PENERAPAN K-MEDOID PADA RUMAH TANGGA YANG MEMILIKI SUMBER PENERANGAN LISTRIK PLN BERDASARKAN PROVINSI

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Cici Astria ◽  
Agus Perdana Windarto ◽  
Dedy Hartama
Keyword(s):  
Daily Life ◽  
West Java ◽  
Low Level ◽  
Household Data ◽  
Central Java ◽  
North Sulawesi ◽  
Data Source ◽  
High Level ◽  
Level Cluster ◽  
Cluster Level

Electricity has now become something that is very much needed in daily life, every household has used PLN electricity lighting sources. However, there are also some regions in Indonesia that have not been able to enjoy good and even electric lighting. The data source used from the Indonesian Statistics Agency website is the Percentage of Household Data by Province and Treatment, 2013-2014. This study aims to classify households that have a source of electricity for PLN using the datamining algorithm with K-Medoid. The data is processed into 2 clusters, namely high level clusters (C1) and low level clusters (C2). Where the results of this study concluded from 33 provinces in Indonesia that the cluster level of low waste sorting behavior (C1) obtained 11 provinces namely Aceh, Kep. Bangka Belitung, DKI Jakarta, West Java, Central Java, DI Yogyakarta, East Java, Banten, Bali, West Nusa Tenggara, and North Sulawesi and 23 other provinces are included in the low-level cluster (C2).Keywords: Electricity, Datamining, K-Medoid, Clustering, Household Sources

scholarly journals IMPLEMENTASI DATAMINING PADA KASUS TENAGA LISTRIK YANG DIBANGKITKAN BERDASARKAN PROVINSI

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Afrina Wati ◽  
Iin Indriani ◽  
Tira Sifrah Saragih Manihuruk ◽  
Sintya Sintya ◽  
Ivo Yohana Manurung ◽  
...  
Keyword(s):  
Electric Power ◽  
Power Plants ◽  
Electric Energy ◽  
Central Java ◽  
Data Source ◽  
The Government ◽  
High Level ◽  
Level Cluster ◽  
North Sumatra

Indonesia is one of the most vital electric energy users. The development of the world of technology and information in its use does not escape from access to electricity. This study discusses the Implementation of Datamining in the Case of Electric Power Generated by Province. The increasing need for electricity usage from time to time has never escaped the attention and auspices of the government. The data source in this study was accessed from the official website of the Indonesian government, namely the Central Statistics Agency (http://www.bps.go.id). The data used in this study are data from 2011-2017 which consists of 33 provinces in Indonesia. In the analysis of this study using 3 (three) cluster levels, namely the first high level cluster (C1), the second moderate level cluster (C2) and the third low level cluster (C3). So that the final results of the analysis of the case study of Electric Power Generating by Province obtained new data and information, namely the high cluster province of 2 provinces namely East Java and Banten, the medium cluster province of 4 provinces namely North Sumatra, South Sumatra, West Java and Central Java while low cluster provinces as much as 27 in other provinces. The results of the analysis of this study can be used as input for the government and the State Electricity Company (PLN), in order to make the province of the highest cluster category a top priority in increasing the growth of power plants as well as being more interactive in the utilization of electricity effectively and efficiently.Keywords: Data Mining, K-Means, Clustering, Energy, Electric Power, Province

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 The Phenomenon of Production, Distribution, and Consumption Serang Banten Typical Pottery Crafts in Bali

2016 ◽  
Vol 2 (11) ◽  
pp. 1
Author(s):  
I Wayan Mudra ◽  
Ni Made Rai Sunarini
Keyword(s):  
Qualitative Method ◽  
Snowball Sampling ◽  
West Java ◽  
Central Java ◽  
Production Distribution ◽  
Transportation Process ◽  
Distribution Process ◽  
Trade Center ◽  
Data Source

In the globalization era, Bali became the trade center at many crafts products from various regions in Indonesia, was included pottery products trading. The pottery products recently from outside Bali that was marketed in Bali, the production itself was done outside of Bali, merely the distribution and consumption in Bali, for instance, Lombok pottery, Kasongan Yogyakarta, Jepara of Central Java, West Java, and Serang Banten and others. The last few years, there was something different i.e. a typical pottery of Serang Banten, West Java, which was originally produced in West Java, is now produced in Bali, consequently, the distribution process from West Java to Bali was stopped. The research was intended to determine the factors that encourage the production, distribution, and consumption the craft of typical pottery Serang Banten in Bali. The research approached applied a qualitative method, based on the deconstruction and an ideology of capitalism theories. The research location was conducted at Denpasar Bali in 2015-2016. The technique of collecting the data included observation, interviewed, and documentation. The data source was determined by using purposive and snowball sampling. The results of the study were some factors that encouraged the production, distribution, and consumption of pottery Serang Banten in Bali was to avoid the loss of the transportation process; provided maximum services to consumers; Serang Banten pottery has a unique and potential marketing opportunities.

