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.

scholarly journals Implementasi Metode K-Medoids Clustering Dalam Pengelompokan Data Penyakit Alergi Pada Anak

2021 ◽  
Vol 6 (1) ◽  
pp. 130
Author(s):  
Haryati Ningrum ◽  
Eka Irawan ◽  
Muhammad Ridwan Lubis
Keyword(s):  
Immune System ◽  
Allergic Diseases ◽  
Health Department ◽  
Clustering Method ◽  
The Public ◽  
National Statistics ◽  
Abnormal Response ◽  
Data Grouping ◽  
Cluster 2 ◽  
High Cluster

Allergies are an abnormal response from the immune system. People who experience allergies have an immune system that reacts to a substance that is usually harmless in the environment. There are two limitations in this study, namely, seafood allergy and air allergy. In this study, the data used were sourced from the National Statistics Agency in 2011-2019. This study uses data mining techniques in data processing with the k-medoids clustering method. The k-medoids method is a clustering method that functions to split the dataset into several groups. The advantages of this method are able to overcome the weaknesses of the k-means method which is sensitive to outliers. Another advantage of this method is that the results of the clustering process do not depend on the order in which the dataset is entered. This method can be applied to data on the percentage of children affected by allergies by province, so that it can be seen the grouping of provinces based on this data. From this grouping data obtained 3 clusters namely low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) from the percentage of allergy immunization under five in each province. It is hoped that this research can provide information to the health department, especially the public health center regarding data grouping of Allergic Diseases in children in Indonesia which has an impact on equity in giving anti-allergic immunization to children in Indonesia

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 Dalam Pengelompokkan Penduduk Buta Huruf Menurut Provinsi

2019 ◽  
Vol 1 ◽  
pp. 721
Author(s):  
Sri Rahayu Ningsih ◽  
Irfan Sudahri Damanik ◽  
Agus Perdana Windarto ◽  
Heru Satria Tambunan ◽  
Jalaluddin Jalaluddin ◽  
...  
Keyword(s):  
Decision Making ◽  
Implementation Process ◽  
The State ◽  
Clustering Method ◽  
The Government ◽  
High Cluster ◽  
Central Statistics

Illiteracy is the state of being unable to read and to write for communication. A large number of people still experiencing illiteracy in a country is one indicator showing that the country is still not developed. As many as 3.4 million people or around 2.07% of the population in Indonesia are still illiterate. This study aims to create a grouping model using the k-medoids algorithm. The k-medoids method is a clustering method that serves to break down datasets into groups. The data used is sourced from the Central Statistics Agency. Entered data are percentage of illiterate population in 2009-2017. The number of records used is 34 provinces which are divided into 3 clusters namely high cluser, medium cluster and low cluster. From the results of k-medoids calculation, one (1) province was categorited as a high cluster, twelve (12) provinces as a medium cluster and twenty-one (21) provinces as a low cluster. The implementation process using the RapidMiner 5.3 application is used to help find accurate values. It is hoped that this research can be used as one of the bases for decision making for the government in an effort to equalize the level of illiteracy according to the province which has an impact on reducing of illiteracy rates in Indonesia.

scholarly journals Smartphone Price Grouping by Specifications using K-Means Clustering Method

2021 ◽  
Vol 13 (2) ◽  
pp. 64-74
Author(s):  
Ahmad Agung Zefi Syahputra ◽  
Annisa Dwi Atika ◽  
Muhammad Adam Aslamsyah ◽  
Meida Cahyo Untoro ◽  
Winda Yulita
Keyword(s):  
College Students ◽  
High School ◽  
Data Mining ◽  
Confusion Matrix ◽  
Data Mining Algorithm ◽  
Clustering Method ◽  
Mining Algorithm ◽  
Data Grouping ◽  
The Right ◽  
Cluster 2

The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similarity of the characteristics by one data with other data, which is memory_size and best_price. The results of this research indicated that the right clustering of smartphone prices was within 3 different clusters, which was cluster 0 has centroid of Rp2.000.000,00, cluster 1 has centroid of Rp18.000.000,00, and cluster 2 has centroid of Rp9.000.000,00. The results of the evaluation used a confusion matrix, summary of prediction result, indicated that the clustering process had 100% of accuracy that could be seen on the table which showed the results of clustering. The conclusion from this research was that K-Means Clustering could form clusters in determining the price of a smartphone in relation to the specifications used as the attribute determining the price cluster for a smartphone.

