scholarly journals Data Mining Untuk Pemilihan Piranti Snorkeling Dengan Metode Fuzzy C-Means Clustering

S CIES ◽  
2016 ◽  
Vol 7 (1) ◽  
pp. 37-41
Author(s):  
Putu Wirayudi Aditama ◽  
I Putu Nata Susila ◽  
I Wayan Wijaya Kusuma
Keyword(s):  
Data Mining ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Snorkeling adalah salah satu hobu yang sangat digemari saat ini. Snorkeling sering di lakukan untuk refreshing sambil menyegarkan pikiran setelah berkutat dengan aktifitas dan pekerjaan yang menguras pikiran. Piranti atau (peralatan) snorkeling yang paling utama terdiri dari 4 alat yaitu, masker selam, baju selam, snorkel dan kaki katak atau sirip selam. Dalam penerapanya banyak kendala yang dihadapi para penyelam terutama dalam hal memilih piranti masih bersifat manual yaitu para penyelam hanya mendapat informasi dari instruktur snorkeling yang berada di kawasan snorkeling. Keadaan tersebut sudah pasti membuat proses pemilihan menjadi tidak efesien karena keterbatasan informasi yang bisa didapat sehingga perlunya dibuatkan sebuah sistem pendukung keputusan. Fuzzy C-means Clustering (FCM) adalah suatu teknik pengelompokan data yang mana keberadaan tiap-tiap titik dalam suatu cluster ditentukan oleh derajat keanggotaan. Pada penelitian ini akan digunakan metode perhitungan FCM untuk menentukan piranti yang tepat untuk direkomendasikan kepada User. Dengan adanya sistem pendukung keputusan dapat membantu user dalam memilih piranti yang sesuai dengan kriteria yang diinginkanya.

An Outlier Detection Method Based on Fuzzy C-Means Clustering

2009 ◽  
Vol 419-420 ◽  
pp. 165-168
Author(s):  
Qiang Li ◽  
Jian Pei Zhang ◽  
Guang Sheng Feng
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Data Mining Techniques ◽  
Fuzzy C Means ◽  
Second Stage ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Two Stages

Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.

scholarly journals A STUDY OF COMPARING CONCEPTUAL AND PERFORMANCE OF K-MEANS AND FUZZY C MEANS ALGORITHMS (CLUSTERING METHOD OF DATA MINING) OF CONSUMER SEGMENTATION

2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Yunita Sinambela ◽  
Sukrina Herman ◽  
Ahsani Takwim ◽  
Septian Rheno Widianto
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Life Time ◽  
Customer Segmentation ◽  
Customer Data ◽  
Fuzzy C Means ◽  
Consumer Segmentation ◽  
Fuzzy C Means Clustering ◽  
Role Based ◽  
And Performance

Consumers an important asset in a company that should be maintained properly especially potential customers. Tight competition requires companies to focus on the needs of the customer wants. Consumer segmentation is one of the processes carried out in the marketing strategy. To support the grouping process results consumers or consumer segmentation data mining is the support of a very important role. Based on mapping studies on data mining in support of consumer segmentation obtained two algorithms are often used for consumer segmentation include a K-Means Clustering and Fuzzy C-Means clustering. The attributes used for mining in customer segmentation processes are customer data, products, demographics, consumer behavior, transactions, RFMDC, RFM (Recency, Frequency Monetary) and LTV (Life Time Value). And it is important to combine the clustering algorithm to algorithm Classification, Association, and CPV to get the potential value of each cluster.

Mapping Student Performance With Employment Using Fuzzy C-Means

2020 ◽  
Vol 11 (4) ◽  
pp. 36-52
Author(s):  
Ishwank Singh ◽  
Sai Sabitha ◽  
Tanupriya Choudhury ◽  
Archit Aggarwal ◽  
Bhupesh Kumar Dewangan
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Student Performance ◽  
Performance Indicators ◽  
Educational Outcomes ◽  
Research Work ◽  
University Student ◽  
Fuzzy C Means ◽  
Research Activities ◽  
Fuzzy C Means Clustering

Technical organisations are ranked based on performance indicators like resources, students' intake, global reputation, and research activities. Student performance and placement are important factors in deciding the ranking of a university. Student performance analysis is a recent and widely researched domain aimed at reforming the education system. The analysis assists institutions to understand and improve their performance and educational outcomes. Admissions, academics, and placement are the three most significant processes during which the large amount of data is gathered within a university and there is a requirement of analysis. The data mining techniques are used for data analysis processes and it encompasses data understanding, pre-processing, modelling, and implementation. In this research work, fuzzy c-means clustering technique is used to understand fuzziness of student performance, classify and map the student performance to employability. To understand this objective, the dataset has been collected from universities, pre-processed, and analysed.

scholarly journals An Analysis of Students’ Learning Interest in Programming Language Based on Data Mining with Fuzzy C-Means Method

2021 ◽  
Vol 17 (09) ◽  
pp. 4
Author(s):  
Sitti Aisa ◽  
Asmah Akhriana ◽  
Ahyuna Ahyuna ◽  
Andi Irmayana ◽  
Irmawati Irmawati ◽  
...  
Keyword(s):  
Data Mining ◽  
Programming Language ◽  
Technical Information ◽  
Data Mining Technique ◽  
Java Programming Language ◽  
Fuzzy C Means ◽  
Java Programming ◽  
Fuzzy C Means Clustering ◽  
Data Collection Technique ◽  
Learning Interest

<p class="0abstract">Students' learning interest and motivation in programming language to date are determined by their scores and abilities to create applications. However, this is not sufficient to identify students' learning interest in programming language because some students got low scores. This study aims to identify students' learning interest in Dipanegara school of informatics management and computer (STMIK), Makassar, in Java programming language. The samples were 65 technical information students and 63 information system students. The data collection technique of this study was questionnaire and processed with data mining technique. Additionally, Fuzzy C-Means clustering method was applied on Java Netbeans programming language to classify the level of students' interest in studying Java programming language.   The result of the study was a web-based application that could determine students’ learning interests. It was obtained through questionnaires and resulted as follows: 47 students had high learning interest, 45 students had moderate interest, and 36 students had fair interest out of 128 samples.</p>

scholarly journals Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

2014 ◽  
Vol XL-2/W3 ◽  
pp. 19-23
Author(s):  
P. Akhavan ◽  
M. Karimi ◽  
P. Pahlavani
Keyword(s):  
Data Mining ◽  
Cutaneous Leishmaniasis ◽  
Fuzzy Inference System ◽  
Prevalence Rate ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

scholarly journals Application of Fuzzy C-Means Clustering Method Using Matlab To Map the Potential of Rice Plant In Bekasi Regency

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Winarni Suwarso
Keyword(s):  
Data Mining ◽  
Rice Plant ◽  
Food Crops ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Rice Yields ◽  
Clustering Data ◽  
Fuzzy C Means Algorithm

Abstract Based on the data of rice crops from BPS-Statistics of Bekasi Regency in the field of Food Crops, there are several sub-districts in Bekasi Regency with varying rice yields. Therefore, it is necessary to group the sub-districts with the highest potential of rice producers. Therefore, a method is needed to facilitate the classification of paddy producing districts. By Fuzzy C-Means clustering method, the division of rice-producing sub-districts can be done based on the area of rice harvest (Ha) and rice production (ton). In this research, clustering of potential sub-districts using the Fuzzy C-Means algorithm is aimed at facilitating the grouping of a sub-district with the largest and low rice yields. The result is an illustration that shows the subdistrict grouping based on the results of paddy farming. Keywords: Clustering, Data Mining, Fuzzy C-Means Algorithm

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