtraction Statistic Models for Improvement Detection and Counting of Active Spermatozoa Motility I Gede Susrama Masdiyasa; I D. G. Hari Wisana; I K. Eddy Purnama; M. Hery Purnomo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 9, No. 1 April 2018
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (437.904 KB) | DOI: 10.24843/LKJITI.2018.v09.i01.p04

Abstract

An important early stage in the research of sperm analysis is the phase of sperm detection or separating sperm objects from images/video obtained from observations on semen. The success rate in separating sperm objects from semen fluids has an important role for further analysis of sperm objects. Algorithm or Background subtraction method is a process that can be used to separate moving objects (foreground) and background on sperm video data that tend to uni-modal. In this research, some of the subproject model statistics of substrata model are Gaussian single, Gaussian Mixture Model (GMM), Kernel Density Estimation and compared with some basic subtraction model background algorithm in detecting and counting the number of active spermatozoa. From the results of the tests, the Grimson GMM method has an f-measure value of 0.8265 and succeeded in extracting the sperm form near its original form compared to other methods
Co-Authors Gideon Setya Budiwitjaksono Hafidz Amarul M I D. G. Hari Wisana I K. Eddy Purnama Ilham Ade Widya Sampurno M. Hery Purnomo Ni Made Ika Marini Mandenni