Implementasi K-Nearest Neighbor pada Decission Support System Pemilihan Satuan Pengamanan Event Perguruan Tinggi

2020 ◽  
Vol 5 (1) ◽  
pp. 67
Author(s):  
Aries Setiawan ◽  
Budi Widjajanto ◽  
Achmad Wahid Kurniawan ◽  
Setyo Budi
Keyword(s):  
Support System ◽  
Nearest Neighbor ◽  
Selection Process ◽  
College Level ◽  
K Nearest Neighbor ◽  
Tertiary Institutions ◽  
Security Guards ◽  
Approval Rate ◽  
Level Of Importance ◽  
Selection Of

Routine events required by tertiary institutions require escort from selected security guards. Elections based on personal subjectivity will lead to results that are not in accordance with the purpose of the security itself. However, if the selection is based on the objectives will give results that are in accordance with professionalism. Each security unit has a different level of importance, so that at the level of security the event needs a level of professionalism in accordance with the level of importance at the college level. In detail the selection of security units on several criteria, namely event, years of service, cooperation, service, personality, skills and responsibilities. The method used in this selection process is the K-Nearest Neighbor, with the final result approval rate of  0.88%

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

scholarly journals Sistem Pendukung Keputusan Pemilihan Perumahan Dengan Menggunakan Metode Multi Attribute Utility Theory

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Muhammad L O Mardin ◽  
Achamad Fuad ◽  
Hairil K Sirajuddin
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Utility Theory ◽  
Selection Process ◽  
Housing Prices ◽  
Multi Attribute Utility Theory ◽  
Selection Decision ◽  
The City ◽  
Selection Of

Abstrak: Banyaknya pilihan rumah seringkali membuat calon pembeli merasa ragu atau kesulitan saat harus menentukan langsung rumah yang mana yang akan dibeli, karena pada pemilihan perumahan yang akan dibeli belum ada sistem yang akan membantu dalam memilih perumahan yang dibeli, sehingga pada proses pemilihan masih menggunakan pikiran saja dan belum ada perhitungan pada saat pemilihan perumahan yang akan di beli. Tujuan penelitian ini menghasilkan sebuah sistem pendukung keputusan pemilihan perumahan. Kriteria yang diajukan dalam proses pemilihan perumahan yaitu: Harga perumahan, Jarak dari pusat kota, Jarak dengan pasar terdekat, [1], tipe perumahan, jarak dengan jalan umum, jarak dengan lahar. Dari hasil pemilihan perumahan menggunakan sistem yang telah dibuat. dengan 10 alternatif, dengan tingkat kepentingan masing-masing kriteria yang digunakan yaitu: harga = 5, tipe rumah = 5, jarak dengan pusat kota = 2, jarak dengan pasar terdekat = 2, jarak dengan jalan umum = 4, jarak perumahan dengan lahar = 5, telah diperoleh alternatif yang akan direkomendasikan yaitu perumahan safira residen 70 dengan dengan nilai tertinggi 0,65.Kata kunci: Sistem Pendukung Keputusan, Pemilihan, Perumahan, Multi Attribute Utility TheoryAbstract: A large number of choices of houses often makes prospective buyers feel doubtful or difficult when they have to determine directly which house to buy because, in the selection of housing to be purchased, no system will assist in choosing the housing to be purchased so that in the selection process, you still use your mind. There is no calculation at the time of the selection of housing to be purchased. The purpose of this research is to produce a housing selection decision support system. The criteria proposed in the housing selection process are housing prices, distance from the city, distance to the nearest market, [1], type of housing, distance to public roads, distance to lava. From the results of the election using the system that has been created. With ten alternatives, with their respective interests. The criteria used are: price =5, type of house = 5, distance to city center = 2, distance to the nearest market = 2, distance to public roads = 4 distance from housing to lava = 5, has obtained an alternative that will be recommended, namely the residential sapphire housing 70 with the highest value of 0.65Keywords: Housing, Selection, Decision Support System, Multi-Attribute Utility Theory.

scholarly journals Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

2020 ◽  
Vol 10 (7) ◽  
pp. 2525 ◽  
Author(s):  
Md Junayed Hasan ◽  
Jaeyoung Kim ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Nearest Neighbor ◽  
Selection Process ◽  
Health State ◽  
K Nearest Neighbor ◽  
Bag Of Features ◽  
State Classification ◽  
Spherical Tank ◽  
Art Works ◽  
Health State Classification

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

scholarly journals EVALUATION OF MUTUAL INFORMATION ESTIMATORS FOR TIME SERIES

2009 ◽  
Vol 19 (12) ◽  
pp. 4197-4215 ◽  
Author(s):  
ANGELIKI PAPANA ◽  
DIMITRIS KUGIUMTZIS
Keyword(s):  
Time Series ◽  
Nonlinear Systems ◽  
Mutual Information ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Series Length ◽  
K Nearest Neighbors ◽  
Data Adaptive ◽  
The Comparative Study ◽  
Selection Of

We study some of the most commonly used mutual information estimators, based on histograms of fixed or adaptive bin size, k-nearest neighbors and kernels and focus on optimal selection of their free parameters. We examine the consistency of the estimators (convergence to a stable value with the increase of time series length) and the degree of deviation among the estimators. The optimization of parameters is assessed by quantifying the deviation of the estimated mutual information from its true or asymptotic value as a function of the free parameter. Moreover, some commonly used criteria for parameter selection are evaluated for each estimator. The comparative study is based on Monte Carlo simulations on time series from several linear and nonlinear systems of different lengths and noise levels. The results show that the k-nearest neighbor is the most stable and less affected by the method-specific parameter. A data adaptive criterion for optimal binning is suggested for linear systems but it is found to be rather conservative for nonlinear systems. It turns out that the binning and kernel estimators give the least deviation in identifying the lag of the first minimum of mutual information from nonlinear systems, and are stable in the presence of noise.

