scholarly journals Cluster Analysis Menggunakan Algoritma Fuzzy C-means dan K-means Untuk Klasterisasi dan Pemetaan Lahan Pertanian di Minahasa Tenggara

2017 ◽  
Vol 8 (3) ◽  
Author(s):  
Jemaictry Tamaela ◽  
Eko Sediyono ◽  
Adi Setiawan
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Open Source ◽  
Visual Information ◽  
Agricultural Land ◽  
Agricultural Lands ◽  
Fuzzy C Means ◽  
Gis Data ◽  
Perform Cluster Analysis

Abstract. The purpose of this study is to perform cluster analysis and implementation by utilizing fuzzyc-means (FCM) and k-means (KM) to process agricultural data based on the data mining results. The fuzzy c-means (FCM) and k-means (KM) are implemented to find out and form the agricultural land clusters which appropriate the commodity types based on the supporting attributes that are used. The analysis and implementation results could provide some land information such as the number of the clusters, the land areas, the region areas, the locations and the productivity levels. The results of this study could be applied as the suggestion in converting the land functions and structuring the agricultural lands. The utilization of Openstreetmap is an open source solution which is implemented in the application. It could give visual information related to the agricultural land regions based on the clusters which make it easier to comprehend. Keywords: Cluster analysis, C-means, K-means, GIS, Data mining Abstrak. Penelitian ini bertujuan untuk melakukan analisis cluster dan implementasinya dengan menggunakan algoritma fuzzy c-means (FCM) dan k-means (KM) untuk mengelola data  pertanian dari hasil data mining yang dilakukan. Fuzzy c-means (FCM) dan k-means (KM) dimplementasikan untuk menemukan dan membentuk klaster-klaster daerah lahan pertanian sesuai dengan jenis komoditi berdasarkan atribut-atribut pendukung yang digunakan. Hasil analisis dan implementasi dapat menyediakan informasi lahan seperti jumlah kluster, luas lahan, luas daerah, letak dan tingkat produktifitas. Hasil yang diperoleh dapat menjadi bahan masukan dalam proses alih fungsi dan penataan lahan pertanian. Penggunaan Openstreetmap merupakan solusi open source yang diimplementasikan pada aplikasi dapat memberikan informasi  visual daerah-daerah lahan pertanian berdasarkan klaster yang dihasilkan sehingga lebih mudah untuk dipahami.Keywords: Cluster analysis, c-means, k-means, GIS, Data mining

Conflicts caused by afforestations of agricultural land in densely populated areas

1991 ◽  
Vol 56 ◽  
Author(s):  
N. Lust ◽  
B. Muys
Keyword(s):  
Nature Conservation ◽  
Agricultural Land ◽  
Agricultural Lands ◽  
Biological Value ◽  
Ecological Problems ◽  
The Subject ◽  
Points Of View

In  densely populated areas, such as Flanders, serious conflicts arise due to the  afforestation of agricultural lands. Roughly speaking, three sectors are  involved: agriculture, forestry and nature conservation. These groups do not  always share the same points of view. But even within the different sectors  there are several subgroups with their own interests and approaches.  Generally speaking, agriculture is strongly opposed, forestry has a  relatively diversified meaning, whereas nature conservation is either opposed  or in favour, depending on the region's biological value.     The policy in this matter is not forest friendly. Legislation strongly  hampers the afforestation of agricultural lands. Moreover the subject leads  to a series of ecological problems, which have not really been considered up  to now.

scholarly journals Educational data mining for students' performance based on fuzzy C-means clustering

2019 ◽  
Vol 2019 (11) ◽  
pp. 8245-8250
Author(s):  
Yu Li ◽  
Jin Gou ◽  
Zongwen Fan
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

A collection of open source applications for mass spectrometry data mining

PROTEOMICS ◽  
2014 ◽  
Vol 14 (20) ◽  
pp. 2275-2279 ◽  
Author(s):  
Óscar Gallardo ◽  
David Ovelleiro ◽  
Marina Gay ◽  
Montserrat Carrascal ◽  
Joaquin Abian
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Open Source ◽  
Mass Spectrometry Data

Denmark: experience of market turnover of agricultural land

Ekonomika APK ◽  
2021 ◽  
Vol 317 (3) ◽  
pp. 89-96
Author(s):  
Ihor Yurchenko
Keyword(s):  
Structural Model ◽  
Agricultural Land ◽  
Legal Framework ◽  
Empirical Method ◽  
Practical Significance ◽  
Agricultural Lands ◽  
Logical Scheme ◽  
Logical Method ◽  
Danish Experience ◽  
The Right

