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 Analisis Algoritma Datamining pada Kasus Daerah Pelaku Kejahatan Pencurian Berdasarkan Provinsi

2020 ◽  
Vol 4 (1) ◽  
pp. 77
Author(s):  
Rinawati R ◽  
Erene Gernaria Sihombing ◽  
Linda Sari Dewi ◽  
Ester Arisawati
Keyword(s):  
Crime Rate ◽  
West Java ◽  
Central Java ◽  
South Sulawesi ◽  
West Sumatra ◽  
The Government ◽  
North Sumatra ◽  
Very High ◽  
Central Statistics

Theft is a behavior that causes harm to victims who are targeted and can cause victims. The level of theft behavior is increasing in each region due to the increasing number of unemployment and lazy nature of work that makes a person commit theft to make ends meet. The purpose of this study was to analyze using the technique of datamining in the area of perpetrators of theft crimes by province. The technique used is clustering with the K-means method. Data sourced from the Indonesian Central Statistics Agency with the url address: https://www.bps.go.id/. The results of the study using this technique are clustered in areas in Indonesia which have the highest crime theft rates. From the results of the study using the K-means technique, that there are 17 provinces out of 34 provinces that have the highest crime theft (C1) areas, namely: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Lampung, DKI Jakarta, West Java, Central Java, East Java, Banten, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, South Sulawesi, Papua. The results of the study are expected to be information for the government in conducting policies to reduce the crime crime rate in Indonesia which is very high (50%).

scholarly journals SISTEM IDENTIFIKASI PERSEBARAN PECEMARAN AIR OLEH LIMBAH DI INDONESIA MENGGUNAKAN AVERAGE LINKAGE DAN K-MEAN CLUSTER

2018 ◽  
Vol 1 (2) ◽  
pp. 36
Author(s):  
LALU MUTAWALLI ◽  
Mohammad Taufan Asri Zaen ◽  
Indi Febriana Suhriani
Keyword(s):  
Water Contamination ◽  
Population Distribution ◽  
West Kalimantan ◽  
The Third ◽  
Central Java ◽  
East Kalimantan ◽  
North Sulawesi ◽  
South Sulawesi ◽  
Average Linkage ◽  
West Sumatra

Water contamination is a problem that is always difficult to resolved. One of the main sources that causes water contamination is waste caused by human activities. The needed for a system that can analyze the data of water contamination sources. The main cause of water contamination that became variables in this study are family, factory and other waste. The method of Cluster and Average Linkage is used to analyze hierarchical data. The results of Cluster analysis hierarchically divided into three provincial groups based on the population distribution of waste. The first group is the Province of Nanggroe Aceh Darussalam, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Bangka Belitung, DKI Jakarta, DI Yogyakarta, East Java, Banten, Bali, NTB, NTT, East Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, West Papua and Papua. The second group consists of West Java and Central Java. In the third group occupied by West Kalimantan, Central Kalimantan and South Kalimantan. The source of water contamination, namely family waste, dominates the second group or it can be said that the province classified as the second group is dominated by family waste. The source of factory waste water contamination that dominates in the third group or it can be said that the provinces classified in the third group are dominated by factory waste as one of the most important sources of water contamination. The first group consisted of 28 members or 28 provinces, the second group had 2 members, while the third group consisted of 3 members. The first group has a source of water contamination, the most important of which is based on the indicators that are seen to be stable for the three indicators. The main source of water contamination based on the three indicators studied for the second group is dominated by family waste and other wastes. Whereas for the third group is dominated by factory waste.

scholarly journals Penerapan Metode K-Means Dalam Mengelompokkan Banyaknya Desa/ Kelurahan Menurut Keberadaan Permukiman Di Bantaran Sungai Berdasarkan Provinsi

2020 ◽  
Vol 2 (1) ◽  
pp. 49-56
Author(s):  
Indah Pratiwi M.S ◽  
Agus Perdana Windarto ◽  
Irfan Sudahri Damanik
Keyword(s):  
Research Data ◽  
West Kalimantan ◽  
Central Kalimantan ◽  
Central Java ◽  
South Sulawesi ◽  
The Government ◽  
Level Cluster ◽  
North Sumatra ◽  
High Cluster ◽  
River Banks

