scholarly journals Pemetaan Wilayah Potensial Terhadap Penjualan Sepeda Motor Honda Menggunakan K-Means Clustering

2020 ◽  
pp. 53-59
Author(s):  
Zulrahmadi ◽  
Sarjon Defit ◽  
Yuhandri Yunus
Keyword(s):  
Data Processing ◽  
Clustering Method ◽  
Sales Data ◽  
Cluster 2

Indragiri Hilir regency consists of land and water which are divided into 20 districts, 39 sub-districts and 197 villages. Looking at the geographical condition of Indragiri Hilir Regency, motorcycle sales companies need to know the areas that have potential for motorcycle sales. Grouping potential areas is important in increasing sales profit for the company. This study aims to help PT. Capella Dinamik Nusantara in making the decision to increase sales to be more significant, promotion and marketing techniques were more targeted towards Honda motorcycle sales in the mapped areas. The data used in this study are Honda motorcycle sales data from 2017 to 2019. Data processing in this study uses the K-Means Clustering method with 3 clusters, namely Cluster 0 (C0) Less Potential, Cluster 1 (C1) Enough Potential, Cluster 2 (C2) Has the potential to sell Honda motorcycles. The result of the grouping process with 2 iterations states that for Cluster 0 there are 5 regions, for Cluster 1 there are 3 regions, and for Cluster 2 there are 2 regions.

scholarly journals Goods Stock Management using the K-Means Algorithm Method

2020 ◽  
Vol 10 (1) ◽  
pp. 22-45
Author(s):  
Dhio Saputra
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Test Results ◽  
Stock Management ◽  
Clustering Method ◽  
Sales Data ◽  
Using Data ◽  
Cluster 2

The grouping of Mazaya products at PT. Bougenville Anugrah can still do manuals in calculating purchases, sales and product inventories. Requires time and data. For this reason, a research is needed to optimize the inventory of Mazaya goods by computerization. The method used in this research is K-Means Clustering on sales data of Mazaya products. The data processed is the purchase, sales and remaining inventory of Mazaya products in March to July 2019 totaling 40 pieces. Data is grouped into 3 clusters, namely cluster 0 for non-selling criteria, cluster 1 for best-selling criteria and cluster 2 for very best-selling criteria. The test results obtained are cluster 0 with 13 data, cluster 1 with 25 data and cluster 2 with 2 data. So to optimize inventory is to multiply goods in cluster 2, so as to save costs for management of Mazayaproducts that are not available. K-Means clustering method can be used for data processing using data mining in grouping data according to criteria.

scholarly journals Analisis K-Medoids Clustering Dalam Pengelompokkan Data Imunisasi Campak Balita di Indonesia

2019 ◽  
Vol 1 ◽  
pp. 687
Author(s):  
Siti Sundari ◽  
Irfan Sudahri Damanik ◽  
Agus Perdana Windarto ◽  
Heru Satria Tambunan ◽  
Jalaluddin Jalaluddin ◽  
...  
Keyword(s):  
Data Processing ◽  
Virus Transmission ◽  
Clustering Method ◽  
Immunization Program ◽  
Data Grouping ◽  
Cluster 2 ◽  
High Cluster ◽  
Central Statistics

Measles is a contagious infections disease that attacks children caused by a virus. Transmission of measles from people through coughing and sneezing. Measles causes disability and death, so further threatment is needed. Measles immunization program that can inhibit the development of measles is one of the efforts in eradicating the disease. In this study the data used were sourced from the Central Statistics Agency National in 2013-2017. This study uses datamining techniques in data processing with K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break datasets into groups. The advantages of this method are the ability to overcome the weaknesses of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the entry sequence of the dataset. The k-medoids clustering method can be applied to the data on the percentage of measles immunization can be identified based on province, so that the grouping of provinces based on these data. From the data grouping three clusters are obtained: low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) with the percentage of measles immunization in each of these provinces from data grouping in percentage. It is expected this research can provide information to the govermant about the data on grouping measles immunization for toddlers in Indonesia which has an impact on the distribution of immunization against measles toddlers in Indonesia.