The purpose of the article is to reveal the experience of the functioning of the market circulation of agricultural land in Denmark, in order to further implement in Ukrainian practice, the positive and avoid negative aspects of this experience. Research methods. The study used an empirical method (comprehensive assessment of the modern model of market turnover of agricultural land in Denmark); generalization and systematization (construction of the concept and logical-structural model of economic turnover of lands); abstract-logical method (theoretical generalizations and formulation of conclusions). Research results. It was found that the tightly regulated market turnover of agricultural land in Denmark was changed to a more liberal one, with permission to buy land for foreigners, but this not only did not attract investment as expected, but on the contrary, led to even more negative and crisis phenomena in country. The main tools, mechanisms and conditions of land turnover in agriculture of this country are determined. Scientific novelty. The main purpose of regulating the market turnover of land in Danish agriculture has been established. The legal framework of Denmark for regulating the market circulation of agricultural land has been studied. The structural and logical scheme of market circulation of agricultural lands is formed. The provisions on the Ukrainian model of regulating the market turnover of agricultural lands were further developed, taking into account the experience of the studied country. Practical significance. The results of the study of the experience of the Kingdom of Denmark on the market turnover of land, in terms of granting non-residents access to the right to purchase agricultural land, is a clear practical answer and a caveat that should undoubtedly be taken into account in Ukraine. The application of the Danish experience should help to build an effective model of market turnover of agricultural land in our country. Tabl.: 1. Figs.: 1. Refs.: 18.

scholarly journals An Efficient Clustering Approach for Automatic Detection of Calcification in Low Dose Chest CT

2019 ◽  
pp. 163-168
Author(s):  
P. Tamijiselvy ◽  
N. Kavitha ◽  
K. M. Keerthana ◽  
D. Menakha
Keyword(s):  
Data Mining ◽  
Low Dose ◽  
Early Stage ◽  
Clustering Algorithms ◽  
Automatic Detection ◽  
Chest Ct ◽  
Data Mining Algorithm ◽  
Fuzzy C Means ◽  
Data Mining Algorithms ◽  
Using Data

The degree of aortic calcification has been appeared to be a risk pointer for vascular occasions including cardiovascular events. The created strategy is fully automated data mining algorithm to segment and measure calcification using Low-dose Chest CT in smokers of age 50 to 70 .The identification of subjects with increased cardiovascular risk can be detected by using data mining algorithms. This paper presents a method for automatic detection of coronary artery calcifications in low-dose chest CT scans using effective clustering algorithms with three phases as Pre-Processing, Segmentation and clustering. Fuzzy C Means algorithm provides accuracy of 80.23% demonstrate that Fuzzy C means detects the Cardio Vascular Disease at early stage.

scholarly journals Penerapan Algoritma Fuzzy C-Means Dipadukan Dengan Model Fuzzy Recency Frequency Monetary (RFM) Untuk Customer Relationship Management (CRM) (Studi Kasus Di TokoSweet Amirah Medan)

2019 ◽  
Vol 15 (1) ◽  
pp. 7
Author(s):  
Yohanni Syahra ◽  
Yusnidah Y ◽  
Beni Andika
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Fuzzy C Means

Konsumen merupakan aset yang sangat penting bagi perusahaan retail.Hal ini adalah alasan mengapa perusahaan retail harus merencanakan dan menggunakan strategi yang cukup jelas dalam memperlakukan konsumen.Dengan banyaknya jumlah konsumen yang dimiliki oleh suatu perusahaan retail, maka masalah yang dihadapi adalah bagaimana menentukan konsumen potensial.Dengan menerapkan konsep CRM (Customer Relationship Management), perusahaan dapat melakukan identifikasi konsumen potensial dengan melakukan segmentasi konsumen. Tujuan dari proses segmentasi konsumen adalah untuk mengetahui perilaku konsumen dan menerapkan strategi pemasaran yang tepat sehingga mendatangkan keuntungan bagi pihak perusahaan. Penelitian ini membahas tentang bagaimana proses data mining dari data konsumen di Toko Sweet Amirah, yaitu perusahaan retail yang khusus menjual perlengkapan dan peralatan bayi serta underwear pria dan wanita dewasa dan anak-anak dan berlokasi di Jalan Gedung Arca No. 29 B-C, Medan.ProsesData Miningini menggunakan data yang berasal dari data penjualan pada Toko Sweet Amirah dan bertujuan untuk mencari konsumen potensial.Model RFM merupakan model segmentasi yang umum digunakan pada perusahaan retail. Selanjutnya melakukan proses clustering menggunakan algoritma Fuzzy C-Means (FCM). Pada FCM jumlah cluster ditentukan. Hasil clustering dari algoritma tersebut digunakan untuk Aplikasidata miningmenggunakan MATLAB versi 7.10.0 dan memanfaatkan beberapatoolboxyaituFuzzy Logic ToolboxdanDatabase Toolbox

scholarly journals ORGANIZATION OF SUSTAINABLE AGRICULTURAL LAND USE BASED ON LANDSCAPE ANALYSIS

2020 ◽  
Vol 12 (3) ◽  
pp. 339-348
Author(s):  
Vladimir TATARINTSEV ◽  
◽  
Leonid TATARINTSEV ◽  
Alex MATSYURA ◽  
Andrei BONDAROVICH ◽  
...  
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Arable Land ◽  
Agricultural Landscapes ◽  
Landscape Analysis ◽  
Anthropogenic Activity ◽  
Agricultural Land Use ◽  
Agricultural Lands ◽  
Rational Use ◽  
Erosion Processes