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 Varietas Non Hibrida Cabai Besar Anies IPB

2017 ◽  
Vol 1 (1) ◽  
pp. 56
Author(s):  
Muhamad Syukur ◽  
Sobir , ◽  
Siti Marwiyah ◽  
Awang Maharijaya ◽  
Anas D. Susila ◽  
...  
Keyword(s):  
Dynamic Stability ◽  
Block Design ◽  
Female Parent ◽  
West Java ◽  
Harvesting Time ◽  
Central Java ◽  
Randomized Complete Block Design ◽  
Two Factors ◽  
High Level ◽  
Selection Of

The pepper is very important horticultural commodities. The purpose of this research was to study the advantagesof Anies IPB varieties and develop the description. Anies IPB was the result of the selection of segregating populations from the crosses of IPB C120 (as the female parent) and IPB C5 (as the male parent). The experiments were performed in four locations namely Boyolali (Central Java), Sumedang (West Java), and Bogor (West Java). The experimental used the randomized complete block design (RCBD) two factors with three replications. The replications nested within the locations. The first factor was 9 lines and 2 open pollinated varieties, and the second factor was the three locations. Each lines on each replicate in each location were planted 24 plants. The results showed that the superiority of Anies IPB varieties were (1) The productivity of Anies IPB was higher than the check varieties. Productivity can reach 18.6 tons ha-1. (2) Anies IPB has fruit that was longer than the Tit Super and Trisula. (3) Anies IPB has early-harvesting time, ranged between 76.67 - 84.67 days after planting. (4) Anies IPB has a high level of stability and classified into the dynamic stability, which means that veryadaptive to the optimum environment.Keyword: non hybrid, pepper productivity superiority, variety

scholarly journals Penerapan Rapidminer Pada Data Mining Klastering (Kasus: Distribusi Persentase Rumah Tangga Menurut Kabupaten/Kota Dan Bahan Bakar Untuk Memasak)

2020 ◽  
Vol 1 (2) ◽  
pp. 52-58
Author(s):  
Ismi Azhami ◽  
Rahmi Fauziah
Keyword(s):  
Data Mining ◽  
Daily Life ◽  
Energy Sources ◽  
Cooking Fuel ◽  
Low Level ◽  
The North ◽  
The Government ◽  
High Level ◽  
North Sumatra ◽  
Central Statistics

Fuel is any material that can be converted into energy. For example in daily life humans often use energy sources as fuel for cooking including Gas/LPG, Electricity, Kerosene, Charcoal/Briquettes, Wood and others. The purpose of this study is to classify the distribution of the percentage of fuel used in each district/city in Northern Sumatra. This study discusses the analysis of the K-Means method in the case of the distribution of household percentages by district/city and cooking fuel in North Sumatra through the North Sumatra Central Statistics Agency website. The data is processed into 2 clusters namely high level (C1) and low-level clusters (C2). Thus obtained from 34 districts/cities in North Sumatra 23 regions are grouped in high-level clusters (C1) and 10 regions are grouped in low-level clusters (C2).This needs to be done so that it becomes input in the form of information to the government to find out villages that still have low understanding and have not been fulfilled in a district/city in the Province of North Sumatra.

scholarly journals PENERAPAN DATA MINING PADA JUMLAH PELANGGAN PERUSAHAAN AIR BERSIH MENURUT PROVINSI MENGGUNAKAN METODE K-MEANS CLUSTERING

2019 ◽  
Vol 2 (2) ◽  
pp. 119-125
Author(s):  
Lestari Sinaga ◽  
Abdullah Ahmad ◽  
Muhammad Safii
Keyword(s):  
Calculated Data ◽  
Clean Water ◽  
Low Level ◽  
Central Java ◽  
Medium Level ◽  
Centroid Point ◽  
Water Companies ◽  
The Right ◽  
High Level ◽  
Number Of Customers

Water is one of the primary needs for humans so that everyone has the right to get clean water for their daily needs. Along with increasing population, the need for water will increase. So with that the PDAM must sell clean / decent water to its customers, clean water becomes the focus of attention and has the greatest power compared to other problems. Because water is a basic necessity, most of the companies impose rates that can be reached by the community and prices are adjusted to the growth in demand. The purpose of this research is to get a grouping of the number of customers of clean water companies in all provinces using the K-Means Algorithm, K-Means is a method for grouping data into a cluster by calculating the closest distance from a data to a centroid point. Clusters used are high level clusters (C1), medium level clusters (C2), and for low level clusters (C3). Centroid data obtained is for high-level clusters (C1) which are as many as 7710154, for medium-level clusters as much as 929586, and for low-level clusters as much as 112462. Based on the calculated data obtained high-level results, namely the province of Indonesia, for the medium level namely province North Sumatra, DKI Jakarta, West Java, Central Java and East Java, and other provinces are low levels. So that this result can be a support for the company in order to increase water needs.

