scholarly journals SISTEM PENDUKUNG KEPUTUSAN PENENTUAN INVESTASI KELAPA SAWIT DENGAN METODE INTERNAL RATE OF RETURN DALAM SATU BLOCK SAWIT DI LINGGA KUAMANG BUNGO JAMBI

Compiler ◽  
2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Bambang Sudibyo ◽  
Joko Suismianto
Keyword(s):  
Decision Support ◽  
Data Processing ◽  
Decision Support Systems ◽  
Palm Oil ◽  
Support Systems ◽  
Rate Of Return ◽  
Internal Rate Of Return ◽  
The Public ◽  
Internal Rate ◽  
Existing Data

ABSTRACTTechnology developed in order to facilitate human work that would not be done manually, with the presence of the latest technologies that it is not a work of man may not be done quickly, accurately, and save time. One was for the manufacture of palm data processing applications that provide an overview to the public that it is feasible to plant palm oil or not to do, based on existing data and then recycled into Decision Support Systems. To process these data using the Internal Rate Of Return (IRR) as a method to make decisions based on data that have been processed. In this study processed data is the data of a group of Palm Jl. Salak Linga Kuamang Bungo Jambi. From the results of the data managed by the farmer group Palm block 22, Jalan Salak, Linga Kuamang, with interpolation of 30% per year, with a capital of USD 888,253,000.00 and generate income of Rp 2,063,387,050.00 in if using the Internal rate of Return produce interpolation (I) of 30, 113%. Since IRR> I, then it is worth to continue.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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