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 PENERAPAN DATA MINING DALAM MENGELOMPOKAN PRODUKSI JAGUNG MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Nanda Erlangga ◽  
Solikhun Solikhun ◽  
Irawan Irawan
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Rapid Development ◽  
Domestic Market ◽  
Corn Production ◽  
Low Level ◽  
High Level ◽  
Level Cluster ◽  
Central Statistics

Corn needs are currently experiencing a fairly rapid development can be seen in terms of the domestic market, here researchers want to increase the productivity and quality of corn production. The data that will be used is the data from the Central Statistics Agency. The method in this study is the K-means clustering algorithm and the application used is Rapidminer which will be grouped into 2 clustering, namely high and low. The results of this study are 2 high level cluster provinces, 32 low level cluster provincesKeywords: Corn, Data mining, K-means Clustering c

scholarly journals Analysis of Data Mining Using K-Means Clustering Algorithm for Product Grouping

2020 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Mohammad Imron ◽  
Uswatun Hasanah ◽  
Bahrul Humaidi
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Clustering Algorithm ◽  
Processing Technology ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Sales Data ◽  
Using Data

Rizki Barokah Store is one of the stores that every day sell a variety of basic materials of daily necessities such as food, drinks, snacks, toiletries, and so on. However, some problems occur in the Rizki Barokah Store is often a build-up of product stocks that resulted in the product has expired. This is due to an error in making decisions on the product stock. In addition to these problems, with the amount of sales data stored on the database, the store has not done data mining and grouping to know the potential of the product. Whereas data-processing technology can already be done using data mining techniques. To overcome the period of the land, the technique used in data mining with the clustering method using the algorithm K-means. With the use of these techniques, the purpose of this research is to grouping products based on products of interest and less interest, advise on the stock of products, and know the products of interest and less demand.

Adaptive Feature Selection for Ultrasound Image Processing and its Application to Liver Cirrhosis Detection

2013 ◽  
Vol 411-414 ◽  
pp. 1372-1376
Author(s):  
Wei Tin Lin ◽  
Shyi Chyi Cheng ◽  
Chih Lang Lin ◽  
Chen Kuei Yang
Keyword(s):  
Clustering Algorithm ◽  
Ultrasound Image ◽  
Image Features ◽  
Ultrasound Images ◽  
Analytic Hierarchy ◽  
Low Level ◽  
Adaptive Feature Selection ◽  
High Level ◽  
Objects Detection ◽  
Hierarchy Process

An approach to improve the regions of interesting (ROIs) selection accuracy automatically for medical images is proposed. The aim of the study is to select the most interesting regions of image features that good for diffuse objects detection or classification. We use the AHP (Analytic Hierarchy Process) to obtain physicians high-level diagnosis vectors and are clustered using the well-known K-Means clustering algorithm. The system also automatically extracts low-level image features for improving to detect liver diseases from ultrasound images. The weights of low-level features are adaptively updated according the feature variances in the class. Finally, the high-level diagnosis decision is made based on the high-level diagnosis vectors for the top K near neighbors from the medical experts classified database. Experimental results show the effectiveness of the system.

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.

Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value

2017 ◽  
pp. 24-48 ◽  
Author(s):  
Mamta Mittal ◽  
R. K. Sharma ◽  
V.P. Singh ◽  
Lalit Mohan Goyal
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Mining Techniques ◽  
Single Pass ◽  
Hidden Patterns ◽  
Data Objects

Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five experiments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets.

Automatic Image Annotation Using Semantic Subspace Graph Spectral Clustering Algorithm

2011 ◽  
Vol 271-273 ◽  
pp. 1090-1095
Author(s):  
Yu Tang Guo ◽  
Chang Gang Han
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Image Annotation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Semantic Level ◽  
Semantic Class ◽  
Low Level ◽  
Advantages And Disadvantages ◽  
High Level

Due to the existing of the semantic gap, images with the same or similar low level features are possibly different on semantic level. How to find the underlying relationship between the high-level semantic and low level features is one of the difficult problems for image annotation. In this paper, a new image annotation method based on graph spectral clustering with the consistency of semantics is proposed with detailed analysis on the advantages and disadvantages of the existed image annotation methods. The proposed method firstly cluster image into several semantic classes by semantic similarity measurement in the semantic subspace. Within each semantic class, images are re-clustered with visual features of region Then, the joint probability distribution of blobs and words was modeled by using Multiple-Bernoulli Relevance Model. We can annotate a unannotated image by using the joint distribution. Experimental results show the the effectiveness of the proposed approach in terms of quality of the image annotation. the consistency of high-level semantics and low level features is efficiently achieved.