The research aims to classify the settlements along the river banks by province. To solve this problem, the researchers applied the K-Means Algorithm method. Where the source of research data was collected based on documents explaining the number of villages / sub-districts according to the existence of settlements on the river banks produced by the Central Statistics Agency (BPS). The data used in the study are data from 2014 - 2018 which consists of 34 provinces. The data will be processed by clustering in 2 clusters, namely the settlement level cluster on the high riverbank and the settlement level cluster on the low riverbank. The high cluster consists of 11 data, namely the provinces of Aceh, North Sumatra, Jambi, South Sumatra, West Java, Central Java, East Java, West Kalimantan, Central Kalimantan, South Kalimantan, and South Sulawesi. By conducting the research, it can provide input and as a solution to related parties in charge of dealing with settlement problems along the river banks, especially for the government, in order to get more attention in provinces with high riverbank settlement rates.

scholarly journals Pengelompokkan Tingkat Kriminalitas di Indonesia Menggunakan Algoritma Average Linkage

2021 ◽  
pp. 96-104
Author(s):  
Azman Azman ◽  
Anisa Anisa
Keyword(s):  
Criminal Cases ◽  
West Kalimantan ◽  
Central Java ◽  
Linkage Algorithm ◽  
East Kalimantan ◽  
North Sulawesi ◽  
Average Linkage ◽  
Cluster Area ◽  
West Sumatra ◽  
The Government

Crime needs to be analyzed and grouped so that the act does not cause harm either ecologically or psychologically. The statistical method that can be used to classify crime is the Average Linkage Algorithm. The study aims to group and analyze the characteristics of criminal cases in Indonesia. From the results of the analysis, 3 clusters were formed based on the average of each cluster. Cluster 1 consists of Aceh, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep. Bangka Belitung, Kep. Riau, West Java, Central Java, DI Yogyakarta, East Java, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, Maluku, North Maluku and Papua. Cluster 2 consists of North Sumatra while Cluster 3 consists of Metro Jaya. The grouping results are the basis of the government, apparatus, and the community in implementing the handling of criminal acts that occur in each cluster area so that prevention can minimize the losses caused by these crimes.

scholarly journals PEMANFAATAN DATAMINING PADA PENGELOMPOKAN PROVINSI TERHADAP PENCEMARAN LINGKUNGAN HIDUP

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Mawaddah Anjelita ◽  
Agus Perdana Windarto ◽  
Dedy Hartama
Keyword(s):  
Water Pollution ◽  
Clustering Algorithm ◽  
High Water ◽  
West Kalimantan ◽  
Central Java ◽  
East Kalimantan ◽  
North Sulawesi ◽  
West Sumatra ◽  
The Government ◽  
The Many

This research aims to provide input for the government so that it can immediately tackle water pollution given the many adverse effects that lurk in various aspects of life. The method used in this study researchers used the method of K-means clustering datamining algorithm. The data used in this study are the number of villages according to the type of environmental pollution in 2018 which consists of 34 provinces in Indonesia obtained through the official website of the Directorate of Statistics Indonesia. The variable used is water pollution. The variable used is water pollution. Data is grouped into 2 clusters, namely provinces that have high levels of water pollution (C1) and provinces that have low levels of water pollution (C2). K-Means Clustering algorithm in this study produces 2 iterations, so the final result is: high water pollution (C1) in the provinces of North Sumatra, West Java, Central Java, East Java, for low level water pollution (C2) is in provinces of Aceh, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep.Bangka Belitung, Kep.Riau, DKI Jakarta, DI Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, West Papua, Papua.Keywords:Datamining, Clustering, K-means , Water pollution

scholarly journals Spatial Analysis of Prevalence of Early Marriage and HDI in Indonesia

2021 ◽  
Vol 18 (1) ◽  
pp. 31-41
Author(s):  
Salsavira Salsavira ◽  
Jahra Afifah ◽  
Fiqih Tri Mahendra ◽  
Lathifah Dzakiyah
Keyword(s):  
Early Marriage ◽  
The Sustainable Development ◽  
South Sulawesi ◽  
West Sumatra ◽  
Development Goals ◽  
The Government ◽  
Age Of Marriage ◽  
Geographically Weighted Logistic Regression ◽  
North Sumatra ◽  
Central Sulawesi