The aim of the work was the landscape analysis of agricultural geographical landscapes in the Altai Territory and elaboration of measures aimed at the rational use of agricultural lands. Environmental and landscape (landscape) approach became the main method of scientific research used in the analysis of modern agricultural landscapes. The cartographic method, using GIS-technologies, made it possible to digitize the obtained materials. Synthesized maps of agro-ecological, natural and other zoning of territories are based on topographic, soil, geobotanical and other thematic maps made during land surveying during the field survey. Retrospective analysis, induction and deduction methods,analysis and synthesis, as well as the abstract-logic method were also used in the work. Our main result was the analysis of land use territory for agricultural enterprise in municipal district of Altai Krai. Exploration of lands indicates a pronounced plant-growing specialization of JSC “Pobeda” with a developed animal breeding direction. Limiting factors affecting the rational use of land are natural and climatic conditions, terrain,unsystematic anthropogenic activity and, as a result, the development of erosion processes. The degree of eroded and deflated arable land is more than 50%, hay and pasture lands are also very unstable. Landscapes have been typified, based on which eleven types of land have been identified and their geomorphological description has been carried out. The first five types of land can be used for agricultural production with limitations compensated by crop technology and erosion control measures, the sixth and seventh types require grassing and, in some cases,conservation, the eighth and ninth types can be partially used for pasture and area valorization; the remaining two are not suitable for agricultural use but should be potentially used for planting and forest management. As a result of the presented transformation of agricultural lands, the structure of cultivated areas has changed. The area of arable land decreased by 877 ha, and of pastures by 365 ha,while the area under hayfields, fallow lands, and forest lands increased by 295, 191, and 875 ha respectively. Low-productive lands were withdrawn from agriculture. We suggested that the sustainability of agricultural land use was mainly caused by the reduction of anthropogenic load and increase in ecological equilibrium of the territory.

scholarly journals IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN METODE RECENCY FREQUENCY MONETARY (RFM) PADA APLIKASI DATA MINING UNTUK PENGELOMPOKAN PELANGGAN

Sebatik ◽  
2018 ◽  
Vol 22 (2) ◽  
pp. 88-94
Author(s):  
Nindya Rahmawati Syarif ◽  
Windarto Windarto
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Fuzzy C Means ◽  
Rfm Model ◽  
Total Data

Pada dunia retail, konsumen merupakan salah satu aset yang sangat berpengaruh. Oleh sebab itu konsumen menjadi alasan perusahaan retail harus merencanakan dan mempunyai strategi yang baik dalam memperlakukan konsumennya. Dengan banyaknya jumlah konsumen yang dimiliki oleh suatu perusahaan retail, maka masalah yang harus dihadapi adalah bagaimana menentukan konsumen potensial dan menerapkan strategi pemasaran yang tepat sehingga mendatangkan keuntungan bagi pihak perusahaan. Maka dari itu, dengan menerapkan konsep CRM (Customer Relationship Management), perusahaan dapat melakukan penerapan konsumen potensial dengan melakukan segmentasi pengelompokan konsumen. Penelitian ini membahas tentang proses data mining menggunakan data konsumen dan data transaksi pada PT Eka Cipta Rasa. Proses data mining dimulai dengan melakukan proses clustering menggunakan algoritma Fuzzy C-Means (FCM). Output dari FCM adalah deretan pusat cluster dan beberapa derajat keanggotaan untuk setiap titik data. Hasil clustering digunakan untuk melakukan segmentasi kelas konsumen dengan menggunakan model Fuzzy RFM. Model Fuzzy RFM yaitu dengan menggabungkan teori himpunan fuzzy dengan model RFM dengan segmentasi berdasarkan atribut Recency, Frequency, dan Monetary. Umumnya perusahaan retail menggunakan metode ini untuk proses segmentasi konsumennya. Pengelompokkan (cluster) pelanggan berdasarkan karakteristik dan sifat saat bertransaksi dapat dijadikan suatu alternatif dalam pemecahan masalah. Dalam hal  ini, pelanggan akan dibagi menjadi 4 kelompok atau cluster pelanggan diantaranya  Golden, Silver, Bronze, dan Iron adapun variabel yang menjadi acuan pengelompokannya adalah tanggal pembelian akhir , frekuensi beli, dan total pembelian. Data yang digunakan merupakan data transaksi pelanggan periode Januari sampai Juni 2016. Total data adalah 4007 transaksi dari 328 pelanggan. Setelah data tersebut diolah dengan algoritma Fuzzy C-Means dan metode RFM, hasil akhir pada proses ini menunjukkan iterasi berakhir pada iterasi ke – 9 dengan total fungsi objektif sebesar 102,2 dan perubahan fungsi objektif sebesar 51,1. Cluster pelanggan yang dihasilkan adalah Golden : 38, Silver: 186, Bronze : 103 dan Iron : 0 .

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