scholarly journals Perancangan Data Warehouse dan Penerapan Teknik Clustering Spatial pada Wesel: Studi Kasus PT XXX

2013 ◽  
Vol 4 (1) ◽  
pp. 199
Author(s):  
Arief Dwi Hartadi ◽  
Oktalia Juwita
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
Decision Making Process ◽  
Marketing Effectiveness ◽  
Research Outcome ◽  
Clustering Data ◽  
Money Order ◽  
The Right ◽  
Em Clustering

By setting up the right service and product for the customer, the company may increase its marketing effectiveness as well as sales. For that purpose, information on company’s product and sales is highly needed in order to help on decision making process, which expectedly enables company to create value and eventually gain competitive advantage. This research attempts to develop a Data warehouse for PT XXX, which is expected to exhibit useful information for the company without disturbing its operational system. The Data warehouse is developed with Oracle 11g. Apart from that, this research also aims to gather information and knowledge with spatial database. The data mining is conducted by using WEKA application, which compares the algorithm of DBSCAN, K-Means, and EM clustering. Data mining from the spatial data is expected to create area map which indicates sales rate of PT Pos Indonesia’s products. Through this it is hoped that the company may focus more in marketing certain product with particular advantage in one particular area to produce effectiveness. The research outcome indicates that the Data warehouse being developed has already managed to exhibit the receipt report of postal money order (wesel pos), retail stock, as well as retail sales. 

The Integral of Spatial Data Mining in the Era of Big Data

2017 ◽  
pp. 90-126
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

scholarly journals OPTIMALISASI PENENTUAN SUMBER PASOKAN KOPI ARABIKA GAYO MELALUI PENDEKATAN HIRARCHICAL CLUSTERING DATA MINING

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Arie Saputra
Keyword(s):  
Data Mining ◽  
Added Value ◽  
Purchase Price ◽  
Arabica Coffee ◽  
Sampling Process ◽  
Coffee Farmers ◽  
Clustering Data ◽  
The Right

Gayo coffee scattered in the mountains and being in two districts of the central highlands and Central Aceh district has become the center of world attention. Gayo Arabica coffee has a unique manifold and the added value created by the mountainous nature Gayo. This factor makes Gayo Arabica coffee has the added value that is not replaceable by other similar commodities. The success of the stakeholders Gayo coffee obtain certification which is organic, fairtrade, coffee practice and Geographic indication that can be a proof of the worldwide recognition of the quality and added value of this coffee. The average price of the last on the coffee harvest season in March 2012 ranged between Rp 100.000,- until Rp 110.000,- in each Kg grean bean on exporter level. Determination of clusters of farmers the right so that the quality and price of supplies could be predicted well by the cooperative as exporters are very important. These routes and ketelusuran origin coffee blend in one location with other location mebuat coffee quality decreases. Mapping the supply of unclear origin uniformity of the quality of the coffee making is difficult to determine. This effect on selling prices decreased overall coffee farmers to the detriment of farmers with good quality coffee. The good name of the cooperative from the viewpoint of importers deteriorate as evidenced by a decrease in the purchase price of the importer in the contract.The sampling process quality coffee supply also becomes difficult because unhomogenity supply region. Supply region is crucial to the quality of the coffee due influenced the position and height of the land. Thus, this research is expected to help formulate clusters of farmers so that the quality and price of coffee could be improved both in terms of farmers and exporters. The last hope of course the welfare of farmers and other stakeholders could be better.Keywords: Data Mining, Optimization, Gayo Arabica Coffee, Supply Chain

scholarly journals Analisis Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Penjualan Sepeda Motor Studi Kasus PT. Alfa Scorpii

JUTI UNISI ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Abdul Muni
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Sales Promotion ◽  
Large Data Sets ◽  
Data Sets ◽  
Promotion Strategy ◽  
Corporate Earnings ◽  
Clustering Data ◽  
The Right ◽  
The Cost

PT. Alpa Scorpii is the sector private the economy in the motorcycle sales. The utilization of the data is not maximum, sales report that is used only limited to report. Promotion strategy is to increase the income of the company in relation to the straight way with the cost. The data mining so that data can be used as the existing knowledge from the large data sets or with the term knowledge discovery or pattern recognition. Many methods in data mining one only with the method the algorithm K-Means the Cluster. Clustering  data so that the field of marketing can perform the motor sales promotion strategy to new customers with the right and can improve corporate earnings.

scholarly journals Sistem Pendukung Keputusan Penentuan Lokasi Industri Berbasis Spasial Menggunakan Metode MOORA

2020 ◽  
Vol 7 (3) ◽  
pp. 639-646
Author(s):  
Agusta Praba Ristadi Pinem ◽  
Henny Indriyawati ◽  
Basworo Ardi Pramono
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
System Modeling ◽  
Decision Making Process ◽  
Industry Location ◽  
Location Determination ◽  
Strategic Location ◽  
The Right ◽  
Moora Method

