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.

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 Segmentasi Perkebunan Kelapa Sawit dengan Data Mining Teknik K-Means Clustering Berdasarkan Luas Areal, Produksi dan Produktivitas

2021 ◽  
Vol 2 (2) ◽  
pp. 56
Author(s):  
Trisna Yuniarti ◽  
Dahliyah Hayati
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Oil Palm ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Mining Technique ◽  
Mining Technique ◽  
The Government ◽  
Clustering Data ◽  
Medium Potential

The oil palm is the most productive plantation product in Indonesia. Government strategies and policies related to oil palm plantations continue to be carried out considering that the plantation area is increasing every year. Segmentation of oil palm plantations based on area, production, and productivity aims to identify groups of potential oil palm plantations in the territory of Indonesia. This segmentation can provide consideration in formulating strategies and policies that will be made by the government. The segmentation method for grouping oil palm plantations uses the K-Means Clustering Data Mining technique with 3 clusters specified. Data mining stages start from data collection until representation is carried out, where 34 data sets are collected, only 25 data sets can be processed further. The results of this grouping obtained three plantation segments, namely 72% of the plantation group with low potential, 20% of the plantation group with medium potential, and 8% of the plantation group with high potential.

scholarly journals Penerapan algorithma k-mean untuk clustering data obat pada puskesmas rumbai

2016 ◽  
Vol 7 (2) ◽  
pp. 108-114
Author(s):  
Taslim Taslim ◽  
Fajrizal Fajrizal
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Decision Support Tool ◽  
Health Centers ◽  
Health Clinic ◽  
Support Tool ◽  
Insurance Program ◽  
Health Insurance Program ◽  
The Government ◽  
Clustering Data

Abstrak- Melalui program jaminan kesehatan pemerintah berupaya terus menjamin kesehatan bagi masyarakat melalui puskesmas puskesmas atau balai pengobatan. Salah satu komponen yang sangat penting pada puskesmas maupun balai pengobatan adalah masalah ketersediaan obat. Ketersediaan obat harus dikelola secara baik untuk menjamin obat yang dibutuhkan oleh masyarakat selalu tersedia dengan jumlah yang cukup dan memadai. Clusterisasi pada data mining dapat digunakan untuk menganalisa pemakaian obat yang terjadi selama ini pada sebuah puskesmas untuk digunakan sebagai salah satu alat bantu penunjang keputusan bagi pihak puskesmas untuk mengajukan permintaan obat pada periode yang akan datang. Hasil dari penelitian ini dapat mengelompokkan tingkat pemakaian obat pada apotik puskesmas Rumbai Bukit Pekanbaru Kata Kunci : Data mining, k-means, obat, puskesmas, cluster Abstrack- Through the government health insurance program seeks to continue to ensure public health through public health centers health centers or clinics. One very important component in health centers and clinics is the issue of availability of drugs. Availability of the drug should be managed properly to ensure the drug is needed by the community always available in sufficient quantities and adequate. Clusterisasi on data mining can be used to analyze the use of drugs that occurred during this time in a health clinic to be used as a decision support tool for the clinic to request the drug in the coming period. The results of this research can classify the level of drug use at the health center pharmacy Tassel Hill Pekanbaru Keyword: Data mining, k-means, medicine, health centers, cluster

Algeria will create new obstacles for importers

2020 ◽  
Keyword(s):  
Economic Activity ◽  
Foreign Currency ◽  
Content Type ◽  
Global Policy ◽  
Policy Responses ◽  
The Government ◽  
External Borrowing ◽  
Push Up

Significance In part, this reflects a slump in economic activity because of the COVID-19 pandemic and global policy responses. Yet it also points to a longer-term push to limit foreign currency outflows, including through additional layers of regulation for importers. Impacts Plans to overhaul the local shipping sector are unlikely to yield immediate results. If the import targets are not met, the government may have to reconsider its refusal to utilise external borrowing. The combination of a devaluation and shortages of products on the market will push up inflation.

scholarly journals IMPLEMENTASI DATA MINING UNTUK KARTU INDONESIA SEHAT BAGI MASYARAKAT KURANG MAMPU MENGGUNAKAN METODE CLUSTERING PADA DINAS SOSIAL KOTA PALEMBANG

2020 ◽  
Vol 1 (4) ◽  
pp. 169-174
Author(s):  
Wulan Lestari ◽  
Fatoni Fatoni ◽  
Hutrianto Hutrianto
Keyword(s):  
Data Mining ◽  
Social Welfare ◽  
Social Service ◽  
Population Data ◽  
Clustering Method ◽  
The Social ◽  
Long Time ◽  
The Government ◽  
The City ◽  
Government Institution

