scholarly journals Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province

2020 ◽  
Vol 1 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Nabila Amalia Khairani ◽  
Edi Sutoyo
Keyword(s):  
Data Mining ◽  
Forest Fires ◽  
Clustering Algorithm ◽  
Social Aspects ◽  
Mining Method ◽  
Clustering Method ◽  
West Kalimantan ◽  
A Value ◽  
The Impact

Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries. One of the regions that has the highest fire hotspots is West Kalimantan Province. Forest and land fires have an impact on health, especially on the communities around the scene, as well as on the economic and social aspects. This must be overcome, one of them is by knowing the location of the area of ??fire and can analyze the causes of forest and land fires. With the impact caused by forest and land fires, the purpose of this study is to apply the clustering method using the k-means algorithm to be able to determine the hotspot prone areas in West Kalimantan Province. And evaluate the results of the cluster that has been obtained from the clustering method using the k-means algorithm. Data mining is a suitable method to be able to find out information on hotspot areas. The data mining method used is clustering because this method can process hotspot data into information that can inform areas prone to hotspots. This clustering uses k-means algorithm which is grouping data based on similar characteristics. The hotspots data obtained are grouped into 3 clusters with the results obtained for cluster 0 as many as 284 hotspots including hazardous areas, 215 hotspots including non-prone areas and 129 points that belong to very vulnerable areas. Then the clustering results were evaluated using the Davies-Bouldin Index (DBI) method with a value of 3.112 which indicates that the clustering results of 3 clusters were not optimal.

scholarly journals Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province

2020 ◽  
Vol 1 (1) ◽  
pp. 9-16
Author(s):  
Nabila Amalia Khairani ◽  
Edi Sutoyo
Keyword(s):  
Data Mining ◽  
Forest Fires ◽  
Clustering Algorithm ◽  
Social Aspects ◽  
Mining Method ◽  
Clustering Method ◽  
West Kalimantan ◽  
A Value ◽  
The Impact

Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries. One of the regions that has the highest fire hotspots is West Kalimantan Province. Forest and land fires have an impact on health, especially on the communities around the scene, as well as on the economic and social aspects. This must be overcome, one of them is by knowing the location of the area of ??fire and can analyze the causes of forest and land fires. With the impact caused by forest and land fires, the purpose of this study is to apply the clustering method using the k-means algorithm to be able to determine the hotspot prone areas in West Kalimantan Province. And evaluate the results of the cluster that has been obtained from the clustering method using the k-means algorithm. Data mining is a suitable method to be able to find out information on hotspot areas. The data mining method used is clustering because this method can process hotspot data into information that can inform areas prone to hotspots. This clustering uses k-means algorithm which is grouping data based on similar characteristics. The hotspots data obtained are grouped into 3 clusters with the results obtained for cluster 0 as many as 284 hotspots including hazardous areas, 215 hotspots including non-prone areas and 129 points that belong to very vulnerable areas. Then the clustering results were evaluated using the Davies-Bouldin Index (DBI) method with a value of 3.112 which indicates that the clustering results of 3 clusters were not optimal.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

scholarly journals Determination of the Value of Intangible Assets in the Companies of Lithuania

2017 ◽  
Vol 14 (2) ◽  
pp. 55-68 ◽  
Author(s):  
Rita Bužinskienė
Keyword(s):  
Financial Management ◽  
Intangible Assets ◽  
Fair Value ◽  
Financial Information ◽  
Financial Accounting ◽  
True Value ◽  
A Value ◽  
The Impact

AbstractIn accordance with generally accepted accounting standards, most intangibles are not accounted for and not reflected in the traditional financial accounting. For this reason, most companies account intangible assets (IAs) as expenses. In the research, 57 sub-elements of IAs were applied, which are grouped into eight main elements of IAs. The classification of IAs consists in two parts of assets: accounting and non-accounting. This classification can be successfully applied in different branches of enterprises, to expand and supplement the theoretical and practical concepts of the company's financial management. The article proposes to evaluate not only the value of financial information for IAs (accounted) but also the value of non-financial information for IAs (non-accounted), thus revealing the true value of IAs that is available to the companies of Lithuania. It names a value of general IAs. The results of the research confirmed the IA valuation methodology, which allows companies to calculate the fair value of an IA. The obtained extended IAs valuation information may be valuable to both the owners of the company and investors, as this value plays an important practical role in assessing the impact of IAs on the market value of companies.

