Analysis of genes expression can be done with the investigation of a particular microarray data for the description of a gen. This is done to identify whatgenes that were active in the human body. Detection of gene mutation is an activity that can provide contribution in the medical field. Detection of mutated gene is needed to avoid the diseases caused by themsuch as cancer. The detection of gene mutations can be performed by utilizing computer-based system. Group Decision Support System (GDSS) is a computer-based system that can be utilized in detecting human gene mutations that cause disease. The ELECTRE method, which is a Multi-Attribute DecisionMaking, is a method in modeling multi-criteria GDSS. In this paper we propose implementation of model for multi-criteria GDSS in which the simulation data is the mutated genes that can causecancer

http://www.airccse.org/journal/jcsit/0211ijcsit04.pdf

ELECTRE Methods in Solving Group Decision Supports System Bioinformatics on Gene Mutation Detection simulation

Application of Group Decision Support System (GDSS) can assist for delivering the decision of various opinions (preference) cancer detection based on the preferences of various expertise. In this paper we propose ELECTRE-Entropy for GDSS Modeling. We propose entropy weighting for each criteria under ELECTRE Method.ELECTRE is one method in Multi-Attribute Decision Making (MADM). Modeling of Group Decision Support Sytemapplyfor multi-criteria which the simulation data mutated genes that can cause cancer and solution recommended.

/pdf/electre-entropy-method-in-group-decision-support-system-41ax2bbu28.pdf

ELECTRE-Entropy method in Group Decision Support System Modelto Gene Mutation Detection

The increasing number of tourist destination becomes the main factor for export earning, job vacancy, business development, and infrastructure. The problem that occurs is the difference in regional income (GDP) that is quite significant in each region. Thus, it is necessary for the government to make a decision or policy in increasing tourist visits, mainly in Bali. In this case, choosing the most efficient decision from a number of decisions is for the government, tourists, community leaders, academics, and entrepreneurs in the tourism sector, especially in Bali. It is important to have a modeling decision support group (GDSS). GDSS modeling by integrating a knowledge-based (KB) risk analysis can determine decisions, extract information, and identify problems in the tourism sector especially, tourism objects in each region, more specifically. Problem identification in risk analysis modeling is determining decisions in handling risks and finding solutions from alternative tourism decisions that are potentially enlarged and knowledge gained from each decision-maker (DM). The process of identifying knowledge starts with comparing the assessment criteria on each tourism object and knowledge of tourism decision-makers. The results of GDSS modeling are subsequently integrated into knowledge-based risk analysis so that a decision is obtained in the form of an impact or risk and solution or recommendation in developing the specified tourism object. The purpose of combining the result is to understand the impacts or risks that may arise, and recommendations recommended so that the impacts or risks can be avoided.

/pdf/modeling-of-a-tourism-group-decision-support-system-using-athwroduf6.pdf

Modeling of a Tourism Group Decision Support System using Risk Analysis based Knowledge Base

as ongoing activity needs certain planning in order to be zero in on and has clear direction. Therefore, it is needed a model for the analysis medium in determining regional priority to development implementation in the future. This research applied multi attribute decision making (MADM) concept for analyzing data of GRDP of Papua Province. Two methods of MADM have been used in this research, those are wighted product (WP) and Technique of Order Preference by Similarity to Ideal Solution (TOPSIS). Twenty nine regencies were analyzed based on the data of GRDP to observe which region will be the development priority later. Result of the research by comparing the two methods are obtained six regencies which need special attention, those are Central Mamberamo Regency, Yalimo, Nduga, Intan Jaya, Mamberamo Raya and Deiyai. The research is expected can be the supporting material of decision in development planning field for the policy implementer, so that able to determine regional priority for development activities in the future. KeywordsPlan, MADM, GRDP, Weighted Product, TOPSIS.

/pdf/multi-attribute-decision-making-method-for-determination-of-43ziq5soqs.pdf

Multi Attribute Decision Making Method for Determination of Regional Development Plan Priority in Papua Province based on the Indicator of Gross Regional Domestic Product (GRDP)

Mobile and wireless networks play a vital role in our current trend. Networks have their own frequency levels to provide signals for commu- nication between their mobile nodes. Each and every network have their own coverage area which is called a cell. When a node moves from one cell to another cell within the same network, a handoff occurs. If a node moves out of its own network, and attempt to connect to another network there comes vertical handoff. The handoff should be in a seamless manner. For handoff decision making we use Multiple Attribute Decision Making(MADM) method to select the best network. Selection will be based on the criteria of the given networks. In this paper we have used three different MADM methods such as Technique for Order Preference by Similarity to Ideal Solution(TOPSIS), Elimination and Choice translating Reality(ELECTRE) and Grey Relational Analysis(GRA) for handoff decision. Wifi, Wimax and UMTS are the networks applied for handoff selection. Different parameters such as Bandwidth, Delay and Cost of the network has been used for analyzation.

/pdf/handoff-between-heterogeneous-networks-based-on-madm-methods-4sd323ycko.pdf

Handoff between heterogeneous networks based on MADM methods

Detection of gene mutation is an activity that can provide contribution in the medical field. Detection of mutated gene is needed to avoid the diseases caused by them such as cancer. The detection of gene mutations can be performed by utilizing computer-based system. Group Decision Support System (GDSS) is a computer-based system that can be utilized in detecting human gene mutations that cause disease. The ELECTRE method, which is a Multi-Attribute Decision Making, is a method in modeling multicriteria GDSS. In this paper we propose implementation of model for multi-criteria GDSS in which the simulation data is the mutated genes that can cause cancer.

/pdf/implementation-of-madm-methods-in-solving-group-decision-2lv25ot2ze.pdf

Implementation of MADM Methods in Solving Group Decision Support System on Gene Mutations Detection Simulation

Bioinformatika merupakan hasil perkembangan ilmu di bidang kedokteran dan ilmu komputer. Teknologi di bidang komputer telah banyak mambantu dalam bidang kesehatan dan kedokteran. Medical imaging sebagai salah satu domain dalam bidang komputer telah dikembangkan untuk membantu dalam menemukan informasi tentang gen dalam tubuh manusia. Informasi didapat dari penyidikan terhadap struktur gen melalui analisis dari ekspresi gen. Hasil analisis ekspresi gen ini dibutuhkan untuk mendeteksi penyakit melalui identifikasi gen yang tidak normal. Hal ini di butuhkan dalam kehidupan manusia agar struktur fungsi biologis yang tidak normal tersebut tidak mengganggu atau mengancam proses hidup normal manusia.

Ermatita Ermatita

Papers

Implementation of MADM Methods in Solving Group Decision Support System on Gene Mutations Detection Simulation

Medical Imaging Untuk Analisis Ekspresi Gen Dalam Deteksi Penyakit