scholarly journals Klasterisasi Kasus Kekerasan Terhadap Anak dan Perempuan Berdasarkan Algoritma K-Means

2021 ◽  
Vol 5 (2) ◽  
pp. 69-80
Author(s):  
Noviya Adawiyah ◽  
Nina Sulistiyowati ◽  
Mohamad Jajuli
Keyword(s):  
Data Mining ◽  
Violence Against Children ◽  
New Information ◽  
Computation Algorithm ◽  
The Government ◽  
Cluster 2 ◽  
High Cluster

Violence is action or threats against themselves alone, a group of people or community a group of people or community, loss psychologist, trauma, or deprivation of rights. District Karawang is on of the district that exist in the province of Jawa Barat. Violence that befell children and women in the area of Karawang bloom occurs, such as the lacj awareness of the victim to follow up cases that happened. The purpose of knowing the results of the cluster of cases of violence against children and women into three clusters are statterd in every sub-district in the District Karawang with category level of hardness low, medium or high in order that the government Karawang can provide treatment that is defferent and more targeted and focused on the results ot the analysis for each-each district. Data mining is the process of extracting data to obtain new information. In this study using CRIPS-DM methodology.Research is doing computation algorithm k-means clustering on the data of case of violence against children and women in 2016-2020. Results of testing using tools WEKA 3.8 earnded three cluster or the three categories of the level of violence that is cluster 0 there are 4 members who categorized the level of violence high, cluster 1 there are 2 members categorized the level of violence medium, and cluster 2 there are 24 members who categorized the level of violence low, the results of clustering is evaluated using equation testing purity measure, generate value purity 0,617, case that shows the cluster is quite good.

scholarly journals Pengklasteran Dokumen Tentang Dispensasi Nikah Menggunakan Algoritma K-Medoids

2019 ◽  
Vol 1 ◽  
pp. 677
Author(s):  
Selfia Ningsih ◽  
Suhada Suhada ◽  
Rafiqa Dewi ◽  
Agus Perdana Windarto
Keyword(s):  
Data Mining ◽  
Young Women ◽  
Research Method ◽  
Computer Method ◽  
The Government ◽  
High Cluster

Marriage dispensation is the marriage of a prospective bride or groom who is underage and has not been approved to marry according to the regulations. In fact there are still many young women who are married under the age of 20 years. This study aims to determine the marriage dispensation cluster, because there is no research on clustering marriage dispensation documents using a computer method to cluster any area that often conducts marriage dispensations with high and low clusters. The research method used is Data Mining with the K-Medoids algorithm. Based on calculations using the K-Medoids algorithm, high cluster results of 22 sub-districts and low clusters were obtained in 8 sub-districts. The results obtained from this study are expected to be input to the government through socialization activities in order to reduce the number of marriage dispensation in each region.

scholarly journals Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi

2020 ◽  
Vol 2 (2) ◽  
pp. 76-83
Author(s):  
Irmanita Nasution ◽  
Agus Perdana Windarto ◽  
M Fauzan
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Regional Growth ◽  
Data Mining Techniques ◽  
The Government ◽  
Different Characteristics ◽  
Existing Data ◽  
High Cluster

Proverty is one of the problems that inhibits national and regional growth. This research uses data mining techniques. In this study tha data used were sourced from the 2012-2018 statistical center. The research uses data mining techniques. In the data processing using k-means method. K-means method is a method of grouping existing data into several groups where the data in one group has the same characteristics with each other and has different characteristics from the data in other groups. The number of records used is 34 provinces which are divided into 2 clusters namely high and low clusters. The purpose of this study is divided into 2 parts, namely the provincial group with a high proverty rate and the provincial group with the lowest proverty level. From the result of grouping there were 8 provinces of high cluster and 26 low clusters. It is hoped that this research can provide input to the government so that it can give more attention to provinces that are categorized as high in proverty

IMPLEMENTASI RAPIDMINER DENGAN METODE K-MEANS (STUDY KASUS: IMUNISASI CAMPAK PADA BALITA BERDASARKAN PROVINSI)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Riyani Wulan Sari ◽  
Anjar Wanto ◽  
Agus Perdana Windarto
Keyword(s):  
Data Mining ◽  
Causes Of Death ◽  
Data Sources ◽  
Immunization Programs ◽  
Cluster Process ◽  
The World ◽  
The Government ◽  
Cluster Level ◽  
High Cluster

