scholarly journals Optimalisasi Penyaluran Bantuan Pemerintah Untuk UMKM Menggunakan Metode Fuzzy C-Means

2021 ◽  
Vol 5 (3) ◽  
pp. 474-482
Author(s):  
Anggara Cahya Putra ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Economic Activity ◽  
Real Data ◽  
Membership Degree ◽  
Clustering Method ◽  
Micro Scale ◽  
Fuzzy C Means ◽  
Cash Assistance ◽  
Validation Test ◽  
A Value ◽  
Priority Levels

Indonesian MSMEs were very seriously affected by the Covid-19 pandemic, which caused the Indonesian economy has experienced deceleration. The Indonesian government has taken several steps to keep economic activity running, such as direct cash assistance for micro-scale businesses but is having problems in obtaining real data so that assistance is not on target, the clustering method using Fuzzy C-Means (FCM) is used for grouping MSME data. FCM allows the data to be a member of all clusters in which each cluster has a membership degree value of 0-1. The data used is from the website of the Sleman Regency Cooperatives and SME Service. FCM classifies MSME data based on the attributes of revenue, assets and number of workers. This research resulted in grouping MSME data into 3 priority levels for MSMEs in obtaining assistance, namely high priority, medium priority, and low priority. The results of this study show that the number of MSMEs with high priority is 23,023 MSMEs, medium priority is 9,774 MSMEs and low priority is 3,159 MSMEs. The validation test of the FCM method uses the Partition Coefficient Index (PCI) which has a value of 0.826 which means that value good because it is close to 1.  

scholarly journals Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Putri Elfa Mas`udia ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Cluster Center ◽  
Local Optimum ◽  
Membership Degree ◽  
Clustering Method ◽  
Final Grade ◽  
Fuzzy C Means ◽  
Group A ◽  
The Difference

AbstrakNilai akhir mahasiswa dapat ditentukan dengan berbagai cara, beberapa diantaranya menggunakan range nilai, standart deviasi, dll. Dalam penelitian ini akan ditawarkan sebuah metode baru untuk menentukan nilai akhir mahasiswa menggunakan clustering dalam hal ini adalah Fuzzy C-Means.Fuzzy C-Means digunakan untuk mengelompokkan sejumlah data dalam beberapa cluster. Tiap data memiliki derajat keanggotaan pada masing-masing cluster antara 0-1 yang diukur melalui fungsi objektif. Pada Fuzzy C-Means ini fungsi objektif diminimumkan menggunakan iterasi yang biasanya terjebak dalam optimum lokal. Algoritma genetika diharapkan dapat menangani masalah tersebut karena algoritma genetika berbasis evolusi yaitu dapat mencari individu terbaik melalui operasi genetika (seleksi, crossover, mutasi) dan dievaluasi berdasarkan nilai fitness. Penelitian ini bertujuan untuk mengoptimasi titik pusat cluster pada Fuzzy C-Means menggunakan algoritma genetika. Hasilnya, bahwa dengan menggunakan GFS didapatkan fungsi objektif yang lebih kecil daripada menggunakan FCM, walaupun membutuhkan waktu yang relative besar. Meskipun selisih antara FCM dan GFS tidak terlalu besar namun hal tersebut berpengaruh pada anggota cluster  Kata kunci— clustering, Fuzzy C-Means, algoritma genetika AbstractThe final grade of students could be determined in various ways, some of which use a range of values, deviation standard, etc. In this study will be offered a new method for determining final grades of students by using the clustering method. In this research the clustering method that will be used is the Fuzzy C-Means (FCM).Fuzzy C-Means is used to group a number of data in multiple clusters. Each data has a degree of membership (the range value of membership degree is 0-1). Membership degree is measured through the objective function. In Fuzzy C-Means,  objective function is minimized by using iteration and is usually trapped in a local optimum. Genetic algorithm is expected to handle these problems. The operation of genetic algorithm based on evolution that is able to find the best individuals through genetic operations (selection, crossover and mutation) and evaluated based on fitness values.This research aims to optimize the cluster center point of FCM by using genetic algorithms. The result of this research shows that by combining the Genetic Algorithm with FCM could obtained a smaller objective function than using FCM, although it takes longer in execution time. Although the difference of objective function that produced by FCM and FCM-Genetic Algorithm combination is not too big each other, but it takes effect on the cluster members. Keywords— clustering, fuzzy c-means, genetic algorithm

scholarly journals Penentu Jarak Tembak Berdasarkan Kerusakan Proyektil Timah Tanpa Jacket untuk Senjata Api Genggam Jenis Revolver S&W Kaliber .38 Spesial pada Target Tulang

