scholarly journals Analisis Spasial Clustering Zona Potensi Ikan Konsumsi Air Tawar di Kabupaten Bogor

KREA-TIF ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 11
Author(s):  
Intan Damayanti ◽  
Erwin Hermawan ◽  
Nurul Kamilah
Keyword(s):  
Spatial Analysis ◽  
Freshwater Fish ◽  
Fish Consumption ◽  
Clustering Analysis ◽  
Potential Zone ◽  
Distribution Map ◽  
Clustering Method ◽  
Consumption Data ◽  
Fishery Data ◽  
Freshwater Consumption

<p><em>Data ikan konsumsi air tawar sangat diperlukan untuk mengetahui zona potensi ikan konsumsi air tawar, dengan tingkat potensi tinggi, sedang dan rendah pada 40 Kecamatan yang ada di Kabupaten Bogor, namun data yang diperoleh dari Dinas Perikanan dan Peternakan masih disajikan dalam buku yang menggambarkan peta dan data-data tentang perikanan. Serta untuk mengetahui produksi dari Tahun ke Tahun masih kurang optimal karena hanya disajikan dalam bentuk grafik statistik. Hal ini menyebabkan kesulitan dalam mengetahui zona ikan konsumsi air tawar, yang berpotensi memiliki tingkat potensi tinggi, sedang dan rendah, oleh karena itu diperlukanya </em>suatu peta sebaran<em>, untuk memudahkan dalam melihat peta sebaran zona potensi ikan konsumsi air tawar dengan potensi tinggi, sedang, dan rendah dari Tahun 2018-2019, kedalam bentuk peta menggunakan analisis k-means clustering</em> dengan bahasa pemrograman Rstudio<em>. Hasil penelitian analisis dengan k-means clustering menghasilkan 3 cluster dengan kategori tinggi, sedang dan rendah pada jenis ikan konsumsi air tawar. Hasil dari penelitian ini yaitu peta lokasi sebaran zona potensi ikan konsumsi air tawar di Kabupaten Bogor.</em></p><p> </p><p align="left"><strong>K</strong><strong>a</strong><strong>ta kunci: </strong><em>Analisis Spasial, Potensi Ikan Konsumsi, Metode Clustering, Zona potensi</em></p><p align="center"><strong><em>Abstract</em></strong></p><p><em>Freshwater fish consumption data is very necessary to determine the potential zones of freshwater consumption fish, with high, medium and low potential levels in 40 sub-districts in Bogor Regency, but data obtained from the Department of Fisheries and Livestock are still presented in books that describe maps and </em><em>fishery data</em><em>. And to find out the production from year to year is still not optimal because it is only presented in the form of statistical graphs. This causes difficulties in knowing the zones of freshwater consumption fish, which have the potential to have high, medium and low potential levels</em> <em>a distribution map is needed, to make it easier to see a map of the distribution of </em><em>potential</em><em>l freshwater fish consumption zones with high, medium, and low potential. low from 2018-2019, into map form using k-means clustering analysis</em><em> with Rstudio programming language</em><em>. The results of the analysis with k-means clustering resulted in 3 clusters with high medium and low categories for freshwater consumption fish The results of this study are a map of the distribution of potential zones for freshwater consumption fish in Bogor</em><em>.</em></p><p> </p><p><strong>Keywords</strong>: <em>Spatial Analysis, Potential Fish Consumption, Clustering Method, Potential Zone</em><em></em></p>

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

scholarly journals Surveying seafood consumption - a methodological analysis

2006 ◽  
Vol 24 (1) ◽  
pp. 12 ◽  
Author(s):  
Mecki Kronen ◽  
Brian McArdle ◽  
Pierre Labrosse
Keyword(s):  
Fish Consumption ◽  
Data Sets ◽  
Seafood Consumption ◽  
Fresh Fish ◽  
Individual Interviews ◽  
Consumption Data ◽  
Resource Requirements ◽  
Per Capita ◽  
Questionnaire Surveys ◽  
The Village

This paper addresses the need to establish a fast, effective and reliable method for collecting fish and seafood consumption data at the village level. Two different approaches, a community participatory and a classical survey one were tested and validated against each other. Using fully structured questionnaire surveys also reliability of results obtained from household and individual interviews were compared. Furthermore, taking fresh fish consumption as an example, three different methods were assessed to approximate best per capita consumption. Approaches and methods are validated in terms of time and human resource requirements, and data quality by comparing data sets obtained in Polynesian and Melanesian communities. Adding efficiency criteria, adoption of household average consumption surveys is concluded to best combine reliable data and least time and financial requirements. Per capita fresh fish consumption was found to best estimated using a simplified WHO system that takes into account gender-age correction factors.

