scholarly journals A Predictive Model-Based Decision Support System for Diabetes Patient Empowerment

2021 ◽  
Author(s):  
Dietmar Glachs ◽  
Tuncay Namli ◽  
Felix Strohmeier ◽  
Gustavo Rodríguez Suárez ◽  
Michel Sluis ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Support System ◽  
Patient Empowerment ◽  
Personal Data ◽  
Diabetes Patient ◽  
Treatment Goals ◽  
Management Support ◽  
Periodic Review

The main objective of POWER2DM is to develop and validate a personalized self-management support system (SMSS) for T1 and T2 diabetes patients that combines and integrates i) a decision support system (DSS) based on leading European predictive personalized models for diabetes interlinked with predictive computer models, ii) automated e-coaching functionalities based on Behavioral Change Theories, and iii) real-time Personal Data processing and interpretation. The SMSS offers a guided workflow based on treatment goals and activities where a periodic review evaluates the patients progress and provides detailed feedback on how to improve towards a healthier, diabetes appropriate lifestyle.

SISTEM PENUNJANG KEPUTUSAN PEMILIHAN SUPPLIER DI TIA PET SHOP DENGAN METODE (SAW)

JURTEKSI ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 111-116
Author(s):  
Maha Rani ◽  
Ricki Ardiansyah ◽  
Anatia Agusti ◽  
Deby Erdriani ◽  
Nikmatul Husna
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Decision Maker ◽  
Support System ◽  
Decision Makers ◽  
Average Price ◽  
Simple Additive Weighting

Abstract: The goal to be achieved in this research is a decision support system that can provide support to decision makers in determining the supplier to be selected. The decision support system made using the Simple Additive Weighting (SAW) method in processing the data. Based on the results of data processing and information obtained, the decision support system made was successful in giving preference and ranking of suppliers in accordance with the criteria given by the decision maker. In selecting suppliers at Tia Pet Shop, the criteria used are quality, average price, packaging and speed of delivery.            Keywords: Decision Support System; Simple Additive Weighting; Supplier;  Abstrak: Tujuan yang ingin dicapai dalam penelitian ini yaitu sebuah sistem penunjang keputusan yang dapat memberikan dukungan kepada pembuat keputusan dalam menentukan supplier yang akan dipilih. Sistem penunjang keputusan yang dibuat menggunakan metode Simple Additive Weighting (SAW) dalam melakukan pemprosesan datanya. Berdasarkan hasil pengolahan data dan informasi yang didapat sistem penunjang keputusan yang dibuat berhasil memberikan preferensi dan perangkingan supplier sesuai dengan kriteria yang diberikan oleh pembuat keputusan. Dalam pemilihan supplier di Tia Pet Shop kriteria yang digunakan yaitu kualitas, harga rata-rata, pengemasan dan kecepatan pengiriman.  Kata kunci: Simple Additive Weighting; Supplier; Sistem Penunjang Keputusan

Decision Support System for Thesis Session Pass Recommendation Using AHP (Analytic Hierarchy Process) Method

2021 ◽  
Vol 2 (1) ◽  
pp. 215-221
Author(s):  
V. H. Valentino ◽  
Heri Satria Setiawan ◽  
Aswin Saputra ◽  
Yuli Haryanto ◽  
Arman Syah Putra
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Support System ◽  
Scoring System ◽  
Quantitative Methods ◽  
Objective Assessment ◽  
Analytic Hierarchy ◽  
Analytic Hierarchy Process Method ◽  
Hierarchy Process

AbstractThe background this time is how the scoring system is still objective in the thesis trial system, therefore withthis decision support system, the objective assessment will become a definite scoring system, and will beable to help examiners provide the best advice to take. decisions to be taken during the student thesis trial.The method used in this research is to use quantitative methods, by conducting a librarian study and thencombined with data taken from student participants in the thesis examination, with the library studymethod, it will be possible to explore this research and data processing will also be maximized. Manysystems use the AHP algorithm method to make decisions that are difficult to make, using the AHP methodcan be taken into consideration in making decisions, because data processing using the AHP method willprovide the best advice for making an important decision. This research will produce a system proposal andhow the data is obtained, then how the data is processed to produce a system proposal, which is best formaking decisions about students who are currently passing their thesis exams or not, with the proposedsystem will greatly help the examiner take decisions that were previously objective.Keywords: Decision Support System, Thesis Session, Pass, AHP.