Penerapan Data Mining Metode Clustering Pada CV. Secom Infotech Menggunakan Algoritma K-Means

2019 ◽  
Vol 1 ◽  
pp. 96
Author(s):  
Dicky Juliawan ◽  
Faisal Amir ◽  
Efani Desi
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Hierarchical Clustering ◽  
Item Type ◽  
Clustering Method ◽  
Sales Data ◽  
Mining Methods ◽  
Existing Data

Grouping sales data at the Secom Infotech Computer Store is still done manually in Excel. How to group it takes time and allows data to be lost. Clustering is one of the data mining methods that are unsupervised and K-Means is a non-hierarchical clustering method that attempts to divide existing data into one or more groups. The K-Means clustering method can be applied to classify a sales data based on the type of item, type of customer, number of items. The data used is sales data in January-June 2018 as many as 30 data. The results of the tests were carried out using the RapidMiner application where the results contained 2 clusters, namely cluster 0 totaling 14 data and cluster 1 totaling 16 data. K-Means clustering method can be used for data processing using the concept of data mining in grouping data according to attributes.

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.

scholarly journals Clustering Fasilitas Kesehatan Berdasarkan Kecamatan Di Karawang Dengan Algoritma K-Means

2021 ◽  
Vol 8 (1) ◽  
pp. 83
Author(s):  
Bagus Muhammad Islami ◽  
Cepy Sukmayadi ◽  
Tesa Nur Padilah
Keyword(s):  
Data Mining ◽  
Developing Countries ◽  
Data Processing ◽  
Health Problems ◽  
Health Facilities ◽  
Microsoft Excel ◽  
Two Factors ◽  
Clustering Data ◽  
Index Value ◽  
Cluster 2

Abstrak: Masalah kesehatan yang ada di dalam masyarakat terutama di negara- negara berkembang seperti Indonesia dipengaruhi oleh dua faktor yaitu aspek fisik dan aspek non fisik. Berdasarkan data yang diperoleh dari karawangkab.bps.go.id data dibagi menjadi 3 cluster yaitu sedikit, sedang dan terbanyak. Algoritma yang digunakan adalah K-Means cluster yang diimplementsikan menggunakan Microsoft Excel dan Rapidminer Studio. Hasil pengolahan data fasilitas kesehatan di karawang menghasilkan 3 cluster dengan cluster 1 yang mempunyai fasilitas kesehatan sedikit sebanyak 23 kecamatan, cluster 2 yang mempunyai fasilitas kesehatan sedang sebanyak 5 kecamatan dan cluster 3 yang mempunyai fasilitas kesehatan terbanyak terdapat 2 kecamatan. Kinerja yang dihasilkan dari algoritma K-means menghasilkan nilai Davies Boildin Index sebesar 0,109.   Kata kunci: clustering, data mining, fasilitas kesehatan, K-Means.   Abstract: Health problems that exist in society, especially in developing countries like Indonesia, are built by two factors, namely physical and non-physical aspects. Based on data obtained from karawangkab.bps.go.id the data is divided into 3 clusters, namely the least, medium and the most. The algorithm used is the K-Means cluster which is implemented using Microsoft Excel and Rapidminer Studio. The results of data processing of health facilities in Karawang produce 3 clusters with cluster 1 which has 23 sub-districts of health facilities, cluster 2 which has medium health facilities as many as 5 districts and cluster 3 which has the most health facilities in 2 districts. The performance resulting from the K-means algorithm results in a Davies Boildin Index value of 0.109.   Keywords: clustering, data mining, health facilities, K-Means.

scholarly journals KOMPARASI METODE CLUSTERING K-MEANS DAN K-MEDOIDS DENGAN MODEL FUZZY RFM UNTUK PENGELOMPOKAN PELANGGAN

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Elly Muningsih - AMIK BSI Yogyakarta
Keyword(s):  
Data Clustering ◽  
Small Data ◽  
Clustering Methods ◽  
Monetary Model ◽  
Clustering Method ◽  
Online Sales ◽  
Rfm Model ◽  
Potential Customers ◽  
Cluster 2 ◽  
Better Than