A Comparative Analysis of the Direct Clustering Algorithms Based on Different Similarity Measure of Vague Sets

2013 ◽  
Vol 380-384 ◽  
pp. 1488-1494
Author(s):  
Wang Wei ◽  
Jin Yue Peng
Keyword(s):  
Similarity Measure ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Evaluation Criteria ◽  
Experimental Result ◽  
Clustering Method ◽  
Vague Sets ◽  
Intelligence System ◽  
System Clustering

In the research and development of intelligence system, clustering analysis is a very important problem. According to the new direct clustering algorithm using similarity measure of Vague sets as evaluation criteria presented by paper, the Vague direct clustering method are used to analysis using different similarity measure of Vague sets. The experimental result shows that the direct clustering method based on the similarity of Vague sets is effective, and the direct clustering method based on different similarity measure of Vague sets is the same basically, but difference on the steps of clustering. To select different algorithms according different conditions in the work of the actual applications.

Health Risks Associated with Freshwater Fish Consumption

2015 ◽  
Vol 5 (4) ◽  
pp. 275-280
Author(s):  
Angelika Linowska ◽  
Ewa Sobecka
Keyword(s):  
Freshwater Fish ◽  
Health Risks ◽  
Fish Consumption

K-Anonymity Algorithm Based on CLIQUE for Green Manufacturing

2013 ◽  
Vol 312 ◽  
pp. 714-718
Author(s):  
Zi Qi Zhao ◽  
Xiao Jun Ye ◽  
Chun Ping Li
Keyword(s):  
Data Processing ◽  
Processing Speed ◽  
Clustering Analysis ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Green Manufacturing ◽  
Multidimensional Data ◽  
Clustering Method ◽  
Analysis Algorithm ◽  
Clique Algorithm

Multidimensional clustering analysis algorithm is for a class of cell-based clustering method of processing speed quickly, time efficiency, mainly to CLIQUE representatives. With time efficient clustering algorithm CLIQUE algorithm can achieve multi-dimensional k - Anonymous the algorithm KLIQUE, KLIQUE algorithm based CLIQUE efficiently retained their CLIQUE algorithm time complexity of features, can play the CLIQUE multidimensional data for the large amount of data processing advantage.

scholarly journals Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020

2021 ◽  
Vol 22 (1) ◽  
pp. 1
Author(s):  
Febiyanti Alfiah ◽  
Almadayani Almadayani ◽  
Danial Al Farizi ◽  
Edy Widodo
Keyword(s):  
Clustering Analysis ◽  
The Other ◽  
Poor People ◽  
Clustering Method ◽  
Capita Expenditure ◽  
Poor Households ◽  
Per Capita ◽  
Poverty Indicators ◽  
Partition Method ◽  
Cluster 2

 Keberadaan pandemi COVID-19 di Indonesia, mengakibatkan kemiskinan di Indonesia semakin tinggi terutama di Jawa Timur yang menjadi satu diantara provinsi lain dengan kasus COVID-19 tinggi di Indonesia. Tujuan penelitian ini yaitu mengetahui pengelompokan kabupaten/kota di Jawa Timur yang mempunyai kesamaan karakteristik berdasarkan indikator kemiskinan tahun 2020. Penelitian ini menggunakan data yang didapatkan dari Badan Pusat Statistik. Metode yang digunakan ialah metode k-medoids clustering yang merupakan metode partisi clustering guna pengelompokan n objek ke dalam k cluster. Berdasarkan hasil penelitian, diperoleh pengelompokan karakteristik masing-masing cluster yang dibentuk berdasarkan nilai indikator kemiskinan di Jawa Timur tahun 2020 sebanyak 2 cluster. Dimana 30 kabupaten/kota pada cluster 1 dan dan 8 kabupaten/kota pada cluster 2. Cluster 1 memiliki karakteristik Persentase Rumah Tangga yang Mempunyai Sanitasi Layak, Angka Harapan Hidup, dan Persentase Angka Melek Huruf Umur 15-55 Th tinggi. Sedangkan cluster 2 memiliki karakteristik Persentase Rumah Tangga Miskin Penerima Raskin, Persentase Penduduk Miskin, dan Persentase Pengeluaran Perkapita untuk Makanan dengan Status Miskin tinggi. Kata kunci: Clustering; Jawa Timur; K-medoids; kemiskinan  K-Medoids Clustering Analysis Based on Poverty Indicators in East Java in 2020 ABSTRACT The existence of the pandemic COVID-19 in Indonesia has resulted in higher poverty in Indonesia, especially in East Java, which is one of the other provinces with high cases in Indonesia. The purpose of this study is to find out the grouping of regencies/cities in East Java that have similar characteristics based on the poverty indicators in 2020. This study uses data obtained from the Badan Pusat Statistik. The method used is k-medoids clustering method which is a clustering partition method for grouping n objects into k clusters. Based on the results of the study, it was found that the grouping of the characteristics of each cluster formed based on the value of the poverty indicator in East Java in 2020 was 2 clusters. Where 30 regencies/cities in cluster 1 and and 8 regencies/cities in cluster 2. Cluster 1 has the characteristics of the percentage of households that have proper sanitation, life expectancy, and a high percentage of literacy rates aged 15-55 years. While cluster 2 has the characteristics of the percentage of poor households receiving Raskin, the percentage of poor people, and the percentage of per capita expenditure on food with high poor status. Keywords: Clustering; East Java; K-Medoids; poverty

Sign in / Sign up

Export Citation Format

Share Document