scholarly journals Penentuan Lokasi Fasilitas Kesehatan Strategis Menggunakan Metode Naive Bayes pada RSU Bintang

2021 ◽  
Vol 6 (2) ◽  
pp. 52
Author(s):  
Aniek Suryanti Kusuma ◽  
Welda Welda ◽  
I Komang Juliana
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Health Facility ◽  
Support System ◽  
Naive Bayes ◽  
Current System ◽  
Naïve Bayes ◽  
Health Facilities ◽  
The Right

At present the selection of strategic health facility locations is not easy, to determine the right location and in accordance with the needs of patients must use the right calculation. Bintang General Hospital (RSU Bintang) has difficulties in determining the strategic location of new health facilities. The difficulty is due to the absence of data processing from the current system so that in determining the location of strategic health facilities is not based on data that has been analyzed. Based on the problems experienced by RSU Bintang and to assist in making a decision in establishing a strategic health facility location, a study was made to design a decision support system that can perform calculations to determine the location of the most strategic health facility with the title "Decision Support System. Determining the Location of Strategic Health Facilities Using the Naive Bayes Method at RSU Bintang”. Decision support system that is built will have several functions, such as processing patient register data, user data processing, alternative location data processing, criteria data processing, data processing rules, Naive Bayes calculations and managing several reports that can be used as decision support for the RSU Bintang. in determining the location of the most strategic health facilities. In this system, testing has been done by using blackbox testing which gets the test results in accordance with the system design.

A Proactive Decision Support System for Online Event Streams

2018 ◽  
Vol 17 (06) ◽  
pp. 1891-1913 ◽  
Author(s):  
Yongheng Wang ◽  
Xiaozan Zhang ◽  
Zengwang Wang
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Bayesian Network ◽  
Support System ◽  
Sequential Decision ◽  
Big Data Processing ◽  
Future System ◽  
System States

In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision making model. A Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well for congestion control in transportation system.

Advances in Data Processing for Airlines Revenue Management

2012 ◽  
pp. 1952-1965
Author(s):  
Félix Mora-Camino ◽  
Luiz Gustavo Zelaya Cruz
Keyword(s):  
Dynamic Programming ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Revenue Management ◽  
Support System ◽  
Control Process ◽  
Optimization Approach ◽  
Geometric Problem ◽  
On Line

In this communication advances in data processing techniques applied to Airlines Revenue Management are displayed. The general introduction presents a brief review of Airlines Revenue Management. The first of the paper introduces the problem of updating the probability distributions of demand for reservations. This updating process, facing the stochastic nature of demand for travel, is a cornerstone for the design of an efficient on-line decision support system to control the reservation process for a flight by an airline. The considered problem is formulated as a dual geometric problem to which an unconstrained non-convex, primal geometric problem is associated. A genetic algorithm optimization approach is proposed to solve the primal geometric problem, and then the classical geometric primal-dual transformations provide the solution to the initial problem. Then, the second part of the paper considers the design of a new Decision Support System for improving the reservation control process of airlines. A new recursive Dynamic Programming model for maximum expected revenue evaluation is defined, which, contrarily to other approaches, takes explicitly into account daily booking request arrivals. A practical Backward Dynamic Programming algorithm is established, leading to the design of an on-line optimisation module for Revenue Management. In this study two cases are considered. The first one considers that fare classes are not physically confined and the obtained results are extended in the second case to cover the situations where confinement of fare classes (Business Class and Economy Class) is applied.