Information technology is developing rapidly and the effect is every single organization will always collect data and information. The information collected is used as a basis for decision making. But not all information can be directly used for the decision making process. Method and weighting are needed in the process of getting information. One method that can be used to support the decision making process is Multi-Objective Optimization on the basis of Ratio Analysis (MOORA). MOORA is included in the Multi Criteria Decision Making (MCDM) which makes it possible to provide the best choice of information from several choices by using criteria values. This research uses the MOORA method as determining strategic industrial locations by combining spatial data. In determining the strategic location of the industry, MOORA uses several criteria and different weights for each criteria. The MOORA with spatial data can be produce the right information related to the determination of strategic industry locations by finding the correlation between method results with industry location in Semarang city. The results obtained from this research are the formation of a decision support system modeling of industrial location determination using the MOORA method with spatial data. Correlation value generated by the Spearman Rank method is 0,9.

scholarly journals THE DEVELOPMENT OF THE APPLICATION OF A DATA WAREHOUSE AT PT JKL

2012 ◽  
Vol 6 (1) ◽  
pp. 8 ◽  
Author(s):  
Choirul Huda ◽  
Rita Puspita Sari ◽  
Muharram Hasein Haekal ◽  
Tri Agustaria
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Data Warehouse ◽  
Historical Data ◽  
Decision Making Process ◽  
Literature Study ◽  
Process Methodology ◽  
The Right ◽  
A Company ◽  
Analyze Data

One rapidly evolving technology today is information technology, which can help decision-making in an organization or a company. The data warehouse is one form of information technology that supports those needs, as one of the right solutions for companies in decision-making. The objective of this research is the development of a data warehouse at PT JKL in order to support executives in analyzing the organization and support the decision-making process. Methodology of this research is conducting interview with related units, literature study and document examination. This research also used the Nine Step Methodology developed by Kimball to design the data warehouse. The results obtained is an application that can summarize the data warehouse, integrating and presenting historical data in multidimensional. The conclusion from this research is the data warehouse can help companies to analyze data in a flexible, fast, and effective data access.Keywords: Data Warehouse; Inventory; Contract Approval; Inventory; Dashboard

The Integral of Spatial Data Mining in the Era of Big Data

2019 ◽  
pp. 863-899
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering

2020 ◽  
Vol 3 (3) ◽  
pp. 187-201
Author(s):  
Sufajar Butsianto ◽  
Nindi Tya Mayangwulan
Keyword(s):  
Data Mining ◽  
Clustering Data ◽  
Cluster 2

Penggunaan mobil di Indonesia setiap tahunnya selalu meningkat dan membuat perusahaan otomotif berlomba-lomba dalam peningkatan penjualannya. Tujuan dari penelitian ini untuk mengelompokan data penjualan kedalam sebuah cluster dengan metode Data Mining Algoritma K-Means Clustering. Data Penjualan nantinya akan dikelompokan berdasarkan kemiripan data tersebut sehingga data dengan karakteristik yang sama akan berada dalam satu cluster. Atribut yang digunakan adalah brand dan penjualan. Cluster yang terbentuk setelah dilakukan proses K-Means Clustering terbagi menjadi tiga cluster yaitu Cluster 0 jumlah anggota 235 dengan presentase 26% dikategorikan Laris, Cluster 1 jumlah anggota 604 dengan presentase 67% dikategorikan Kurang Laris, dan Cluster 2 jumlah angota 61 dengan presentase 7% dikategorikan Paling Laris, dari proses clustering diatas dapat diperoleh validasi DBI (Davies Bouldin Index) dengan nilai 0,341

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

IMPLEMENTASI DATA MINING UNTUK MENENTUKAN TINGKAT PENJUALAN PAKET DATA TELKOMSEL MENGGUNAKAN METODE K-MEANS CLUSTERING

2020 ◽  
Vol 25 (1) ◽  
pp. 76-88
Author(s):  
Suhandio Handoko ◽  
Fauziah Fauziah ◽  
Endah Tri Esti Handayani
Keyword(s):  
Data Mining ◽  
Clustering Data

Perkembangan industri telekomunikasi saat ini sangat pesat karena telekomunikasi sudah menjadi kebutuhan utama bagi masyarakat sehingga banyak perusahaan yang bergerak di industry telekomunikasi. Banyaknya industry Telekomunikasi menuntut para pengembang untuk menemukan strategi atau suatu pola yang dapat meningkatkan penjualan dan pemasaran produk, salah satu strateginya adalah dengan memanfaatkan data transaksi. Paket data merupakan produk dibidang telekomunikasi. Proses Clustering saat ini masih di lakukan secara manual sehingga membutuhkan waktu, proses perhitungan dan ketelitian yang tinggi. Pada penelitian ini dibuat aplikasi berbasis website dengan tujuan untuk mempermudah Clustering data sehingga dapat digunakan sebagai referensi dalam perencanaan promosi produk telkomsel ke berbagai daerah. Metode yang digunakan untuk mengatasi permasalahan tersebut yaitu metode Clustering dengan menggunakan Algoritma K-Means. Algoritma K-Means merupakan algoritma pengelompokkan sejumlah data menjadi menjadi kelompok-kelompok data tertentu. Pada penelitian ini data penjualan dikelompokkan menjadi 3 yaitu data penjualan rendah, data penjualan sedang dan data penjualan tinggi. Pengujian clustering dengan algoritma K-Means pada aplikasi terhadap data transaksi penjualan paket telkomsel diperoleh persentase kesesuaian yaitu 100% dibandingkan dengan clustering manual.

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