The Social Service Office of Palembang City is a government institution that is responsible for carrying out development in the field of social welfare, which covers all programs and activities that are intended to realize, foster and maintain, restore and develop social welfare carried out together as the responsibility of the government and society. In conducting data processing to determine the capable and underprivileged people as recipients of the Healthy Indonesia Card (KIS) with a process that takes a long time, given the large number of population data to be selected, especially in the city of Palembang. Then it can be raised into the research, namely "Implementation of Data Mining for Healthy Indonesia Cards for the Underprivileged Community Using the Clustering Method".  Dinas Sosial Kota Palembang merupakan lembaga pemerintahan yang bertanggung jawab melaksanakan pembangunan di bidang kesejahtraan sosial, yang mencakup semua upaya program dan kegiataan yang ditunjukan untuk mewujudkan, membina dan memelihara, memulihkan dan mengembangkan kesejahtraan sosial yang dilaksanakan bersama sebagai tanggung jawab pemerintah dan masyarakat. Dalam melakukan pengolahan data untuk menentukan masyarakat mampu dan kurang mampu sebagai penerima Kartu Indonesia Sehat (KIS) dengan proses yang membutuhkan waktu yang cukup lama, mengingat banyaknya jumlah data penduduk yang akan diseleksi, khususnya diwilayah Kota Palembang. Maka dapat diangkat ke dalam penelitian yaitu “Implementasi Data Mining untuk Kartu Indonesia Sehat Bagi Masyarakat Kurang Mampu Menggunakan Metode Clustering”.

scholarly journals Simulasi dalam Optimalisasi Pengadaan Barang menggunakan Metode K-Mean Clustering

2021 ◽  
pp. 281-286
Author(s):  
Indah Savitri Hidayat ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Data Mining ◽  
Euclidean Distance ◽  
Clustering Method ◽  
Cluster Process ◽  
Minimum Value ◽  
Maximum Value ◽  
Cluster Calculations ◽  
The Future ◽  
Clustering Data ◽  
Cluster Values

Products provided by a store have an influence on store sales. Consumers will be attracted to stores that provide products according to their wants and needs. The purpose of this research is to find out what ornamental flower products are most in demand by consumers, in demand by consumers and less desirable to consumers. Keywords: inventory of goods, K-Mean Clustering, Data Mining, cluster, optimal. Store managers can get information about goods that have been depleted of inventory stock to be updated immediately. The method used in this study is the K-Mean Clustering method which belongs to one of the branches of Data Mining. The data used in the study is data from January 2020 to December 2020 as many as 100 pieces taken from naafilah official shop, Padang. The data variables used in the entry of goods are the year, product name, price and amount sold. Furthermore, the data is processed using Rapid Miner software. The first stage of processing is to determine the value of clusters randomly, in this study researchers divided the cluster values into 3 groups. Next, the centroid value of each group will be determined. Centroid is derived from the minimum value, middle value and maximum value of the data provided. Then, the cluster process is calculated using the euclidean distance formula. Cluster calculations are done by calculating the closest distance to the data.  The final result of this study is to find out the best-selling, best-selling and less-selling ornamental flowers, so that sellers can optimize the provision of ornamental flowers for the future.

scholarly journals Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method

2017 ◽  
Vol 1 (2) ◽  
pp. 26 ◽  
Author(s):  
Agus Perdana Windarto
Keyword(s):  
Data Mining ◽  
Agricultural Sector ◽  
Country Of Origin ◽  
Staple Food ◽  
Clustering Method ◽  
Total Import ◽  
Cross Border ◽  
Directorate General ◽  
Medium Level ◽  
Level Cluster

Indonesia is a country where most of its people rely on the agricultural sector as a livelihood. Indonesia's rice production is so high that it can not meet the needs of its population, consequently Indonesia still has to import rice from other food producing countries. One of the main causes is the enormous population. Statistics show that in the range of 230-237 million people, the staple food of all residents is rice so it is clear that the need for rice becomes very large. This study discusses the application of datamining on rice import by main country of origin using K-Means Clustering Method. Sources of data of this study were collected based on import import declaration documents produced by the Directorate General of Customs and Excise. In addition since 2015, import data also comes from PT. Pos Indonesia, records of other agencies at the border, and the results of cross-border maritime trade surveys. The data used in this study is the data of rice imports by country of origin from 2000-2015 consisting of 10 countries namely Vietnam, Thailand, China, India, Pakistan, United States, Taiwan, Singapore, Myanmar and Others. Variable used (1) total import of rice (net) and (2) import purchase value (CIF). The data will be processed by clustering rice imports by main country of origin in 3 clusters ie high imported cluster, medium imported cluster and low import level cluster. The clustering method used in this research is K-Means method. Cetroid data for high import level clusters 7429180 and 2735452,25, Cetroid data for medium import level clusters 1046359.5 and 337703.05 and Cetroid data for low import level clusters 185559.425 and 53089.225. The result is an assessment based on rice import index with 2 high imported cluster countries namely Vietnam and Thailand, 4 medium-level clusters of moderate import countries namely China, India, Pakistan and Lainya and 4 low imported cluster countries namely USA, Taiwan, Singapore and Myanmar. The results of the research can be used to determine the amount of rice imported by the main country of origin

scholarly journals Application of Fuzzy C-Means Clustering Method Using Matlab To Map the Potential of Rice Plant In Bekasi Regency