scholarly journals ANALISA METODE DATA MINING PADA PRODUKSI PERIKANAN LAUT YANG DIJUAL DI TEMPAT PERIKANAN IKAN (TPI)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Hanifah Urbach Sari ◽  
Agus Perdana Windarto ◽  
Dedy Hartama
Keyword(s):  
West Kalimantan ◽  
Central Java ◽  
North Sulawesi ◽  
South Sulawesi ◽  
Catchment Areas ◽  
West Sumatra ◽  
The Government ◽  
High Level ◽  
Water Catchment ◽  
North Sumatra

The purpose of this research is that the results of the utilization of fish resources in producing marine fisheries by fishermen can be good using the K-Means clustring method. Data was obtained from the Central Statistics Agency (BPS) and assisted using RapidMiner software. Data used from 2013-2017 consisted of 21 Provinces. With these data can be obtained data with high-level clusters (C1), namely Central Java with production 587002.8 and low-level clusters (C2) provinces of Aceh, North Sumatra, West Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, DKI Jakarta, West Java , DI Yogyakarta, East Java, Banten, Bali, West Nusa Tenggara, West Kalimantan, Central Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi and Gorontalo with a production of 20302.28. This can be input to the government for provinces that have low water catchment areas to be of more concern based on the cluster that has been done.Keywords: K-Means, Sea Fish Production, Clustering, Territory

scholarly journals Kemampuan Koneksi Matematika Siswa Dalam Menyelesaikan Soal Matematika Bentuk Cerita Ditinjau Dari Kemampuan Awal Matematika Siswa SMP Negeri 1 Majene

2018 ◽  
Vol 14 (1) ◽  
pp. 11
Author(s):  
Amran Yahya
Keyword(s):  
Daily Life ◽  
Ability Level ◽  
Math Ability ◽  
Low Level ◽  
Descriptive Research ◽  
Mathematical Connections ◽  
Mathematics Skills ◽  
Mathematics Ability ◽  
High Level ◽  
Mathematical Connection

The type of this research is descriptive research that aims to determine the ability of students mathematical connections to solve the problem of story form on Triangle and Triangle material in class VII of SMP Negeri 1 Majene based on students' early math ability. The subjects of the study were 6 students consisting of 2 students with a high level of early math ability, 2 students with moderate level of math ability, and 2 students with low level of early math ability. The result of the research shows that (1) students with high level of high mathematics ability have high mathematical connection ability, students with high ability level can solve problems and connect them with mathematics, science (other), and daily life well. But there are students who have little problem in solving the problem (2) students with the basic level of mathematics skills have medium math connection ability, students with ability level are able to understand the problem, but have difficulty in solving the problem and connect it with mathematics concept, fields), and daily life. (3) students with low level of early math ability have low mathematical connection ability, Students with low ability level have difficulty in understanding determine the elements of the problem so that they can not solve the problem and connect it with mathematics, science (other), and also everyday life.

PENERAPAN ALGORITMA K-MEANS UNTUK PENGELOMPOKAN DAERAH RAWAN BENCANA DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 1-10
Author(s):  
Amril Mutoi Siregar
Keyword(s):  
Natural Disasters ◽  
Accurate Data ◽  
Disaster Victims ◽  
Central Java ◽  
North Sulawesi ◽  
Natural Beauty ◽  
Or Groups ◽  
Disaster Data ◽  
The Government ◽  
Almost All

Indonesia is a country located in the equator, which has beautiful natural. It has a mountainous constellation, beaches and wider oceans than land, so that Indonesia has extraordinary natural beauty assets compared to other countries. Behind the beauty of natural it turns out that it has many potential natural disasters in almost all provinces in Indonesia, in the form of landslides, earthquakes, tsunamis, Mount Meletus and others. The problem is that the government must have accurate data to deal with disasters throughout the province, where disaster data can be in categories or groups of regions into very vulnerable, medium, and low disaster areas. It is often found when a disaster occurs, many found that the distribution of long-term assistance because the stock for disaster-prone areas is not well available. In the study, it will be proposed to group disaster-prone areas throughout the province in Indonesia using the k-means algorithm. The expected results can group all regions that are very prone to disasters. Thus, the results can be Province West java, central java very vulnerable categories, provinces Aceh, North Sumatera, West Sumatera, east Java and North Sulawesi in the medium category, provinces Bengkulu, Lampung, Riau Island, Babel, DIY, Bali, West Kalimantan, North Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, Papua, west Papua including of rare categories. With the results obtained in this study, the government can map disaster-prone areas as well as prepare emergency response assistance quickly. In order to reduce the death toll and it is important to improve the services of disaster victims. With accurate data can provide prompt and appropriate assistance for victims of natural disasters.

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