scholarly journals Effect of Home Telehealth Data Quality on Decision Support System Performance

2015 ◽  
Vol 64 ◽  
pp. 352-359 ◽  
Author(s):  
Mas S. Mohktar ◽  
Jumadi Abd Sukor ◽  
Stephen J. Redmond ◽  
Jim Basilakis ◽  
Nigel H. Lovell
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Quality ◽  
Support System ◽  
System Performance ◽  
Home Telehealth

scholarly journals Perancangan Decision Support System Penilaian Kinerja Dosen Berdasarkan Penilaian Prestasi Kerja Pegawai dan Beban Kinerja Dosen

MATICS ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 48
Author(s):  
Rizal Furqan Ramadhan ◽  
Herman Tolle ◽  
Muhammad Aziz Muslim
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Performance Assessment ◽  
Support System ◽  
System Performance ◽  
Normal Medium ◽  
Higher Education System ◽  
Final Output ◽  
University Lecturers ◽  
Education Concept

<span>The lecturer is one of the essential<br />components in the Higher Education system. Performance<br />assessment of lecturer needs to be conducted to measure<br />the lecturer capability based on the Tri Darma’s Higher<br />Education concept. Related to the nowadays technology<br />development, to conduct performance assessment of<br />lecturer can use the Decision Support System based on<br />several criteria as the assessment material. The provided<br />criteria in this paper seem to be the obtained criteria from<br />P2KP and BKD component. P2KP is performance<br />assessment of lecturer under the Badan Kepegawaian<br />Negara (BKN) supervision. Meanwhile BKD is<br />performance assessment of lecturer under the DIKTI<br />supervision. The lecturer criteria are taken from those two<br />components because the lecturers’ status cannot be<br />separated from the officer under BKN and educator under<br />the DIKTI support. It is expected that the criteria coming<br />from both components integration will be able to produce<br />performance assessment of lecturer objectively. The<br />method to proceed the assessment was Weighted Product<br />(WP). The examined data of the lecturers were the<br />Brawijaya University lecturers’ data. The final<br />examination data was conducted by taking the data<br />randomly from 20 Brawijaya University lecturers. The<br />final output from this Decision Support System is the<br />lecturers which are selected from three categories, which<br />are, less, normal, and good. It is expected that Decision<br />Support System is able to categorize the standard eligible<br />lecturer (Normal/medium category), and the lecturer<br />surpassing the standard (good category).<br /></span>

Design of a Decision Support System for a Home Telehealth Application

2013 ◽  
Vol 4 (3) ◽  
pp. 68-79 ◽  
Author(s):  
Mas S. Mohktar ◽  
Kezhang Lin ◽  
Stephen J. Redmond ◽  
Jim Basilakis ◽  
Nigel H. Lovell
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Open Source Software ◽  
Business Processes ◽  
Rule Engine ◽  
Clinical Support ◽  
Tier System ◽  
Home Telehealth ◽  
Data Layer

A decision support system (DSS) that has been designed to manage patients using a home telehealth system is presented. The DSS has been developed to assist home telehealth clinical support staff with their workload, and to provide more effective communication between multiple home telehealth users. The three-tier system architecture that consists of a data layer; a business logic layer; and a front-end layer employs business processes and uses a rule engine for its logic and knowledge base. This paper discusses the design considerations involved in the construction of a DSS for the purpose of home telehealth, and illustrates how it may be developed using entirely open source software.

scholarly journals A Recommendation System for People Analytics

2021 ◽  
Vol 12 (2) ◽  
pp. 1-12
Author(s):  
Nan Wang ◽  
Evangelos Katsamakas
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Performance ◽  
Recommendation System ◽  
Business Value ◽  
Computational Experiments ◽  
Hybrid Architecture ◽  
Learning Approaches

Companies seek to leverage data and people analytics to maximize the business value of their talent. This article proposes a recommendation system for personalized workload assignment in the context of people analytics. The article describes the system, which follows a novel two-level hybrid architecture. We evaluate the system performance in a series of computational experiments and discuss future extensions. Overall, the proposed system could create significant business value as a decision support system that could help managers make better decisions. The article demonstrates how computational and machine learning approaches can complement humans in improving the performance of organizations.

Design features of decision support systems with adaptation elements

2020 ◽  
pp. 58-68
Author(s):  
Э.Э. Акимкина
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
System Performance ◽  
Support Systems ◽  
Multidimensional Data ◽  
Internal Environment ◽  
Data Warehouses ◽  
Design Features

Рассмотрены вопросы повышения эффективности систем поддержки принятия решений на основе многомерных хранилищ данных, имеющие существенное значение для выполнения требований по увеличению быстродействия систем. Показано, что эффективность функционирования системы поддержки принятия решений возрастает при введении в нее элементов обслуживания, которые позволяют учитывать изменение условий внешней и внутренней среды. Разработана методика проектирования системы поддержки принятия решений, учитывающая особенности ее адаптации к изменяющимся условиям с помощью элементов обслуживания. Issues of increasing the effectiveness of decision support systems based on multidimensional data warehouses are considered, which are essential for fulfilling the requirements to increase system performance. It is shown that the effectiveness of the functioning of the decision support system increases with the introduction of service elements in it, which allow taking into account changes in the conditions of the external and internal environment. A methodology has been developed for designing a decision support system that takes into account the peculiarities of its adaptation to changing conditions using service elements.

scholarly journals A Recommendation System for People Analytics

2021 ◽  
Vol 12 (2) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Performance ◽  
Recommendation System ◽  
Business Value ◽  
Computational Experiments ◽  
Hybrid Architecture ◽  
Learning Approaches

Companies seek to leverage data and people analytics to maximize the business value of their talent. This article proposes a recommendation system for personalized workload assignment in the context of people analytics. The article describes the system, which follows a novel two-level hybrid architecture. We evaluate the system performance in a series of computational experiments and discuss future extensions. Overall, the proposed system could create significant business value as a decision support system that could help managers make better decisions. The article demonstrates how computational and machine learning approaches can complement humans in improving the performance of organizations.

Validation of a Decision Support System for Improving Irrigation System Performance

2015 ◽  
Vol 141 (5) ◽  
pp. 04014067 ◽  
Author(s):  
Kristoph-Dietrich Kinzli ◽  
David Gensler ◽  
Kendall DeJonge ◽  
Ramchand Oad ◽  
Nabil Shafike
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Performance ◽  
Irrigation System
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