Measles is one of the causes of death in children around the world which always increases every year. Although measles immunization programs have been implemented, the incidence of measles in children is still quite high. This study discusses the Implementation of Rapidminer with the K-Means Method (Case Study: Measles Immunization in Toddlers by Province). The increase in cases of measles in toddlers in Indonesia is a case that has never been separated from the government's attention. Data sources and research were obtained from the Central Statistics Agency (BPS). The data used in this study are data from 2004-2017 which consists of 34 provinces. The cluster process is divided into 3 (three) clusters, namely high cluster level (C1), medium cluster level (C2) and low cluster level (C3). So that the assessment for cases of immunization against measles based on high cluster province (C1) is 21 provinces for medium cluster (C2) of 12 provinces and for low cluster (C3) of 1 province. The results of the cluster can be used as input for the government, especially the provinces, so that provinces that enter the high cluster receive more attention and increase the socialization of measles immunization against children under five. Keywords: Data Mining, Measles, Clustering, K-means

scholarly journals Penerapan Metode K-Means Pada Pengelompokkan Pengangguran Di Indonesia

2021 ◽  
Vol 6 (1) ◽  
pp. 61
Author(s):  
Fadhillah Azmi Tanjung ◽  
Agus Perdana Windarto ◽  
M Fauzan
Keyword(s):  
Data Mining ◽  
Labor Force ◽  
Research Data ◽  
Clustering Method ◽  
Employment Opportunities ◽  
Data Mining Techniques ◽  
Unemployment Data ◽  
The Government ◽  
Cluster 2 ◽  
Central Statistics

Unemployment is a group of labor force who has not done an activity that generates money. Someone who is said to be unemployed can also be categorized as people who have not worked, people who are looking for work, or people who have worked but have not gotten productive results. The purpose of this study is to analyze the unemployment stay by province in Indonesia. This research data is sourced from the Central Statistics Agency in 2014 - 2019. This study uses data mining techniques, namely the K-means algorithm, the K-means method is a clustering method that functions to break the dataset into groups. The K-means method can be used for percentage unemployment data by province. Data will be divided or grouped into 2 Clusters, where Cluster 1 is the group of provinces with the highest potential for unemployment with the results of 13 provinces and Cluster 2 is the province with the lowest potential unemployment results which is 21 provinces. The results of this study are as a way to assist the government in expanding employment to develop and improve the economy in each province in Indonesia. It is hoped that this research can provide input to the government. In particular, the provinces with minimal employment opportunities in Indonesia have an impact on unemployment

Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering

2020 ◽  
Vol 3 (3) ◽  
pp. 187-201
Author(s):  
Sufajar Butsianto ◽  
Nindi Tya Mayangwulan
Keyword(s):  
Data Mining ◽  
Clustering Data ◽  
Cluster 2

Penggunaan mobil di Indonesia setiap tahunnya selalu meningkat dan membuat perusahaan otomotif berlomba-lomba dalam peningkatan penjualannya. Tujuan dari penelitian ini untuk mengelompokan data penjualan kedalam sebuah cluster dengan metode Data Mining Algoritma K-Means Clustering. Data Penjualan nantinya akan dikelompokan berdasarkan kemiripan data tersebut sehingga data dengan karakteristik yang sama akan berada dalam satu cluster. Atribut yang digunakan adalah brand dan penjualan. Cluster yang terbentuk setelah dilakukan proses K-Means Clustering terbagi menjadi tiga cluster yaitu Cluster 0 jumlah anggota 235 dengan presentase 26% dikategorikan Laris, Cluster 1 jumlah anggota 604 dengan presentase 67% dikategorikan Kurang Laris, dan Cluster 2 jumlah angota 61 dengan presentase 7% dikategorikan Paling Laris, dari proses clustering diatas dapat diperoleh validasi DBI (Davies Bouldin Index) dengan nilai 0,341

Volatility Transmission in Indian Banking Sector

2019 ◽  
Vol 118 (7) ◽  
pp. 161-165
Author(s):  
Cyano Prem ◽  
Dr M. Babu ◽  
C. Hariharan ◽  
R. Muneeswaran
Keyword(s):  
Financial Markets ◽  
Banking Sector ◽  
Bad News ◽  
Private Bank ◽  
Volatility Transmission ◽  
The People ◽  
New Information ◽  
High Volatility ◽  
The Government ◽  
Awareness Programme

Any new information about the economy is transmitted fast and it may influence the financial markets, positively or negatively. The present study used GARCH (1, 1) and EGARCH models, to investigate the volatility of Indian banking sectors indices, namely, Nifty PSU Index and Nifty Private Bank Index of NSE India Ltd. The result of the study confirmed that the high volatility was found in both the bank indices. At the same time, negative information about Indian economics did affect the PSU and Private Bank Sector indices during the study period. Finally, the study concluded that bad news travels fast and it increased volatility more than good. Hence the Government should give more information and awareness programme to the people before the implementation of any economic policy.