scholarly journals Clustering Penerima Beasiswa Yayasan Untuk Mahasiswa Menggunakan Metode K-Means

2021 ◽  
Vol 5 (1) ◽  
pp. 258
Author(s):  
Bernadus Gunawan Sudarsono ◽  
Sri Poedji Lestari
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Scholarship Recipients

Grouping of scholarship recipients Scholarship assistance will be made based on the accumulated value using clustering where the scholarship recipients will be given scholarships with different amounts and sizes, because scholarships from foundations are limited and have levels of distribution. The division of groups to students who receive scholarships from foundations uses the clustering method of data mining where the function of clustering is a cluster or the task of grouping something is using the clustering algorithm approach, namely the K-means algorithm. The results of this clustering show that students based on their groups are divided into four groups based on the number of criteria, the results of the grouping show the number and decision of the foundation on granting foundation scholarships to students.

scholarly journals Evaluation of Land Subsidence Impact at Lowland Areas Using Analitycal Hierarchy Process (AHP) Method

2018 ◽  
Vol 68 ◽  
pp. 04017 ◽  
Author(s):  
Andriani ◽  
Eddy Ibrahim ◽  
Dinar Dwi Anugerah Putranto ◽  
Azhar Kholiq Affandi
Keyword(s):  
Land Subsidence ◽  
Social Aspects ◽  
Ahp Method ◽  
A Value ◽  
The People ◽  
Indirect Impact ◽  
Tidal Area ◽  
The Cost ◽  
The Impact ◽  
Hierarchy Process

Land subsidence is a problem that often occurs in lowland areas. The impact of land subsidence causes losses in the economic, physical, ecological and social aspects. The impact of land subsidence could be felt directly and indirectly by the people, so an evaluation of the most frequent (dominant) impacts needs to be done. One method that could be use for assesment using AHP, using pairwise comparisons can be obtained the most frequent (dominant) land subsidence impact. From the results of the study indicate that the direct impact due to land subsidence (weight 0.608)) is more dominant than the indirect impact (0.392). Based on the value of each parameter, three dominant land subsidence impacts are infrastructure damage with a value of 0.387, an increase in the cost of infrastructure construction and maintenance with a value of 0.193 and a flood of 0.129. The results of observations and ground checking at the Tanjung Api-Api area, there was damage to several floors of residents' homes, damage to road and tilt of trees which were damaged in the economic field was the most dominant impact. While floods and seawater intrusion are not dominant in this area because the area is located in the tidal area.

The Research of the Distributed Resource-AwareK-means Clustering Algorithm

2015 ◽  
Vol 19 (3) ◽  
pp. 343-348 ◽  
Author(s):  
Xiaoni Wang ◽  
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Single Point ◽  
Data Communication ◽  
The Internet ◽  
Mining Method ◽  
Time Data ◽  
Network Bandwidth ◽  
Data Mining Method ◽  
Battery Energy

According to the characteristics of the constrained resource in distributed real-time data mining in the Internet of Things (IOT) environment, a distributed data mining method is researched in such environment. Based on the limits of computing ability, storage ability, battery energy resources, network bandwidth, and the Internet single point failure, the distributed network data mining method is researched, and the adaptive technology and peer-to-peer node method are adopted. The DRA-Kmeans algorithm of data mining based on theK-means algorithm is proposed, and the amount of data communication among the sites to reduce the number of iterations and clustering is reduced. Clustering efficiency is improved, and better clustering results and execution efficiency are achieved.

scholarly journals Exemplars can Reciprocate Principal Components

2021 ◽  
Vol 20 ◽  
pp. 30-38
Author(s):  
Kieran Greer
Keyword(s):  
Principal Components ◽  
Forest Fires ◽  
Clustering Algorithm ◽  
Family Type ◽  
Clustering Method ◽  
Tree Structures ◽  
Category Type