2015 ◽  
Vol 17 (3) ◽  
pp. 135
Author(s):  
Sudibyo Sudibyo
Keyword(s):  
Target Location ◽  
Target Position ◽  
Real Data ◽  
Criminal Cases ◽  
Shape Coefficient ◽  
A Value ◽  
Technical Specifications ◽  
Table Data ◽  
Throw Distance ◽  
Test Firing

Abstract This study aims to predict the shooting range based on damage the type of lead a projectile without jacket caliber.38 special fired from handguns kinds brand Revolver S & W caliber .38 specials. Based on the phenomenon of criminal cases of abuse handguns types Revolver and the fact that real data it was found that 8% of the amount of lead projectiles without jacket as forensic evidence, the condition has broken the deformed moderate to severe.         The study was conducted at the Police Forensic Laboratory experimental method test-fired in the shooting box at short throw distance range of 0.5 to 6 meters , where the bone is positioned at the target position changes location every 0.5 meters, so the total number of shots is 12 times shot on 12 position target location, and finally obtained 12 variations of deformation projectile shot results.        Stages test firing conducted through three stages as follows: 1). Phase sample preparation equipment and materials firearms, bullets and target bone. 2). Phase shooting target accurately. 3). Stages of deformation measurements and weighing projectile, arranged in the form of table data.        Material samples of bullet used was the type of lead bullets without jacket caliber .38 special with technical specifications diameter of projectile 9.09 mm (real 9.05 mm), length of projectile 17.90 mm (real 18.61 mm), projectile material lead antimony, projectile weight of 10.25 grams, muzzle velocity (initial) 265 m / sec, rounded nose shape, coefficient of form C = 2, the ballistic coefficient i = 0,9 effective range or the distance accurately of 25 meters.        Material samples of bone were used as target is 1694 SR veal ribs with bone hardness values (87 ± 1.5) shore, is used for the calibration test firing, a human skull age adults (≥ 35 years) with a value of hardness (78 ± 6 ) shore, is used as the target subjects of research, human ribs (costal C-3 / C-6) adult (≥ 35 years) with a value of hardness (69 ± 19.5) shore, is used as the target subjects of research. Keywords : deformation; projectiles; bones

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Spreading Activation Connectivity Based Approach to Network Clustering

2018 ◽  
pp. 207-219
Author(s):  
Alexander Troussov ◽  
Sergey Maruev ◽  
Sergey Vinogradov ◽  
Mikhail Zhizhin
Keyword(s):  
Big Data ◽  
Social Network ◽  
Social Systems ◽  
Network Models ◽  
Real Data ◽  
Spreading Activation ◽  
Network Clustering ◽  
Clustering Method ◽  
Network Characteristics ◽  
Structure Detection

Techno-social systems generate data, which are rather different, than data, traditionally studied in social network analysis and other fields. In massive social networks agents simultaneously participate in several contexts, in different communities. Network models of many real data from techno-social systems reflect various dimensionalities and rationales of actor's actions and interactions. The data are inherently multidimensional, where “everything is deeply intertwingled”. The multidimensional nature of Big Data and the emergence of typical network characteristics in Big Data, makes it reasonable to address the challenges of structure detection in network models, including a) development of novel methods for local overlapping clustering with outliers, b) with near linear performance, c) preferably combined with the computation of the structural importance of nodes. In this chapter the spreading connectivity based clustering method is introduced. The viability of the approach and its advantages are demonstrated on the data from the largest European social network VK.

New fuzzy C-means clustering method based on feature-weight and cluster-weight learning

2019 ◽  
Vol 78 ◽  
pp. 324-345 ◽  
Author(s):  
Mahdi Hashemzadeh ◽  
Amin Golzari Oskouei ◽  
Nacer Farajzadeh
Keyword(s):  
Clustering Method ◽  
Fuzzy C Means ◽  
Feature Weight ◽  
Fuzzy C Means Clustering ◽  
Weight Learning

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

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