scholarly journals ANALISA METODE DATA MINING PADA PENGELOMPOKAN LAPANGAN KERJA INFORMAL SEKTOR NON-PERTANIAN

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Khairunnissa Fanny Irnanda ◽  
Agus Perdana Windarto ◽  
Dedy Hartama ◽  
Anjar Wanto
Keyword(s):  
Data Mining ◽  
Human Resources ◽  
Informal Sector ◽  
Data Sources ◽  
Informal Employment ◽  
The Government ◽  
Central Statistics

The objective of the study is to classify informal employment in non-agricultural sectors. Data sources are obtained from the Central Statistics Agency (BPS). The data used is the proportion of employment for informal non-agricultural sectors (2015-2018), consisting of 34 Provinces in Indonesia. The Method used to solve the problem is datamining technique K-Medoid. The results of the research showed that the percentage of informal employment of non-agricultural sectors based on the lowest region became a record for the government to further increase human resources and more open the field jobs in non-agricultural sectors, among others.Keywords: Informal sector, Datamining, K-Medoid, Clustering, Non-Agricultural

Data Visualization Indicator Disease (Malaria, Dengue Fever, and Measles) in The Year 2012-2015

2017 ◽  
Vol 4 (2) ◽  
pp. 87-93
Author(s):  
Immanuel Luigi Da Gusta ◽  
Johan Setiawan
Keyword(s):  
Data Mining ◽  
Human Resources ◽  
Data Visualization ◽  
Health Sector ◽  
Medical Personnel ◽  
Database Systems ◽  
Process Time ◽  
Acceptance Test ◽  
Visual Data Mining ◽  
The Government

The aim of this paper are: to create a data visualization that can assist the Government in evaluating the return on the development of health facilities in the region and province area in term of human resources for medical personnel, to help community knowing the amount of distribution of hospitals with medical personnel in the regional area and to map disease indicator in Indonesia. The issue of tackling health is still a major problem that is not resolved by the Government of Indonesia. There are three big things that become problems in the health sector in Indonesia: infrastructure has not been evenly distributed and less adequate, the lack of human resources professional health workforce, there is still a high number of deaths in the outbreak of infectious diseases. Data for the research are taken from BPS, in total 10,600 records after the Extract, Transform and Loading process. Time needed to convert several publications from PDF, to convert to CSV and then to MS Excel 3 weeks. The method used is Eight-step Data Visualization and Data Mining methodology. Tableau is chosen as a tool to create the data visualization because it can combine each dasboard inside a story interactive, easier for the user to analyze the data. The result is a story with 3 dashboards that can fulfill the requirement from BPS staff and has been tested with a satisfied result in the UAT (User Acceptance Test). Index Terms—Dashboard, data visualization, disease, malaria, Tableau REFERENCES [1] S. Arianto, Understanding of learning and others, 2008. [2] Rainer; Turban, Introduction to Information Systems, Danvers: John Wiley & Sons, Inc, 2007. [3] V. Friedman, Data Visualization Infographics, Monday Inspirition, 2008. [4] D. A. Keim, "Information Visualization and Visual Data Mining," IEEE Transactions on Visualization and Computer Graphics 8.1, pp. 1-8, 2002. [5] Connolly and Begg, Database Systems, Boston: Pearson Education, Inc, 2010. [6] E. Hariyanti, "Pengembangan Metodologi Pembangunan Information Dashboard Untuk Monitoring kinerja Organisasi," Konferensi dan Temu Nasional Teknologi Informasi dan Komunikasi untuk Indonesia, p. 1, 2008. [7] S. Darudiato, "Perancangan Data Warehouse Penjualan Untuk Mendukung Kebutuhan Informasi Eksekutif Cemerlang Skin Care," Seminar Nasional Informatika 2010, pp. E-353, 2010.

scholarly journals PENGELOMPOKAN PERSENTASE BUTA HURUF UMUR 15-44 MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS

2020 ◽  
Vol 7 (3) ◽  
pp. 230
Author(s):  
Saifullah Saifullah ◽  
Nani Hidayati
Keyword(s):  
Data Mining ◽  
Human Resources ◽  
Data Clustering ◽  
Large Scale ◽  
Market Basket ◽  
Large Scale Data ◽  
The Government ◽  
Large Scale Data Processing ◽  
Scale Data