This paper presents a clustering algorithm that is an extension of the Category Trees algorithm. Category Trees is a clustering method that creates tree structures that branch on category type and not feature. The development in this paper is to consider a secondary order of clustering that is not the category to which the data row belongs, but the tree, representing a single classifier, that it is eventually clustered with. Each tree branches to store subsets of other categories, but the rows in those subsets may also be related. This paper is therefore concerned with looking at that second level of clustering between the category subsets, to try to determine if there is any consistency over it. It is argued that Principal Components may be a related and reciprocal type of structure, and there is an even bigger question about the relation between exemplars and principal components, in general. The theory is demonstrated using the Portugal Forest Fires dataset as a case study. The Category Trees are then combined with other Self-Organising algorithms from the author and it is suggested that they all belong to the same family type, which is an Entropy-style of classifier. Some analysis of classifier types is also presented.

scholarly journals DATA MINING DALAM PENGELOMPOKAN JENIS DAN JUMLAH PEMBAGIAN ZAKAT DENGAN MENGGUNAKAN METODE CLUSTERING K-MEANS (STUDI KASUS: BADAN AMIL ZAKAT KOTA BENGKULU)

2018 ◽  
Vol 1 (2) ◽  
pp. 211
Author(s):  
Prahasti Prahasti
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Clustering Algorithm ◽  
Test Results ◽  
Clustering Method ◽  
Center Point

Abstrack - This research applies data mining by grouping the types and recipients of zakat. The application is done by the k-means clustering algorithm where the data to be entered is grouped by education and type of work in the distribution of zakat. Then a cluster is formed using the centroid value to determine the closest center point of distance between data. In the k-means clustering algorithm data processing is stopped in the iteration count of the data has not changed (fixed data) from the data that has been grouped. The test is done by using the RapidMiner software experiment conducted by the k-means clustering method which consists of input units, data processing units and output units, k-means clustering grouping data 1-2-1-1, 1-2-1-2 and 3-4-3-4. The results obtained from these tests are grouping the distribution of zakat with each cluster not the same. The test results are displayed in slatter graph.  Keywords - Data Mining, K-Means Clusttering, Zakat

scholarly journals Dengue Disease Detection using K- Means, Hierarchical, Kohonen- SOM Clustering

2019 ◽  
Vol 8 (10) ◽  
pp. 904-907
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Research Work ◽  
Data Mining Algorithm ◽  
Clustering Method ◽  
Dengue Disease ◽  
Related Data ◽  
Som Algorithm ◽  
Som Clustering ◽  
Kohonen Som

Data Mining is the process of extracting useful information. Data Mining is about finding new information from pre-existing databases. It is the procedure of mining facts from data and deals with the kind of patterns that can be mined. Therefore, this proposed work is to detect and categorize the illness of people who are affected by Dengue through Data Mining techniques mainly as the Clustering method. Clustering is the method of finding related groups of data in a dataset and used to split the related data into a group of sub-classes. So, in this research work clustering method is used to categorize the age group of people those who are affected by mosquito-borne viral infection using K-Means and Hierarchical Clustering algorithm and Kohonen-SOM algorithm has been implemented in Tanagra tool. The scientists use the data mining algorithm for preventing and defending different diseases like Dengue disease. This paper helps to apply the algorithm for clustering of Dengue fever in Tanagra tool to detect the best results from those algorithms.

scholarly journals THE INVESTIGATION OF ONLINE CUSTOMER COMPLAINTS IN THE BANKING SECTOR BY TEXT MINING

2020 ◽  
Vol 8 (5) ◽  
pp. 3835-3866
Author(s):  
Gamze YILDIZ ERDURAN ◽  
Fatma LORCU
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Customer Service ◽  
Credit Card ◽  
Banking Sector ◽  
Retail Banking ◽  
Mining Method ◽  
Clustering Method ◽  
Customer Complaints ◽  
Data Mining Method

The goal of this study is to obtain new gains that would provide benefits to businesses from customer complaints that customers offer voluntarily and free of charge. In line with this purpose, in this study, 25,390 online customer complaints concerning banks operating in the retail banking sector in Turkey were analysed by data mining method. By using the clustering method in data mining analysis, complaints were grouped, familiar words, similar or the words used together of the complaints were identified. As a result of the analysis done, the most frequently mentioned banks among customer complaints and the subjects that customers complained about most were determined. It was revealed that the subjects that the bank customers complain about most within the relevant periods were “branch, credit card fee, cancellation, customer service, subscription fee”. Also, the result emerged that bank customers used the words “unfair” and “victimisation” when expressing their dissatisfaction.

Sign in / Sign up

Export Citation Format

Share Document