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

IMPLEMENTASI ALGORITMA K-Modes UNTUK MENENTUKAN STRATEGI MARKETING STMIK BUDI DARMA

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ewin Karman Nduru ◽  
Efori Buulolo ◽  
Pristiwanto Pristiwanto
Keyword(s):  
High School ◽  
Data Mining ◽  
High School Students ◽  
Marketing Strategy ◽  
Process Data ◽  
School Students ◽  
The North ◽  
The Government ◽  
Using Data ◽  
North Sumatra

Universities or institutions that operate in North Sumatra are very many, therefore, of course, competition in accepting new students is very tight, universities or institutions do certain ways or steps to be able to compete with other campuses in gaining interest from community or high school students who will continue their studies to a higher level. STMIK BUDI DARMA Medan (College of Information and Computer Management), is the first computer high school in Medan which was established on March 1, 1996 and received approval from the government through the Minister of Education and Culture, on July 23, 1996 with operating license number 48 / D / O / 1996, in promoting the campus, the team usually formed a promotion team to various regions in the North Sumatra Region to provide information to the community. Students who have learned in this campus are quite a lot who come from various regions in North Sumatra, from this point the need to process data from students who are active in college to be processed using data mining to achieve a target, one method that can be used in data mining, namely the ¬K-Modes clustering (grouping) algorithm. This method is a grouping of student data that will be a help to campus students in promoting, using the K-Modes algorithm is expected to help and become a reference for marketing in determining the marketing strategy STMIK Budi Darma MedanKeywords: STMIK Budi Darma, Marketing Strategy, K-Modes Algorithm.

Children change their answers in response to neutral follow-up questions by a knowledgeable asker

2019 ◽  
Author(s):  
Elizabeth Bonawitz ◽  
Patrick Shafto ◽  
Yue Yu ◽  
Sophie Elizabeth Colby Bridgers ◽  
Aaron Gonzalez
Keyword(s):  
Social Learning ◽  
Belief Revision ◽  
Correct Answer ◽  
New Information ◽  
Common Practices ◽  
Parent Child

Burgeoning evidence suggests that when children observe data, they use knowledge of the demonstrator’s intent to augment learning. We propose that the effects of social learning may go beyond cases where children observe data, to cases where they receive no new information at all. We present a model of how simply asking a question a second time may lead to belief revision, when the questioner is expected to know the correct answer. We provide an analysis of the CHILDES corpus to show that these neutral follow-up questions are used in parent-child conversations. We then present three experiments investigating 4- and 5-year-old children’s reactions to neutral follow-up questions posed by ignorant or knowledgeable questioners. Children were more likely to change their answers in response to a neutral follow-up question from a knowledgeable questioner than an ignorant one. We discuss the implications of these results in the context of common practices in legal, educational, and experimental psychological settings.

Rancang Bangun Sistem Informasi Untuk Menentukan Kapabilitas Konsumen Dalam Mengambil Pinjaman KPR

2016 ◽  
Vol 7 (2) ◽  
pp. 75-80
Author(s):  
Adhi Kusnadi ◽  
Risyad Ananda Putra
Keyword(s):  
Data Mining ◽  
Low Income ◽  
Cross Validation ◽  
Classification Tree ◽  
Large Population ◽  
Housing Development ◽  
Good Precision ◽  
Index Terms ◽  
The Government ◽  
Fold Cross Validation

Indonesia is one country that has a relatively large population . The government in the period of 5 years, annually hold a procurement program 1 million FLPP house units. This program is held in an effort to provide a decent home for low income people. FLPP housing development requires good precision and speed of development on the part of the developer, this is often hampered by the bank process, because it is difficult to predict the results and speed of data processing in the bank. Knowing the ability of consumers to get subsidized credit, has many advantages, among others, developers can plan a better cash flow, and developers can replace consumers who will be rejected before entering the bank process. For that reason built a system that can help developers. There are many methods that can be used to create this application. One of them is data mining with Classification tree. The results of 10-fold-cross-validation applications have an accuracy of 92%. Index Terms-Data Mining, Classification Tree, Housing, FLPP, 10-fold-cross Validation, Consumer Capability

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