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 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 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

PENERAPAN CLUSTERING PADA PENDUDUK YANG MEMPUNYAI KELUHAN KESEHATAN DENGAN DATAMINING K-MEANS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Nurul Rofiqo ◽  
Agus Perdana Windarto ◽  
Dedy Hartama
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Population Level ◽  
Research Data ◽  
Health Complaints ◽  
Public Health Services ◽  
National Statistics ◽  
Central Java ◽  
Indonesian Population ◽  
The Government

This study aims to utilize Clushtering Algorithm in grouping the number of people who have health complaints with the K-means algorithm in Indonesia. The source of this research data was collected based on the documents of the provincial population which had health complaints produced by the National Statistics Agency. The data used in this study are data from 2013-2017 consisting of 34 provinces. The method used in this research is K-means Algorithm. Data will be processed by clushtering in 3 clushter, namely clusther high health complaints, clusther moderate and low health complaints. Centroid data for high population level clusters 37.48, Centroid data for moderate population level clusters 27.08, and Centroid data for low population level clusters 14.89. So that obtained an assessment based on the population index that has health complaints with 7 provinces of high health complaints, namely Central Java, Yogyakarta, Bali, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, Gorontalo, 18 provinces of moderate health complaints, and 9 other provinces including low health complaints. This can be an input to the government to give more attention to residents in each region who have high health complaints through improving public health services so that the Indonesian population becomes healthier without health complaints.Keywords: data mining, health complaints, clustering, K-means, Indonesian residents

scholarly journals Indonesia’s coffee and cocoa agribusiness opportunities in Regional Comprehensive Economic Partnership trade cooperation

2021 ◽  
Vol 892 (1) ◽  
pp. 012071
Author(s):  
S K Dermoredjo ◽  
S M Pasaribu ◽  
D H Azahari ◽  
E S Yusuf
Keyword(s):  
Rural Areas ◽  
Economic Community ◽  
High Quality ◽  
South Sulawesi ◽  
Economic Partnership ◽  
The Government ◽  
Asean Economic Community ◽  
Regional Comprehensive Economic Partnership ◽  
North Sumatra

Abstract Following the agreement of the ASEAN Economic Community (AEC) in 2015, it has been approved that cooperation between ASEAN and the other five partner countries, namely China, Japan, Australia, New Zealand, and Korea, has been bound in new economic partnerships, Regional Comprehensive Economic Partnership (RCEP). The main objective of RCEP is to empower economic integrity and enhance the economic development of respective member countries. Coffee and cocoa are two of Indonesia’s important estate commodities for exports. A study focusing on coffee and cocoa agribusiness development to take advantage of RCEP was conducted in several production centers of rural areas. This paper aims to analyze the role of coffee and cocoa business in RCEP trade cooperation by: (i) understanding and analyzing trade on RCEP using RCA and RO, (ii) reviewing trade development in RCEP toward Indonesia’s coffee and cocoa performance using Gereffi analysis. This study was conducted in North Sumatra and South Sulawesi provinces. The study revealed an opportunity for coffee and cocoa to increase their market in the RCEP region but only with its high quality. Coffee and cocoa should be well prepared with significant, integrative, and comprehensive improvement. The government is suggested increasing the production and productivity of coffee and cocoa through intensive extension and closely working with the farmers.

scholarly journals Penerapan Data Mining Dalam Mengelompokkanm Jumlah Usaha Berdasarkan Provinsi Menggunakan K-Means Clustering

2019 ◽  
Vol 1 ◽  
pp. 937
Author(s):  
Rahel Adelina Hutasoit ◽  
M. Safii ◽  
Iin Parlina
Keyword(s):  
Data Mining ◽  
Business Performance ◽  
Industrial Revolution ◽  
Assessment Process ◽  
Business World ◽  
New Era ◽  
Central Java ◽  
The Government ◽  
Medium Enterprises ◽  
North Sumatra

Industrial revolution 4.0 is an era where technology in the IT field is growing very rapidly, it can also be said a new era for entrepreneurs in Indonesia that must be a golden opportunity to improve business performance and opportunities for millennials to enter the business or business world. In this era people are expected to be able to compete especially in the business world. This study discusses the Application of Data Mining in Grouping the Number of Enterprises by Province Using K-Means Clustering. The source of this research data is collected based on the information documents of the number of businesses in Indonesia produced by the National Statistics Agency. The data used in this study are provincial data consisting of 34 provinces. Data will be processed by clustering in 3 clusters, namely cluster of high number of businesses, cluster of medium number of businesses and cluster of low number of businesses. The results obtained from the assessment process are based on the index of the number of businesses with 4 provinces, the number of high businesses, namely North Sumatra, West Java, Central Java, and East Java, 13 Provinces, the number of medium enterprises and 17 other provinces, including low business numbers. This can be used as input to the community, especially in provinces where the number of businesses is low so they can compete in the business world and input for the government to provide facilities and infrastructure to support entrepreneurs in Indonesia.

scholarly journals DESCRIPTION AND MAPPING OF URBANIZATION DEVELOPMENT IN SUMATERA UTARA