scholarly journals Survival Analysis of Python and R within the Job Market Trend

2020 ◽  
Vol 1 (1) ◽  
pp. 31-40
Author(s):  
Hina Afzal ◽  
Arisha Kamran ◽  
Asifa Noreen
Keyword(s):  
Data Mining ◽  
Survival Analysis ◽  
Programming Languages ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Techniques ◽  
R Programming Language ◽  
R Programming ◽  
High Level

The market nowadays, due to the rapid changes happening in the technologies requires a high level of interaction between the educators and the fresher coming to going the market. The demand for IT-related jobs in the market is higher than all other fields, In this paper, we are going to discuss the survival analysis in the market of parallel two programming languages Python and R . Data sets are growing large and the traditional methods are not capable enough of handling the large data sets, therefore, we tried to use the latest data mining techniques through python and R programming language. It took several months of effort to gather such an amount of data and process it with the data mining techniques using python and R but the results showed that both languages have the same rate of growth over the past years.

scholarly journals Adaptive Upgradation of Personalized E-Learning Portal using Data Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 224-227
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Experience ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Development Phase ◽  
Data Mining Techniques ◽  
E Learning ◽  
Learning Portal

Implementation of data mining techniques in elearning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pretraining program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his/her performance in the pretraining program.

scholarly journals Clustering Clustering Data Eskspor Buah-Buahan Berdasarkan Negera Tujuan Menggunakan Algoritma K-Means

2021 ◽  
Vol 8 (1) ◽  
pp. 73
Author(s):  
Haviz Atma Negara ◽  
Achmad Rizaldi Putra ◽  
Ultach Enri
Keyword(s):  
Data Mining ◽  
United Arab Emirates ◽  
Economic Activity ◽  
Foreign Currency ◽  
Clustering Method ◽  
Maximum Profit ◽  
The Government ◽  
Clustering Data ◽  
Food Commodities ◽  
Level Cluster

Abstrak: Ekspor merupakan kegiatan ekonomi dalam memasarkan dan jual barang, baik industry, pangan, serta kebutuhan lainnya kepada negara lainnya yang memiliki kurs atau nilai mata uang asing yang lebih besar, tujuannya ialah untuk mencari keuntungan yang sebesar-besarnya. Penelitian ini bertujuan untuk menerapkan data mining dengan metode k-means clustering data ekspor buah-buahan menurut negara tujuannya yang merupakan salah satu komoditas pangan. Penelitian ini menggunakan data pada tahun 2012 sampai 2019 yang diambil melalui situs https://www.bps.go.id. Data diolah dengan mengklasterkan data ekspor kedalam 3 kelompok yaitu tinggi, sedang dan rendah. Didapatkan hasil Centroid data untuk cluster tingkat ekspor tinggi sebesar 2054519.3, centroid data untuk cluster tingkat ekspor sedang sebesar 489020.3, centroid data untuk cluster tingkat ekspor rendah sebesar 20.2. Sehingga diperoleh hasil cluster negara tujuan ekspor yaitu 2 negara cluster tingkat ekspor tinggi yakni negara Tiongkok & Malaysia, 2 negara cluster tingkat ekspor rendah yakni Vietnam & Thailand, dan 6 negara cluster tingkat ekspor rendah yakni Hongkong, Singapura, Nigeria, India, Jepang, Uni Emirat Arab. Informasi pengklasteran data ekspor buah-buahan ini dapat menjadi saran serta masukan bagi pemerintah maupun perusahaan-perusahaan swasta maupun negeri dalam menentukan strategi ekspor buah-buahan dimasa depan.   Kata kunci: buah-buahan, data mining, ekspor, k-means, rapid miner.   Abstract Export is an economic activity in marketing and selling goods, both industrial, food, and other needs to other countries that have a higher exchange rate or foreign currency value, the aim is to seek the maximum profit. This study discusses the application of data mining using the k-means clustering method on fruit export data based on destination countries. This study uses export data from one of Indonesia's food commodities, namely fruits based on the main destination countries in 2012 to 2019 which was taken through the https://www.bps.go.id site. The data is processed by clustering the export data into 3 groups, namely high, medium and low. The data centroid for the high export level cluster is 2054519.3, the data centroid for the medium export level cluster is 489020.3, the data centroid for the low export level cluster is 20.2. So that an assessment is obtained based on the fruit export index with 2 cluster countries with high export levels, namely China & Malaysia, 2 cluster countries with low export levels, namely Vietnam & Thailand, and 6 cluster countries with low export levels, namely Hong Kong, Singapore, Nigeria, India, Japan, United Arab Emirates. This information on clustering fruit export data can be a suggestion and input for the government and private and state companies in determining fruit export strategies in the future.   Keywords: fruits, data mining, exports, k-means, rapid miner.

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

Sign in / Sign up

Export Citation Format

Share Document