Advances in Data Processing for Airlines Revenue Management

2010 ◽  
pp. 132-145
Author(s):  
Félix Mora-Camino ◽  
Luiz Gustavo Zelaya Cruz
Keyword(s):  
Dynamic Programming ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Revenue Management ◽  
Support System ◽  
Control Process ◽  
Optimization Approach ◽  
Geometric Problem ◽  
On Line

In this communication advances in data processing techniques applied to Airlines Revenue Management are displayed. The general introduction presents a brief review of Airlines Revenue Management. The first of the paper introduces the problem of updating the probability distributions of demand for reservations. This updating process, facing the stochastic nature of demand for travel, is a cornerstone for the design of an efficient on-line decision support system to control the reservation process for a flight by an airline. The considered problem is formulated as a dual geometric problem to which an unconstrained nonconvex, primal geometric problem is associated. A genetic algorithm optimization approach is proposed to solve the primal geometric problem, and then the classical geometric primal-dual transformations provide the solution to the initial problem. Then, the second part of the paper considers the design of a new Decision Support System for improving the reservation control process of airlines. A new recursive Dynamic Programming model for maximum expected revenue evaluation is defined, which, contrarily to other approaches, takes explicitly into account daily booking request arrivals. A practical Backward Dynamic Programming algorithm is established, leading to the design of an on-line optimisation module for Revenue Management. In this study two cases are considered. The first one considers that fare classes are not physically confined and the obtained results are extended in the second case to cover the situations where confinement of fare classes (Business Class and Economy Class) is applied.

scholarly journals Decision Support System in a Memristor-Based Mobile CIM Architecture Applied on Big Data Computation and Storage

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Li ◽  
Zhao Deng
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Computer Architecture ◽  
Support System ◽  
Integrated Circuit ◽  
Data Communication ◽  
Oxide Semiconductor ◽  
And Storage

Data computation and storage are essential parts of developing big data applications. The memristor device technology could remove the speed and energy efficiency bottleneck in the existing data processing. The present experimental work investigates the decision support system in a new architecture, computation-in-memory (CIM) architecture, which can be utilized to store and process big data in the same physical location at a faster rate. The decision support system is used for data computation and storage, with the aims of helping memory units read, write, and erase data and supporting their decisions under big data communication ambiguities. Data communication is realized within the crossbar by the support of peripheral controller blocks. The feasibility of the CIM architecture, adaptive read, write, and erase methods, and memory accuracy were investigated. The integrated circuit emphasis (SPICE) simulation results show that the proposed CIM architecture has the potential of improving the computing efficiency, energy consumption, and performance area by at least two orders of magnitude. CIM architecture may be used to mitigate big data processing limits caused by the conventional computer architecture and complementary metal-oxide-semiconductor (CMOS) transistor process technologies.

scholarly journals Sistem Pendukung Keputusan Untuk Menentukan Kelayakan Pengiriman Peserta Magang Ke Jepang Pada Dinas Tenaga Kerja Kota Medan Dengan Metode Analytical Hierarchy Process (AHP)

2019 ◽  
Vol 3 (2) ◽  
pp. 22
Author(s):  
Ryki Perdana ◽  
Achmad Yani
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
Process Data ◽  
Existing Problems ◽  
Efficient Alternative ◽  
Priority Order ◽  
Hierarchy Process

<p><em>Selection of apprentices to Japan is an activity that is often done by the Medan City Manpower Office to find apprentices to Japan who are eligible to be sent and placed there. Every agency or company in general has used a computerized application to process data easily and quickly. The reality on the ground is that the Medan City Manpower Office is not ready in the process of selecting apprentices to Japan. The system used is still manual, resulting in a lack of effectiveness in selecting apprentices to Japan. The use of computers is very necessary for data processing so as to produce an accurate and fast information. Computerized data processing is needed to obtain information that can be used to produce solutions to existing problems. To solve the problems above, we need a Decision Support System. Decision support system is a system that helps the performance of staff recruiting at the Medan City Manpower Office. For this reason a Decision Support System to determine the appropriateness of apprentices to Japan. This study applies the Analytical Hierarchy Process (AHP) method which is one that can solve multi-criteria problems. The benefit of this research is to provide a more effective and efficient alternative to facilitate decision making in determining the appropriateness of apprentices to Japan. Inputs requested from the user are the assessment criteria and the results provided by the system are alternative apprenticeship data based on the highest priority order of alternative values.</em></p><p>Keywords: Decision Support System, AHP, Internship Participant Eligibility</p>

scholarly journals Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi di Sekolah Menengah Pertama dengan Metode VIKOR dan TOPSIS

