SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI TOPIK JUDUL SKRIPSI MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER

Decision Support System (DSS) is a computer-based interactive application that combines data and mathematical models to assist the decision-making process in dealing with a problem.[1] At the Faculty of Engineering, Informatics Study Program, Nurul Jadid University, there are several stages in the process of preparing a thesis that students need to do, namely submission of thesis titles, submission of thesis proposals, proposal seminars, research and thesis guidance. After writing is considered ready and finished, students present the results of their thesis at the lecturer examines the thesis exam, but students whose thesis exam results pass with revisions, carry out the revision process in accordance with the examiner's input. The problem that is often experienced by students at the Faculty of Engineering Informatics Study Program is the process of submitting thesis titles, where students have difficulty determining the topic of thesis title. Then a Decision Support System for Thesis Title Topic Recommendations was created using the Naive Bayes Classifier Method at the Faculty of Engineering, Informatics Study Program, Nurul Jadid University, which aims to help facilitate lecturers and students in the management process of determining the recommendation criteria for thesis title topics, the process of managing thesis title recommendations and thesis title submissions, the method used is prototyping with the PHP programming language, MySQL database and the Naive Bayes Classifier method, for system design using Flowcharts, DFD, and ERD. Based on the results of the Naive Bayes Classifier method, it produces test results with very good Likert scale calculations with a high accuracy value of 96.6%

Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review

<p>Abstract</p> <p>Background</p> <p>Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit.</p> <p>Methods</p> <p>The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system.</p> <p>Results</p> <p>Data will be summarized using descriptive summary measures, including proportions for categorical variables and means for continuous variables. Univariable and multivariable logistic regression models will be used to investigate associations between outcomes of interest and study specific covariates. When reporting results from individual studies, we will cite the measures of association and p-values reported in the studies. If appropriate for groups of studies with similar features, we will conduct meta-analyses.</p> <p>Conclusion</p> <p>A decision-maker-researcher partnership provides a model for systematic reviews that may foster knowledge translation and uptake.</p

\u3cem\u3eWater Expert\u3c/em\u3e: A Conceptualized Framework for Development of a Rule-Based Decision Support System for Distribution System Decontamination

Significant drinking water contamination events pose a serious threat to public and environmental health. Water utilities often must make timely, critical decisions without evaluating all facets of the incident. The data needed to enact informed decisions are inevitably dispersant and disparate, originating from policy, science, and heuristic contributors. Water Expert is a functioning hybrid decision support system (DSS) and expert system framework that emphasizes the meshing of parallel data structures in order to expedite and optimize the decision pathway. Delivered as a thin-client application through the user\u27s web browser, Water Expert\u27s extensive knowledgebase is a product of inter-university collaboration that methodically pieced together system decontamination procedures. Decontamination procedures are investigated through consultation with subject matter experts, literature review, and prototyping with stakeholders. This paper discusses the development of Water Expert, analyzing the development process underlying the DSS and the system\u27s existing architecture specifications. Water Expert constitutes the first system to employ a combination of deterministic and heuristic models which provide decontamination solutions for water distribution systems. Results indicate that the decision making process following a contamination event is a multi-disciplinary effort. This contortion of multiple inputs and objectives limit the ability of the decision maker to find optimum solutions without technological intervention

Demand Forecasting for Food Production Using Machine Learning Algorithms: A Case Study of University Refectory

Accurate food demand forecasting is one of the critical aspects of successfully managing restaurants, cafeterias, canteens, and refectories. This paper aims to develop demand forecasting models for a university refectory. Our study focused on the development of Machine Learning-based forecasting models which take into account the calendar effect and meal ingredients to predict the heavy demand for food within a limited timeframe (e.g., lunch) and without pre-booking. We have developed eighteen prediction models gathered under five main techniques. Three Artificial Neural Network models (i.e., Feed Forward, Function Fitting, and Cascade Forward), four Gauss Process Regression models (i.e., Rational Quadratic, Squared Exponential, Matern 5/2, and Exponential), six Support Vector Regression models (i.e., Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian), three Regression Tree models (i.e., Fine, Medium, and Coarse), two Ensemble Decision Tree (EDT) models (i.e., Boosted and Bagged) and one Linear Regression model were applied. When evaluated in terms of method diversity, prediction performance, and application area, to the best of our knowledge, this study offers a different contribution from previous studies. The EDT Boosted model obtained the best prediction performance (i.e., Mean Squared Error = 0,51, Mean Absolute Erro = 0,50, and R = 0,96)