<p><em>Data Mining is a method that is often needed in large-scale data processing, so data mining has important access to the fields of life including industry, finance, weather, science and technology. In data mining techniques there are methods that can be used, namely classification, clustering, regression, variable selection, and market basket analysis. Illiteracy is one of the factors that hinder the quality of human resources. One of the basic things that must be fulfilled to improve the quality of human resources is the eradication of illiteracy among the community. The purpose of this study is to determine the clustering of illiterate communities based on provinces in Indonesia. The results of the study are illiterate data clustering according to the age proportion of 15-44 namely 1 high group node, low group has 27 nodes, and medium group 6 nodes. The results of this study become input for the government to determine illiteracy eradication policies in Indonesia based on provinces.</em></p><p><strong>Kata Kunci</strong>: <em>Illiterate</em><em>, Data mining, K-Means Clustering</em></p><p><em>Data Mining termasuk metode yang sering dibutuhkan dalam pengolahan data berskala besar, maka data mining mempunyai akses penting pada bidang kehidupan diantaranya yaitu bidang industri, bidang keuangan, cuaca, ilmu dan teknologi. Pada teknik data mining terdapat metode-metode yang dapat digunakan yaitu klasifikasi, clustering, regresi, seleksi variabel, dan market basket analisis. Buta huruf merupakan salah satu faktor yang menghambat kualitas sumber daya manusia. Salah satu hal mendasar yang harus dipenuhi untuk meningkatkan kualitas sumber daya manusia adalah pemberantasan buta huruf di kalangan masyarakat</em><em> </em><em>Adapun tujuan penelitian ini adalah menetukan clustering masyarakat buta huruf</em><em> berdasarkan propinsi di Indonesia</em><em>.</em><em> </em><em>Hasil dari penelitian adalah data clustering buta huruf menurut propisi umur 15-44 yaitu</em><em> 1 node</em><em> kelompok tinggi</em><em>,  kelompok rendah memiliki 27 node</em><em>, dan kelompok  sedang  6 node. Ha</em><em>sil penelitian ini menjadi bahan masukan kepada pemerintah untuk menentukan kebijakan</em><em> </em><em>pemberantasan buta huruf di Indonesia berdasarakn propinsi</em><em>.</em></p><p><strong>Kata Kunci</strong>: Buta Huruf, Data mining, <em>K-Means Clustering</em><em></em></p>

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 REVEALING THE SUCCESS OF RENITA ENCENG GONDOK IN MAINTAINING ITS BUSINESS EXISTENCE

2020 ◽  
Vol 4 (1) ◽  
pp. 61
Author(s):  
Eka Wahyu Liana ◽  
Rr. Lulus Prapti Nugroho S.S ◽  
Dian Triyani
Keyword(s):  
Human Resources ◽  
Data Sources ◽  
Observation Data ◽  
Material Quality ◽  
Production Section ◽  
The Government ◽  
Paper Towels ◽  
The Way

<p><em>This research was conducted at the Renita Eceng Gondok SME, Demakan, Banyubiru, Ambarawa, Semarang in the marketing and production section. The aim of this study is to explore the success of the Renita Eceng SME business in maintaining its existence.</em><em> This study is a case study with triangulation of data sources, observation data, interviews, documentation, and narratives. Informant study is based on the appropriate principle and adequacy. There are 6 basic informant principles in this study. The results of this study indicate that the strategy of Renita Eceng Gondok in achieving success and maintaining its existence is paying attention on material quality and improve the skill of their employee (human resources). The way of Renita Enceng Gondok explores creative ideas in creating variety of superior Enceng Gondok handycrafts products is by looking for patterns from internet then they developed the pattern based on the instruction from the owner of Renita Eceng Gondok. These ideas were turn into shoes, sandals, paper towels, jars, trash bins, frame, miniatures, and glass for hotel construction, laundry baskets, and others. The government supports this SME by facilitating exhibitions, training in management, marketing, equipments, etc. </em></p>

scholarly journals Recruitment, Selection, and Retention of Human Resources in the Implementation of the National Social Economic (Susenas) Sensus Program in the Statistic Center of the Lubuklinggau City

2020 ◽  
Vol 2 (2) ◽  
pp. 01-08
Author(s):  
Utami Riani Putri ◽  
Syamsir
Keyword(s):  
Human Resources ◽  
Swot Analysis ◽  
National Development ◽  
The People ◽  
Presidential Decree ◽  
Economic Census ◽  
The Government ◽  
Main Support ◽  
The City ◽  
Central Statistics

