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 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 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 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 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 Spatial analysis of training alumni distribution to support competence development in Meteorology, Climatology, and Geophysics Agency

2021 ◽  
Vol 3 (1) ◽  
pp. 294-307
Author(s):  
Arisman Arisman
Keyword(s):  
Spatial Analysis ◽  
Participation Rate ◽  
Basic Training ◽  
Competency Development ◽  
Gis Application ◽  
Central Java ◽  
East Kalimantan ◽  
South Sulawesi ◽  
Administrative Area ◽  
Branch Office

This study aims to determine the distribution pattern of training alumni and their achievements held by BMKG Training Center during the 2015-2019 period in Indonesia as input to increase competency development strategies related to employee obligations in obtaining competency development of 20 lesson hours/person / year. The theoretical approach used is to map the training alumni based on the position in which they are on duty when participating in the training based on their administrative area. The method used in this research is a quantitative method with a descriptive statistical analysis approach and spatial analysis. The data processed is training alumni of technical, functional, leadership, and CPNS basic training. Training alumni data are processed and analyzed spatially based on Geographic Information Systems using the Quantum GIS application and Map visualization using ArcGIS application. The results of this study indicate that the spatial pattern of alumni distribution is not evenly distributed. The participation rate of training alumni is still around 21% and has not been able to reach all BMKG employees. Achievement of academic hours per person per year is in the range of 29 lesson hours. The results of the Spatial Analysis shows the BMKG branch office needs to get more opportunities in improving employee competency in 17 provinces spread across Aceh, Riau Islands, Jambi, Banten, West Java, DKI, Central Java, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, Bali, South Sulawesi, West Sulawesi, NTT, Maluku and Papua.

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 DAMPAK KETERKAITAN EKONOMI PULAU SULAWESI, JAWA TIMUR DAN KALIMANTAN TIMUR TERHADAP EKONOMI WILAYAH

2016 ◽  
Vol 7 (1) ◽  
pp. 97 ◽  
Author(s):  
. Arman ◽  
Setia Hadi ◽  
Noer Azam Achsani ◽  
Akhmad Fauzi
Keyword(s):  
Spillover Effect ◽  
East Kalimantan ◽  
North Sulawesi ◽  
South Sulawesi ◽  
Small Influence ◽  
The Impact ◽  
Central Sulawesi ◽  
Better Than ◽  
Economic Linkages

This study analyzed the effects of the economic linkages between regions Other Sulawesi, South Sulawesi, East Java and East Kalimantan. North Sulawesi, Central Sulawesi, Southeast Sulawesi and Gorontalo aggregated into one unit area of Sulawesi Other. South Sulawesi and West Sulawesi aggregated into a single unit into a region of South Sulawesi. Combined with consideration of South Sulawesi, West Sulawesi because in 2005 both areas are still joined in a single administration. Basic Data 2005 in upgrade to the Year 2011 by using the technique of RAS. The estimated number of sectors as many as 35 sectors. The study analysis showed patterns of economic linkages Other Sulawesi region is relatively lower than other regions. The pattern of economic linkages in South Sulawesi region is relatively better than Other Sulawesi. Role of East Java's economy is very large compared to other regions. The pattern of East Kalimantan's economy is relatively good, but more influenced by oil mining sector. The impact of economic linkages between regions showed Sulawesi region Another economic impact to the region of East Java and East Kalimantan but very little significance to the region of South Sulawesi. Other Sulawesi region provide spillover effect to East Java and East Kalimantan but very little influence to South Sulawesi. The impact of economic linkages East Java provides a very small influence other regions. The impact of economic linkage East Kalimantan region give greater influence to the East Java region than to Other Sulawesi and South Sulawesi region

scholarly journals Efek Investasi Terhadap Pertumbuhan Ekonomi di Pulau Jawa dan Pulau Sulawesi

2020 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
Muh Jamil
Keyword(s):  
Economic Growth ◽  
Structural Equation ◽  
Secondary Data ◽  
Growth Index ◽  
Equation Model ◽  
Central Java ◽  
North Sulawesi ◽  
South Sulawesi ◽  
Central Sulawesi ◽  
Very High

This research aimed to analyze Efect Of Investment to economic growth in Java island and Sulawesi island in 2006-2013. The research used the secondary data, time series and cross section of the eight provinces, namely Jakarta, West Java, Central Java, East Java, North Sulawesi, Central Sulawesi, South Sulawesi and Southeast Sulawesi. The data used comprised the investment index and economic growth index. The data were then Analyzed using Structural Equation Model (SEM) processed using Amos and SPSS econometric software. The results showed that the effect of investment on positive economic growth was significant on Java and positively insignificant on Sulawesi Island. That means that each increase in investment by one percent increases economic growth by 0.479 percent on Java and on Sulawesi Island investment has no effect on economic growth. The investment spread in Java is more stable from year to year and from region to region. Different things on the island of Sulawesi, investment is not stable, sometimes very high and sometimes also very low in other years

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.

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