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 Penerapan Metode K-Means Pada Pengelompokkan Pengangguran Di Indonesia

2021 ◽  
Vol 6 (1) ◽  
pp. 61
Author(s):  
Fadhillah Azmi Tanjung ◽  
Agus Perdana Windarto ◽  
M Fauzan
Keyword(s):  
Data Mining ◽  
Labor Force ◽  
Research Data ◽  
Clustering Method ◽  
Employment Opportunities ◽  
Data Mining Techniques ◽  
Unemployment Data ◽  
The Government ◽  
Cluster 2 ◽  
Central Statistics

Unemployment is a group of labor force who has not done an activity that generates money. Someone who is said to be unemployed can also be categorized as people who have not worked, people who are looking for work, or people who have worked but have not gotten productive results. The purpose of this study is to analyze the unemployment stay by province in Indonesia. This research data is sourced from the Central Statistics Agency in 2014 - 2019. This study uses data mining techniques, namely the K-means algorithm, the K-means method is a clustering method that functions to break the dataset into groups. The K-means method can be used for percentage unemployment data by province. Data will be divided or grouped into 2 Clusters, where Cluster 1 is the group of provinces with the highest potential for unemployment with the results of 13 provinces and Cluster 2 is the province with the lowest potential unemployment results which is 21 provinces. The results of this study are as a way to assist the government in expanding employment to develop and improve the economy in each province in Indonesia. It is hoped that this research can provide input to the government. In particular, the provinces with minimal employment opportunities in Indonesia have an impact on unemployment

scholarly journals Algoritma K-Medoids untuk Mengelompokkan Desa yang Memiliki Fasilitas Sekolah di Indonesia

2019 ◽  
Vol 1 ◽  
pp. 520
Author(s):  
Ivana Indrini Putri Damanik ◽  
Solikhun Solikhun ◽  
Ilham Syahputra Saragih ◽  
Iin Parlina ◽  
Dedi Suhendro ◽  
...  
Keyword(s):  
School Facilities ◽  
Education Level ◽  
Clustering Method ◽  
Learning To Read ◽  
National Statistics ◽  
Level Data ◽  
School Laboratory ◽  
The Government ◽  
Representative Points ◽  
High Cluster

School facilities are learning facilities and infrastructure. Study rooms, study rooms, sports halls, prayer rooms, arts rooms and sports equipment. Means of learning to read textbooks, reading books, school laboratory tools and facilities and various other learning media. This study discusses the application of the K-Medoids method in grouping villages that have school facilities based on the province and education level. Data sources used from the National Statistics Agency (BPS). This study uses data mining techniques in data processing using the k-medoids clustering method. The k-medoid method is part of a fairly efficient grouping of partitions in small datasets and looks for the most representative points. The advantages of this method can overcome the shortcomings of the k-means method that is sensitive to outliers. Another advantage of this method is that the results of the grouping process do not match the entry sequence of the dataset. Grouping k-medoid method can be applied to the percentage of facilities based on the province, so that provincial grouping can be determined based on the data. From the grouping data, 3 clusters were obtained, namely a low cluster of 15 provinces, a moderate cluster of 16 provinces and a high cluster of 3 provinces from the percentage of school facilities in each province. It is hoped that this research can provide information to the government about data collection of school facilities in Indonesia which discusses examiners in the provision of school facilities in Indonesia.

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.

scholarly journals Book data grouping in libraries using the k-means clustering method

2019 ◽  
Vol 1230 ◽  
pp. 012074 ◽  
Author(s):  
Saut Parsaoran Tamba ◽  
M Diarmansyah Batubara ◽  
Windania Purba ◽  
Maria Sihombing ◽  
Victor Marudut Mulia Siregar ◽  
...  
Keyword(s):  
Clustering Method ◽  
Data Grouping
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