2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Nurhanifah Nurhanifah
Keyword(s):  
Urban Areas ◽  
Rapid Development ◽  
Geographical Area ◽  
Research Data ◽  
Urban Residents ◽  
Toll Roads ◽  
Library Research ◽  
Urbanization Development ◽  
The Government ◽  
North Sumatra

The number of urban residents continues to increase each year due to the mobility of the population from villages to cities (urbanization). Aims this article  to describe the development of urbanization in North Sumatra. Method of research is library research, data collection is carried out by taking from various sources such as BPS data, journals, documentation, and coupled with observational data then analyzed descriptively. This research found  that the population development of North Sumatra (North Sumatra) is still concentrated in Medan City. Although its geographical area is only 0.36% of North Sumatra's area, Medan is inhabited by 2,279,894 million people. The concentration of the population of North Sumatra in Medan is due to the ongoing urbanization or migration of people from villages to cities. The high number of urban residents can be a problem for the government, one of which is the emergence of the phenomenon of poverty, homelessness, and congestion. Often people who move to urban areas have big reasons and expectations such as wanting to get a job, high wages, prestige, and wanting to enjoy urban facilities. The conclusion is the development of the urban population in North Sumatra is in line with the rapid development of infrastructure, such as toll roads, and industries built by the government or the private sector continue to grow. The increasing level of urbanization shows the increasing number of people living in urban areas (cities) which causes the population to become denser in urban areas. The large number of people who urbanize in North Sumatra is due to economic and social factors.Keywords: Urbanization, Mapping, Development, Nort Sumatra 

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 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 ANALISIS PENANGANAN KONFLIK ANTAR ORGANISASI KEMASYARAKATAN DI SUMATERA UTARA (MEDAN) DAN JAWA TENGAH (SURAKARTA)

Jurnal HAM ◽  
2016 ◽  
Vol 7 (1) ◽  
pp. 10
Author(s):  
Denny Zainuddin
Keyword(s):  
Social Order ◽  
Negative Impact ◽  
Legal Framework ◽  
Resource Mobilization ◽  
Central Java ◽  
Independence Movement ◽  
Fundamental Freedoms ◽  
History Of ◽  
The Government ◽  
North Sumatra

AbstrakOrganisasi Kemasyarakatan hadir, tumbuh dan berkembang sejalan dengan sejarah perkembangan bangsa. Dalam sejarah perjuangan kemerdekaan negara Republik Indonesia, Ormas merupakan wadah utama dalam pergerakan kemerdekaan, pada satu sisi, Ormas merupakan sebuah bentuk kebebasan fundamental yang dimiliki oleh setiap individu baik dalam kerangka etika maupun legal, yang dilindungi dan dijamin pelaksanaannya oleh negara. Namun pada sisi lain, pelaksanaan kebebasan fundamental tersebut justru ditengarai memiliki dampak negatif, yakni menabrak batas-batas keajegan dan ketertiban sosial masyarakat Indonesia.Penelitian ini melihat kebijakan pemerintah daerah dalam mengatasi konflik antar organisasi massa. Adapun pokok masalah ini diurai dalam beberapa pertanyaan, yaitu bagaimanakah dinamika konflik antar Ormas yang terjadi dan apa saja faktor penyebabnya, Kebijakan apa saja yang telah keluarkan oleh Pemerintah Daerah dalam rangka pengananan konflik antar Ormas, Bagaimana pengaruh kebijakan Pemda terhadap pengananan konflik antar Ormas.Penelitian ini dianallisis dengan menggunakan teori mobilisasi sumber daya dan analisis circle of conflict, untuk mendapatkan jawaban bagaimana Konflik Ormas yang terjadi di Sumatera Utara dan Jawa tengah (Solo) dan bagaimana penanganan konflik oleh Pemerintah di kedua lokasi tersebut.Penelitian ini menilai bahwa Pemda masih secara parsial menangani potensi konflik antar Ormas. Kebijakan yang ada masih bersifat administratif ketimbang sepenuhnya memberdayakan Ormas dalam mencapai tujuan bersama.Kata kunci: Pemerintah, konflik, OrmasAbstractCivil society organizations present, grow and develop in line with the historical development of the nation. In the history of the struggle for freedom in Indonesia, CSOs are the main container in the independence movement, mass is a form of the fundamental freedoms of every individual in both the ethical and legal framework, which is protected and guaranteed execution by the state. the implementation of the fundamental freedoms it is considered to have a negative impact, namely crashing boundaries and social order of Indonesian society.The research looked at government policy in resolving the conflict between CSOs. As this subject is broken down into several questions, namely how the dynamics of the conflict between CSOs happened and what are the causes, any policy that has been issued by the local government in order from administration of conflict between CSOs, How to influence the Government's policies from administration of conflicts among CSOs.This study in anallisis by using the theory of resource mobilization and the circle of conflict analysis, to get the answer to how conflicts CSOs that happened in North Sumatra and Central Java (Solo) and how to deal with conflict by the Government at both locations.The study assessed that the existing policy is still an administrative nature rather than fully empowering organizations to achieve common goals.Keywords: government, conflict, CBOs

Sign in / Sign up

Export Citation Format

Share Document