2017 ◽  
Vol 3 (2) ◽  
pp. 113
Author(s):  
Rivanda Putra ◽  
Indah Werdiningsih ◽  
Ira Puspitasari
Keyword(s):  
Student Achievement ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Support System ◽  
Object Oriented ◽  
Second Phase ◽  
Ideal Solution ◽  
Topsis Method ◽  
Object Oriented Design

Abstrak— Penelitian ini bertujuan merancang dan membangun sistem pendukung keputusan untuk pemilihan siswa berprestasi di SMP Taruna Jaya I Surabaya dengan metode VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) dan Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS). Sistem pendukung keputusan ini dibangun melalui 6 tahap. Tahap pertama adalah pengumpulan data dan informasi melalui wawancara dan analisis dokumen. Tahap kedua adalah pengolahan data dan informasi untuk mendapatkan rancangan sistem yang akan dibangun. Tahap ketiga adalah analisis sistem yang meliputi input data siswa, pembobotan kriteria dengan metode AHP, serta perankingan alternatif dengan metode VIKOR dan TOPSIS. Tahap keempat adalah perancangan sistem menggunakan konsep Object Oriented Design. Tahap kelima adalah implementasi sistem berbasis web. Tahap terakhir adalah evaluasi sistem dengan membandingkan tingkat akurasi antara metode VIKOR dan TOPSIS. Berdasarkan hasil uji konsistensi, terdapat 7 percobaan yang tidak konsisten dan 13 percobaan yang konsisten. Hasil yang diperoleh adalah tingkat akurasi yang tertinggi sebesar 80% dengan menggunakan TOPSIS. Berdasarkan hasil tersebut maka metode TOPSIS dapat digunakan pada kasus pemilihan siswa berprestasi di SMP Taruna Jaya I Surabaya dengan derajat kepentingan antar kriteria adalah nilai aktivitas sedikit lebih penting dari nilai rapot, nilai aktivitas lebih penting dari nilai prestasi, nilai aktivitas sangat kuat penting dari nilai sikap, nilai rapot sedikit lebih penting dari nilai prestasi, nilai rapot lebih penting dari nilai sikap, dan nilai prestasi sedikit lebih penting dari nilai sikap.Kata Kunci— AHP, Pemilihan Siswa Berprestasi, Sistem Pendukung Keputusan, TOPSIS, VIKORAbstract— This research proposes a solution to create a decision support system of student achievement selection using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method. The decision support system would resolve the problem of big data processing which needs more effort and more time. The development of decision support system of student achievement selection consisted of 6 phases. The first phase was collecting the data and information via interviews and document analysis. The second phase was data processing to create system design. The third phase was analyzing the system that includes the input of student data, weighing the criteria using AHP method, and rank the alternatives using VIKOR and TOPSIS method. The fourth phase was designing the system using Object Oriented Design. The fifth phase was implementing the system using a web-based. The sixth phase was the evaluation of system by comparing the level of accuracy between VIKOR and TOPSIS methods. Based on the result of consistency test, there were 7 inconsistent experiments and 13 consistent experiments. The result obtained is the highest accuracy rate of 80% by using TOPSIS. Based on these results, TOPSIS method can be used in case of selection of outstanding students in SMP Taruna Jaya I Surabaya with degree of importance among the criteria is activity value was slightly more important than report value, activity value was more important than achievement value, activity value was very important from attitude value, report value was slightly more important than achievement value, report value was more important than attitude value, and achievement value was slightly more important than attitude value.Keywords— AHP, Decision Support System, Student Achievement Selection , TOPSIS, VIKOR

Sign in / Sign up

Export Citation Format

Share Document