Abstract ~ The K-Means method is one of the clustering methods that is widely used in data clustering research. While the K-Medoids method is an efficient method used for processing small data. This study aims to compare two clustering methods by grouping customers into 3 clusters according to their characteristics, namely very potential (loyal) customers, potential customers and non potential customers. The method used in this study is the K-Means clustering method and the K-Medoids method. The data used is online sales transaction. The clustering method testing is done by using a Fuzzy RFM (Recency, Frequenty and Monetary) model where the average (mean) of the third value is taken. From the data testing is known that the K-Means method is better than the K-Medoids method with an accuracy value of 90.47%. Whereas from the data processing carried out is known that cluster 1 has 16 members (customers), cluster 2 has 11 members and cluster 3 has 15 members. Keywords : clustering, K-Means method, K-Medoids method, customer, Fuzzy RFM model. Abstrak ~ Metode K-Means merupakan salah satu metode clustering yang banyak digunakan dalam penelitian pengelompokan data. Sedangkan metode K-Medoids merupakan metode yang efisien digunakan untuk pengolahan data yang kecil. Penelitian ini bertujuan untuk membandingkan atau mengkomparasi dua metode clustering dengan cara mengelompokkan pelanggan menjadi 3 cluster sesuai dengan karakteristiknya, yaitu pelanggan sangat potensial (loyal), pelanggan potensial dan pelanggan kurang (tidak) potensial. Metode yang digunakan dalam penelitian ini adalah metode clustering K-Means dan metode K-Medoids. Data yang digunakan adalah data transaksi penjualan online. Pengujian metode clustering yang dilakukan adalah dengan menggunakan model Fuzzy RFM (Recency, Frequenty dan Monetary) dimana diambil rata-rata (mean) dari nilai ketiga tersebut. Dari pengujian data diketahui bahwa metode K-Means lebih baik dari metode K-Medoids dengan nilai akurasi 90,47%. Sedangkan dari pengolahan data yang dilakukan diketahui bahwa cluster 1 memiliki 16 anggota (pelanggan), cluster 2 memiliki 11 anggota dan cluster 3 memiliki 15 anggota. Kata kunci : clustering, metode K-Means, metode K-Medoids, pelanggan, model Fuzzy RFM.

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 Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

2021 ◽  
Vol 22 (1) ◽  
pp. 476
Author(s):  
Rini Sovia ◽  
Abulwafa Muhammad ◽  
Syafri Arlis ◽  
Guslendra Guslendra ◽  
Sarjon Defit
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Medicinal Product ◽  
Pharmaceutical Products ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
Great Demand ◽  
Mining Algorithm ◽  
Using Data

<p>This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 Data Mining Algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the C45 algorithm produces a number of rules that can identify the inheritance of a type of medicinal product. C45 algorithm is able to produce 20 types of categories that will be labeled goals based on the number of pharmaceutical products, since it can be concluded that C45 successfully defines 55% of the existing objective categories.</p>

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

scholarly journals Pengelompokan Nilai Akademik untuk Menentukan Kenaikan Kelas Menggunakan Algoritma K-Medoids

2019 ◽  
Vol 1 ◽  
pp. 740
Author(s):  
Sulastry Silitonga ◽  
Eka Irawan ◽  
Saifullah Saifullah ◽  
Muhamad Ridwan Lubis ◽  
Iin Parlina
Keyword(s):  
Academic Achievement ◽  
Data Collection ◽  
Success Rate ◽  
School Curriculum ◽  
Average Score ◽  
Clustering Method ◽  
Academic Values ◽  
School Grades ◽  
Cluster 2 ◽  
Grade 11

To find out the success rate of each student in mastering each activity that is followed, the school uses the student's academic value as its parameter. To facilitate the school curriculum in data collection on the success of each student based on academic achievement, through this thesis research the author would like to propose a method of k-medoids based on the achievement of student academic values. The student value data variables that will be used in this study are the average score of the report, the average extracurricular value, and the average aklak value or personality. Data was obtained from the administration of school grades at SMA Negeri 2 Siborong-borong, especially the grade 11 value data for even semester examinations. The clustering method used in writing this research is the K-medoids algorithm. The clustering results show that groups of student value data formed as many as 3 clusters and the results in cluster 1 as many as 18items, cluster 2 as many as 44 items and as many as 30 items .

Sign in / Sign up

Export Citation Format

Share Document