This research is motivated by the process of recruitment, selection, and retention of human resources in implementing the SUSENAS program at the Central Statistics Agency of the City of Lubuklinggau. The purpose of this study was to determine the process of implementing the SUSENAS program through recruitment, selection, and retention at the Lubuklinggau Central Statistics Agency. This study uses qualitative methods, the informants in this study are BPS employees of Lubuklinggau City, field officers, and the people of Lubuklinggau City, so that the sample obtained in this study amounted to 13 respondents. Data collection techniques in this study are documentation techniques and interviews. The data analysis technique used is the SWOT analysis. The results show that development achievements are the main data source for policymakers in planning national development, one of which is through the National Socio-Economic Survey (Susenas) which is the main support for meeting the needs of the government in implementing national development in line with the National Medium-Term Development Plan, to planning, monitoring, and evaluating, as well as measuring the accountability of development and public welfare, namely through the Central Statistics Agency (BPS), is a Non-Departmental Government Institution in Indonesia that has duties based on Presidential Decree No. 103/2001 concerning Position, Duties, Functions, Authority, Organizational Structure, and Work Procedure of non-Departmental Government Institutions, carry out government duties in the field of statistical activities in accordance with the provisions of the applicable laws and regulations. in the process of recruitment, selection, and retention on the implementation of the national socio-economic program in the Lubuklinggau municipal statistics body, the writer can conclude that the Lubuklinggau City Central Statistics Agency seeks qualified, disciplined, and responsible field officers so that the resulting data is of high quality. Constraints encountered in implementing the national socio-economic census program (SUSENAS) on recruitment, selection, and retention of human resources at the Lubuklinggau municipal statistical body are fluctuations where prospective officers who do not meet the needs of the public interest who are less interested in becoming field officers due to lack of promotion from the field of statistics. Central Agency for Statistics of the City of Lubuklinggau.)

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 K-Medoids Clustering Pada Ekspor Buah-Buahan Menurut Negara Tujuan

2021 ◽  
Vol 1 (1) ◽  
pp. 111-120
Author(s):  
Aris Saputri ◽  
Hidayatullah Hidayatullah ◽  
Ari Dermawan
Keyword(s):  
Data Mining ◽  
Computer Science ◽  
Research Data ◽  
Destination Country ◽  
The Government ◽  
Clustering Data ◽  
Export Destination ◽  
Central Statistics

Abstrack:The aim of the study is to group fruit exports according to the country of destination. The research data used came from the Indonesian Central Statistics Agency with the url https://www.bps.go.id/ for the category of fruit exports by destination country. The computer science technique used is to utilize K-Medoids clustering data lamination. The results of the study are expected to provide information to the government about the mapping results in the form of clusters of the destination countries for the number of fruit exports. This needs to be done to review the process of fruit exports to destination countries, bearing in mind the results of the export of these fruits have the potential to improve the Indonesian economy. Keywords:Data mining, Klastering, K-Medoids, Fruit Export, Destination Country.  Abstrak:Tujuan dari penelitian adalah untuk mengelompok kan ekspor buah-buahan menurut negara tujuan. Data penelitian yang digunakan berasal dari Badan Pusat Statistik Indonesia dengan kategori ekspor buah-buahan menurut negara tujuan. Teknik ilmu komputer yang digunakan adalah dengan memanfaat kan data mining klastering K-Medoids. Hasil penelitian diharapkan dapat memberikan informasi kepada pemerintah tentang hasil pemetaan berupa cluster terhadap Negara tujuan untuk jumlah ekspor buah-buahan. Hal ini perlu dilakukan untuk meninjau ulang proses ekspor buah buah kenegara tujuan mengingat hasil ekspor buah-buah tersebut berpotensi untuk meningkatkan perekonomian Indonesia. Kata Kunci:Data mining, Klastering, K-Medoids, Ekspor Buah, Negara Tujuan

scholarly journals From Person-Days Lost to Labour Militancy

2007 ◽  
Vol 62 (1) ◽  
pp. 31-65 ◽  
Author(s):  
Linda Briskin
Keyword(s):  
Human Resources ◽  
Statistical Data ◽  
Point Of View ◽  
Data Sources ◽  
The Political ◽  
Micro Data ◽  
Data Presentation ◽  
Employer Perspective ◽  
The Government ◽  
Work Stoppages

Using the micro-data from Human Resources and Social Development Canada (HRSDC) on the 23,944 stoppages in Canada between 1960 and 2004, this article introduces a labour militancy perspective on work stoppages, that is, from the point of view of workers. It explores patterns of militancy with a focus on strike duration, strike size and strikes for first contracts, and supports re-interpretations which help make visible the significance of such stoppages for workers, unions and communities. A labour militancy frame presents an alternative to the employer perspective on time lost, the government concern to measure the economic impact of stoppages, and the scholarly emphasis on strike determinants. As part of re-examining the HRSDC work stoppage data from a labour militancy perspective, the paper considers the source of these data. It juxtaposes the statistical data with interviews with the provincial correspondents who collect the information for HRSDC. Examining the data in this light underscores the political nature of data collection (what is seen to be germane and not), data presentation (what is made visible and what is not), and data sources (whose voices are heard).

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 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

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