scholarly journals Analisis Penerapan Electronic Data Processing (EDP) System dalam Pengolahan Data Penjualan dan Penerimaan Kas Pada PT Hadji Kalla Cabang Parepare

Author(s):  
Arfianty Arfan

Tujuan penelitian ini adalah untuk mengetahui mekanisme yang digunakan PT Hadji Kalla Cabang Parepare dalam pengolahan data penjualan dan penerimaan kas; dan mengetahui apakah penerapan electronic data processing (EDP) System pada PT Hadji Kalla Cabang Parepare telah mendukung pencapaian informasi akuntansi yang akurat. Adapun sumber data yang digunakan dalam penelitian ini meliputi place, paper, dan person. Teknik analisis data yang digunakan dalam penelitian ini adalah teknik analisis deskriptif kualitatif, yaitu metode analisis data yang menggambarkan dan menginterpretasikan serta membandingkan data-data penelitian yang diperoleh dari perusahaan dengan literatur-literatur yang ada untuk dianalisa sehingga memberikan keterangan bagi pemecahan masalah yang dihadapi. Setelah melalui proses pengumpulan, penyusunan, dan analisis data, maka diperoleh  hasil bahwa dalam penerapan electronic data processing (EDP) system, PT Hadji Kalla Cabang Parepare menggunakan mekanisme pengolahan data secara langsung (real time processing) yang dapat dibuktikan melalui tabel indikator real time processing yang telah disesuaikan berdasarkan hasil dari wawancara dan observasi,dan juga diperoleh hasil bahwa penerapan electronic data processing (EDP) telah mendukung pencapaian informasi akuntansi yang akurat berdasarkan komponen-komponen electronic data processing (EDP) system pada PT Hadji Kalla Cabang Parepare telah memadai.   

2021 ◽  
Author(s):  
Masaya Misaki ◽  
Jerzy Bodurka ◽  
Martin P Paulus

We introduce a python library for real-time fMRI (rtfMRI) data processing systems, Real-Time Processing System in python (RTPSpy), to provide building blocks for a custom rtfMRI application with extensive and advanced functionalities. RTPSpy is a library package including 1) a fast, comprehensive, and flexible online fMRI denoising pipeline comparable to offline processing, 2) utilities for fast and accurate anatomical image processing to define a target region on-site, 3) a simulation system of online fMRI processing to optimize a pipeline and target signal calculation, 4) interface to an external application for feedback presentation, and 5) a boilerplate graphical user interface (GUI) integrating operations with RTPSpy library. Since online fMRI data processing cannot be equivalent to offline, we discussed the limitations of online analysis and their solutions in the RTPSpy implementation. We developed a fast and accurate anatomical image processing script with fast tissue segmentation (FastSeg), image alignment, and spatial normalization, utilizing the FastSurfer, AFNI, and ANTs. We confirmed that the FastSeg output was comparable with FreeSurfer, and could complete all the anatomical image processing in a few minutes. Thanks to its highly modular architecture, RTPSpy can easily be used for a simulation analysis to optimize a processing pipeline and target signal calculation. We present a sample script for building a real-time processing pipeline and running a simulation using RTPSpy. The library also offers a simple signal exchange mechanism with an external application. An external application can receive a real-time neurofeedback signal from RTPSpy in a background thread with a few lines of script. While the main components of the RTPSpy are the library modules, we also provide a GUI class for easy access to the RTPSpy functions. The boilerplate GUI application provided with the package allows users to develop a customized rtfMRI application with minimum scripting labor. Finally, we discussed the limitations of the package regarding environment-specific implementations. We believe that RTPSpy is an attractive option for developing rtfMRI applications highly optimized for individual purposes. The package is available from GitHub (https://github.com/mamisaki/RTPSpy) with GPL3 license.


2019 ◽  
Vol 75 ◽  
pp. 03003 ◽  
Author(s):  
Oleg Yakubailik ◽  
Victor Romas'ko ◽  
Evgeny Pavlichenko

The basic problems and trends in the development of modern systems for the reception, storage and real-time processing of satellite data are considered. Abrupt increase in the capability of satellite systems, significant increase in the amount of satellite information and its availability, the development of data processing and presentation technologies, and the use of web technologies are discussed. Data sources of modern remote sensing systems of the Earth and the features of their practical use are considered. It is concluded that the most effective way to obtain real-time information from meteorological satellites are satellite stations that receive data in the X-band at a frequency of 8 GHz. The performance characteristics and capabilities of the equipment of the new satellite receiving complex at Krasnoyarsk Science Center are given. Use of up-to-date computer equipment (high-performance servers and storage systems, local area network with a bandwidth of 10 Gbit/s) and logical separation into the stages of data conversion (data reception, primary and thematic processing) provide the construction of a modern scalable data-processing system for remote sensing data. The paper presents the results of the work on creation of specialized software for information and analytical systems for real-time satellite monitoring.


2018 ◽  
Vol 7 (3.13) ◽  
pp. 79
Author(s):  
Bhavani Buthukuri ◽  
Sivaram Rajeyyagari

MapReduce is the most widely used for huge data processing and it is a part of the Hadoop big data and this will provide the quality and efficient results because of their processing functions. For the batch jobs, Hadoop is the proper and also there is inflated request for non-batch elements homogeneous interactive jobs, and high data currents. For this non-batch assignments, consider Hadoop is not useful and present situations are recommending to these new crises. In this paper, these are divided into two stages that are real-time processing, and stream processing of big data. For every stage, the models are deliberate, stability and diversity to Hadoop. For every group, we have provided the working systems and structures. For the creation of the new examples, some experiments are conducted to improve the new results belongs to available Hadoop-based solutions.      


Author(s):  
Daiki Matsumoto ◽  
Ryuji Hirayama ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Shimobaba ◽  
...  

Author(s):  
David J. Lobina

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.


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