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Winarni Suwarso
Keyword(s):  
Data Mining ◽  
Rice Plant ◽  
Food Crops ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Rice Yields ◽  
Clustering Data ◽  
Fuzzy C Means Algorithm

Abstract Based on the data of rice crops from BPS-Statistics of Bekasi Regency in the field of Food Crops, there are several sub-districts in Bekasi Regency with varying rice yields. Therefore, it is necessary to group the sub-districts with the highest potential of rice producers. Therefore, a method is needed to facilitate the classification of paddy producing districts. By Fuzzy C-Means clustering method, the division of rice-producing sub-districts can be done based on the area of rice harvest (Ha) and rice production (ton). In this research, clustering of potential sub-districts using the Fuzzy C-Means algorithm is aimed at facilitating the grouping of a sub-district with the largest and low rice yields. The result is an illustration that shows the subdistrict grouping based on the results of paddy farming. Keywords: Clustering, Data Mining, Fuzzy C-Means Algorithm

scholarly journals IMPLEMENTASI DATA MINING UNTUK MENGETAHUI MANFAAT RPTRA MENGGUNAKAN METODE K-MEANS CLUSTERING

2019 ◽  
Vol 15 (1) ◽  
pp. 125-132
Author(s):  
Ditta Sri Wardiani ◽  
Nita Merlina
Keyword(s):  
Data Mining ◽  
Open Space ◽  
Provincial Government ◽  
Vacant Land ◽  
Clustering Method ◽  
The Public ◽  
Catchment Areas ◽  
Child Friendly ◽  
The Government ◽  
Water Catchment

The vacant land in DKI Jakarta is increasingly reducing due to the construction of houses, queues, factories, which are increasingly rapid. Making less green open space and water catchment areas. That way, the park of the children to play even decreases, as well as the DKI Jakarta Provincial Government, decides to make a Child-Friendly Integrated Public Room or what we know as RPTRA with facilities that can help the community around it. With this RPTRA, the public especially children can play and interact with each other. Using the K-Means Clustering method can help the government or officers in each RPTRA more easily see how useful this RPTRA and the government also facilitates some of the rooms contained in this RPTRA namely the hall, library, and playroom. The results of this research are the hall that has the highest value for visitors who come where the value obtained is 1319 visitors.

scholarly journals Penerapan K-Medoids Clustering Pada Ekspor Buah-Buahan Menurut Negara Tujuan

2021 ◽  
Vol 1 (1) ◽  
pp. 111-120
Author(s):  
Aris Saputri ◽  
Hidayatullah Hidayatullah ◽  
Ari Dermawan
Keyword(s):  
Data Mining ◽  
Computer Science ◽  
Research Data ◽  
Destination Country ◽  
The Government ◽  
Clustering Data ◽  
Export Destination ◽  
Central Statistics

Abstrack:The aim of the study is to group fruit exports according to the country of destination. The research data used came from the Indonesian Central Statistics Agency with the url https://www.bps.go.id/ for the category of fruit exports by destination country. The computer science technique used is to utilize K-Medoids clustering data lamination. The results of the study are expected to provide information to the government about the mapping results in the form of clusters of the destination countries for the number of fruit exports. This needs to be done to review the process of fruit exports to destination countries, bearing in mind the results of the export of these fruits have the potential to improve the Indonesian economy. Keywords:Data mining, Klastering, K-Medoids, Fruit Export, Destination Country.  Abstrak:Tujuan dari penelitian adalah untuk mengelompok kan ekspor buah-buahan menurut negara tujuan. Data penelitian yang digunakan berasal dari Badan Pusat Statistik Indonesia dengan kategori ekspor buah-buahan menurut negara tujuan. Teknik ilmu komputer yang digunakan adalah dengan memanfaat kan data mining klastering K-Medoids. Hasil penelitian diharapkan dapat memberikan informasi kepada pemerintah tentang hasil pemetaan berupa cluster terhadap Negara tujuan untuk jumlah ekspor buah-buahan. Hal ini perlu dilakukan untuk meninjau ulang proses ekspor buah buah kenegara tujuan mengingat hasil ekspor buah-buah tersebut berpotensi untuk meningkatkan perekonomian Indonesia. Kata Kunci:Data mining, Klastering, K-Medoids, Ekspor Buah, Negara Tujuan

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