Early marriage has become an important issue in Indonesia. Even though the rate of early marriage shows a decline until 2020, the number still makes Indonesia become the country with the second highest early marriage in Southeast Asia. Early marriage that occurs can hinder the achievement of the Sustainable Development Goals (SDG) and can have an impact on the Human Development Index. The existence of a relationship between early marriage and HDI encourages researchers to conduct studies that aimed at examining the effect of the prevalence of early marriage on HDI in each district/city in Indonesia on 2020. This study uses the Geographically Weighted Logistic Regression (GWLR) analysis method with the data sourced from the National Socio-Economic Survey (SUSENAS) raw data in March 2020 and publication data on the website of The Central Bureau of Statistics. The results of the analysis found that the prevalence of early marriage has a negative and significant effect in several districts/cities in the Provinces of Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, Riau Islands, West Java, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Maluku, and West Papua. This research is expected to be a recommendation for the government and community organizations to conduct socialization regarding the maturity age of marriage and the adverse effects that can be caused by early marriage.

scholarly journals Identifikasi Molekuler Hawar Daun Bakteri (Xanthomonas oryzae pv. oryzae) dan Uji Patogenisitasnya pada Galur-galur Padi Isogenik

2016 ◽  
Vol 9 (2) ◽  
pp. 49 ◽  
Author(s):  
Tasliah Tasliah ◽  
Mahrup Mahrup ◽  
Joko Prasetiyono
Keyword(s):  
Molecular Biology ◽  
Molecular Analysis ◽  
Xanthomonas Oryzae ◽  
Specific Primers ◽  
West Kalimantan ◽  
Central Java ◽  
South Sulawesi ◽  
Pcr Product ◽  
West Sumatra ◽  
Pathogenicity Tests

<p>Identification of Xanthomonas oryzae pv.<br />oryzae (Xoo) based on molecular analysis has been<br />introduced just few years ago. This method used some<br />specific primers for Xoo and can be done quickly. The<br />purposes of this research were to identify isolate Xoo<br />originated from five locations in Indonesia and to determine<br />the level of pathogenicity of these bacteria. Studies were<br />conducted in the greenhouse and the Molecular Biology<br />Laboratory of ICABIOGRAD, from 2011 to 2012. Bacterial<br />isolates were taken from five regions in Indonesia, namely:<br />West Sumatra, West Java, Central Java, South Sulawesi, and<br />West Kalimantan. The specific primers of Xoo were<br />Xoo2967, Xoo80, and Xoo. Results showed that 216 isolates<br />could be grown to form yellow colored colonies, which<br />belongs to a criterian for Xoo. Molecular analysis<br />demonstrated that 189 isolates were Xoo and 27 isolates<br />were not. Amplification of DNA of the isolates resulted a 337<br />bp PCR product for primer Xoo2976, 700 bp for primer<br />Xoo80 and 534 bp for primer Xoo. Pathogenicity tests of the<br />Xoo isolates showed xa5, Xa7, and Xa21 resistance genes<br />were still effective againts BLB pathogens originated from<br />those five regions, with percentage of resistance were 93.57,<br />77.49, and 85.37%, respectively.</p>

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

scholarly journals Implementation of Data Mining using the Clustering Method (Case: Region of the Actors of Theft Crime by Province)

2019 ◽  
Vol 2 (2) ◽  
pp. 75 ◽  
Author(s):  
Frinto Tambunan
Keyword(s):  
Data Mining ◽  
Crime Rate ◽  
Crime Rates ◽  
Clustering Method ◽  
Central Java ◽  
South Sulawesi ◽  
West Sumatra ◽  
Using Data ◽  
High Crime ◽  
North Sumatra

Theft is a behavior that causes harm to victims who are targeted and cause casualties. This study aims to classify areas of theft crimes based on provision by using data mining techniques. Data was obtained from the Indonesian statistical center (Badan Pusat Statistik) consisting of 34 provinces. The grouping technique used is K-Means. Clusters are divided into 3 namely: C1: areas with high crime rates of theft, C2: areas with crime rates of ordinary theft and C3: areas with low theft crime rates. Data processing is done using the help of RapidMiner software. The results of the k-means analysis obtained 17 provinces in Indonesia have the highest theft crime rate (C1), namely: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Lampung, DKI Jakarta, West Java, Central Java, East Java, Banten, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, South Sulawesi and Papua. The results of the study concluded that more than 50% of regions in Indonesia still had high rates of crime of theft.

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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