Development of an integrated forest management decision support system: integrating the LANDIS model and ArcGIS

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on April 10, 2009)Includes bibliographical references.Thesis (M.A.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Geography.Forest management is an important field for Decision Support System (DSS) application, but most of the current DSSs for forest management are not fully successful because: 1) the user interface is not friendly, or 2) GIS functions are not fully integrated into the system. These limitations unnecessarily reduce the use of DSS planning tools by forest managers, and decrease practical feedback from managers that could aid in further development of the landscape models. This research presents a universal method to develop a Forest Management Decision Support System (FMDSS) by integrating the LANDIS 4.0 model with the ESRI ArcGIS platform. FMDSS was developed with Visual Basic, ESRI ArcObjects libraries and Microsoft Access database. FMDSS eliminates the time-consuming parameter editing work necessary for the LANDIS model, simplifies the technical operations of running the model, and allows managers to focus on evaluating their management plans. A case study is presented, applying FMDSS to data from the Mark Twain National Forest. This demonstration illustrates how the decision making process is simplified by FMDSS, using the example of creating a new management area for fuel and harvest treatments

Design of an anaerobic biodigestion system utilizing the organic fraction of municipal solid waste for biogas production in an urban environment

M.Tech. (Chemical Engineering)The design process was carried out in two stages: feedstock analysis and system design. Under feedstock analysis, the study investigated the amount of the organic fraction of municipal solid waste (OFMSW) generated at the study area which was situated at the University of Johannesburg’s Doornfontein Campus (UJ DFC) in downtown Johannesburg South Africa. Furthermore, the feedstock analyses involved characterisation studies on the target waste under which several laboratory tests were undertaken. The system design involved sizing of the suitable biogas digester to be used in the system applying mathematical models and feedstock parameters obtained from the feedstock analyses. Via the application of the Simple Multi-Attribute Rating (SMART) technique of multiple-criteria decision analysis (MCDA) as a decision support tool, the most preferred option of biogas plant model was selected from a list of potential alternatives available on the market. And, in addition, a suitable site around the study area was selected by applying the analytical hierarchy process (AHP) technique of MCDA. Other system components and accessories such as the piping, scrubbers and valves were sized, selected, integrated into the system and finally layout drawings were produced using Inventor computer aided drafting (CAD) Software. Furthermore, feasibility assessments were conducted on the proposed system such as energy usage assessments and economic analyses using the net present value (NPV), internal rate of return (IRR) and benefit-cost ratio (BCR) techniques..

Staying ahead of the curve : modeling and public health decision-making

Where are infections spreading? How many people will be affected? What are some different ways to stop the spread of an epidemic? These are questions that the public and decision-makers, including health officials, often ask during an outbreak or emergency. In a process known as modeling, scientists analyze data using complex mathematical methods to provide answers to these and other questions during an emergency response. Just as models are used to predict the path of a hurricane, models can be used to predict the impact of interventions during an epidemic. Modeling is helpful in more than just emergency situations, though. For example, models are also used to predict when the next flu season will start and to decide which flu strains to include in the flu shot each year.Models provide the foresight that can help decision-makers better prepare for the future. Modelers attempt to use all available data to formulate predictions. As more data accumulate, the accuracy of predictions improves. Models can also help us understand situations that were unclear in the past by looking at old data in new ways. With models, decision-makers can look to the future with confidence in their ability to respond to outbreaks and public health emergencies.Tuesday, January 19, 2016 at 1pm ETPresented by: Lauren Ancel Meyers, PhD, Professor, Department of Integrative Biology, Department of Statistics and Data Sciences, University of Texas at Austin ["Modeling to Support Outbreak Preparedness, Surveillance and Response"]; Martin Meltzer, PhD, Lead, Health Economics and Modeling Unit, Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, CDC ["What Do Policy Makers Expect from Modelers during a Response?"]; Daniel Jernigan, MD, Director, Influenza Division, National Center for Immunization and Respiratory Diseases, CDC ["Application of Modeling and Forecasting for Preventing Influenza"]; Richard Hatchett, MD, Chief Medical Officer, Deputy Director for Strategic Sciences, Biomedical Advanced Research and Development Authority, Office of the Assistant Secretary for Preparedness and Response ["Explaining Phenomena, Providing Foresight, and Making Predictions"].Facilitated by: John Iskander, MD, MPH, Scientific Director, Public Health Grand Rounds; Phoebe Thorpe, MD, MPH, Deputy Scientific Director, Public Health Grand Rounds; Susan Laird, MSN, RN, Communications Director, Public Health Grand Rounds.Modeling to Support Outbreak Preparedness, Surveillance and Response [PDF version of the PowerPoint presentation by Lauren Ancel Meyers, p. 2-14] -- What Do Policy Makers Expect from Modelers during a Response?"] [PDF version of the PowerPoint presentation by Martin Meltzer, p. 15-27]-- Application of Modeling and Forecasting for Preventing Influenza [PDF version of the PowerPoint presentation by Daniel B. Jernigan, p. 28-45 ] \ue2\u20ac\u201c Models as Decision Support Tools: Explanation, Foresight, Prediction PDF version of the PowerPoint presentation by Richard J. Hatchett, p. 46-62]

Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models

This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the International Journal of Production Research (2018) © Taylor & Francis, available online at: http://doi.org/10.1080/00207543.2018.1447706[EN] Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC¿s inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified.This first author was partially supported by the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport [grant number FPU15/03595]; the partial support of Project 'Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector.' Ref. GV/2017/025, funded by the Generalitat Valenciana. The other authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research. 56(13):4418-4446. https://doi.org/10.1080/00207543.2018.1447706S44184446561

American graduate admissions: both sides of the table

This is a comprehensive study of graduate admission process in American universities. There are multiple entities involved in the process, out of which the most significant ones are: • The candidate applying for admission in a department in a school • The decision-makers acting upon the candidates' application The goal of this study is to understand the admission process from each of these entities' perspective, and provide them decision-support models for their respective tasks. Although both of the entities interact through a common set of datapoints, i.e. candidate admission application, each of them works towards a very different goal. The juxtaposition of these two tasks provides a very interesting challenge which is hard to resolve deterministically. Solution to such a problem requires learning techniques which can find patterns, adapt according to the dynamic nature of problem, and produce results in a probabilistic fashion. We study and model the graduate admission process from a machine learning perspective based on analysis of large amounts of data. The analysis considers factors such as standardized test scores, and GPA, as well as world knowledge such as university similarity, reputation, and constraints. Based on the targeted entity, learning problem is formulated as classification problem or ranking problem. During learning and inference, not only those features are considered which are available from the data directly, but also the hidden features which need to be incorporated generatively. Our experimental study reveals some key factors in the decision process and, consequently, allows us to propose a recommendation algorithm that provides applicants the ability to make an informed decision regarding where to apply, as well as guides the decision-makers towards a more efficient process

A Statistical Comparative Study of Different Similarity Measures of Consensus in Group Decision Making

Research conducted in collaboration between DMU and University of Granada (Spain). DIGITS, Department of Informatics, Faculty of Technology, De Montfort University, Leicester LE1 9BH, UK; Department of Quantitative Methods in Economic and Business, University of Granada, 18071 Granada, Spain; Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain; Department of Computer Science and A.I., University of Granada, 18071 Granada, SpainNOTICE: this is the author’s version of a work that was accepted for publication in . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences. http://dx.doi.org/10.1016/j.ins.2012.09.014An essential aim in group decision making (GDM) problems is to achieve a high level of consensus among experts. Consensus is defined as general or widespread agreement, and it is usually modelled mathematically via a similarity function measuring how close experts’ opinions or preferences are. Similarity functions are defined based on the use of a metric describing the distance between experts’ opinions or preferences. In the literature, different metrics or distance functions have been proposed to implement in consensus models, but no study has been carried out to analyse the influence the use of different distance functions can have in the GDM process. This paper presents a comparative study of the effect of the application of some different distance functions for measuring consensus in GDM. By using the nonparametric Wilcoxon matched-pairs signed-ranks test, it is concluded that different distance functions can produce significantly different results. Moreover, it is also shown that their application also has a significant effect on the speed of achieving consensus. Finally, these results are analysed and used to derive decision support rules, based on a convergent criterion, that can be used to control the convergence speed of the consensus process using the compared distance functions.The authors would like to acknowledge FEDER financial support from the Project FUZZYLING-II Project TIN2010-17876; the financial support from the Andalusian Excellence Projects TIC-05299 and TIC-05991, and also from the research Project MTM2009-08886. Prof. Francisco Chiclana would like to acknowledge the financial support from the University of Granada 2012 GENIL Strengthening through Short-Visits research program (Ref. GENIL-SSV)