scholarly journals COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

2021 ◽  
Vol 4 (1) ◽  
pp. 33-39
Author(s):  
Budi Pangestu ◽  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Prospective Students ◽  
Naïve Bayes Classifier ◽  
K Nearest Neighbors ◽  
Selection Of

Selection of majors by prospective students when registering at a school, especially a Vocational High School, is very vulnerable because prospective students usually choose a major not because of their individual wishes. And because of the increasing emergence of new schools in cities and districts in each province in Indonesia, especially in the province of Banten. Problems experienced by prospective students when choosing the wrong department or not because of their desire, so that it has an unsatisfactory value or value in each semester fluctuates, especially in their Productive Lessons or Competencies. To provide a solution, a departmental suitability system is needed that can provide recommendations for specialization or major suitability based on students' abilities through attributes that can later assist students in the suitability of majors. The process of classifying the suitability of majors in data mining uses the k-Nearest Neighbor and Naive Bayes Classifier methods by entering 16 (sixteen) criteria or attributes which can later provide an assessment of students through this test when determining the majors for themselves, and there is no interference from people. another when choosing a major later. Research that has been carried out successfully using the k-Nearest Neighbors method has a higher recall of 99%, 81% accuracy and 82% precision compared to the Naïve Bayes Classifier whose recall only yields 98% while the accuracy and precision is the same as the k- Nearest Neighbors.

scholarly journals Intelligent CBR system for automation of the search process for efficient methods for cleaning exhaust gases

2021 ◽  
pp. 44-50
Author(s):  
Liudmyla Bugaieva ◽  
Yurii Beznosyk
Keyword(s):  
Nitrogen Oxides ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Stream Data ◽  
Exhaust Gases ◽  
Intelligent Decision Making ◽  
Decision Making System ◽  
Selection Of

In this study, the objective is to develop an intelligent system for making decisions on the choice of methods for cleaning exhaust gases from sulfur and nitrogen oxides using the Case-Based Reasoning- (CBR). The task of automating the selection of effective methods for cleaning waste gases is urgent and meets the paradigm of sustainable development. A database on methods for cleaning exhaust gases from nitrogen and sulfur oxides was created. The potential use of intelligent inference on precedents from the database to select the most appropriate cleaning method for new emission stream data is considered. The work of the CBR method is represented as a life cycle, which has four main stages: Retrieving, Reusing, Revising and Retaining. The following characteristics of precedents were considered: degree of purification, initial concentration, temperature, presence of impurities, obtained product, material consumption, and energy consumption. All of these characteristics (in CBR attributes), except for the fourth and fifth, are given by numerical values with respective units of measurement and can be easily normalized. The presence of impurities and the product are categorical attributes with a certain set of values (classes). One of the main problems in CBR was solved: the problem of choosing the type of indexes. A set of all input characteristics of the precedent as indices is suggested to be used for the proposed decision support system (DSS) for methods of cleaning gas emissions. The first two phases of the CBR lifecycle use the k-nearest neighbor method to Retrieving and Reusing. The Euclidean metric is used to estimate the distances between precedents in the developed system. During the third and fourth phases of CBR, the intervention of the decision maker is provided. The process finishes with the adoption of the found solution and the possible storage of this solution in the base of use cases. An intelligent decision-making system has been developed for the selection of methods for cleaning exhaust gases from sulfur and nitrogen oxides based on the method of inference by precedents (CBR), which has been done for the first time for such tasks of chemical technology.

scholarly journals PENERAPAN SISTEM PENDUKUNG KEPUTUSAN UNTUK SELEKSI MAHASISWA BERPRESTASI MENGGUNAKAN METODE AHP

2012 ◽  
Vol 9 (2) ◽  
pp. 79
Author(s):  
Titis Handayani
Keyword(s):  
Decision Support ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
Data Selection ◽  
Main Function ◽  
College Level ◽  
The Right ◽  
The University ◽  
Hierarchy Process ◽  
Selection Of

<p>Decision support systems play a role in helping the university to take appropriate decisions. In this study has made a Decision Support System for Selection of Student Achievement which serves to help the university take the right decision by using the method of Analytical Hierarchy Process (AHP). The main function of this system is to process the data selection of outstanding students college level.</p>

FASICA Framework: Service Selection Using K-d Tree and Cache

2015 ◽  
Vol 25 (05) ◽  
pp. 807-828
Author(s):  
Aimrudee Jongtaveesataporn ◽  
Shingo Takada
Keyword(s):  
Nearest Neighbor ◽  
Service Oriented Architecture ◽  
Service Selection ◽  
K Nearest Neighbor ◽  
Search Results ◽  
Key Points ◽  
Service Oriented ◽  
Selection For ◽  
Selection Of

The selection of services is a key part of Service Oriented Architecture (SOA). Services are primarily selected based on function, but Quality of Service (QoS) is an important factor when choosing among several services with the same function. But current service selection approaches often takes time to unnecessarily recompute requests. Furthermore, if the same service is chosen as having the "best" QoS for multiple selections, this may result in that service having too much load. We thus propose the FASICA (FAst service selection for SImilar constraints with CAche) Framework which chooses a service with satisfactory QoS as quickly as possible. The key points are (1) to use a cache which stores previous search results, (2) to use K-Nearest Neighbor (K-NN) algorithm with K-d tree when a satisfactory service does not exist in the cache, and (3) to distribute the service request according to a distribution policy. Results of simulations show that our framework can rapidly select a service compared to a conventional approach.

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