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

Data Mining

2011 ◽  
pp. 921-926
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
New Technologies ◽  
Research Area ◽  
Future Research ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Degree Of Confidence ◽  
Common Application ◽  
Existing Data

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Ragged, 1999). There is a wide range of data mining techniques, which has been successfully used in many applications. This article is an attempt to provide an overview of existing data mining applications. The article begins by explaining the key tasks that data mining can achieve. It then moves to discuss applications domains that data mining can support. The article identifies three common application domains, including bioinformatics, electronic commerce, and search engines. For each domain, how data mining can enhance the functions will be described. Subsequently, the limitations of current research will be addressed, followed by a discussion of directions for future research.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 217-235 ◽  
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 1274-1292
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

scholarly journals Crime Analysis and Prediction Using Data Mining and Machine Learning Techniques

2021 ◽  
Vol 9 (10) ◽  
pp. 1259-1270
Author(s):  
Jasmeet Kaur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Crime Rates ◽  
Crime Analysis ◽  
Crime Trends ◽  
Outlier Analysis ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
The Government

Abstract: With the increase in crime rates across the world, it has become important for the Government and crime handling agencies to control the situation as it has put every person in distress. This paper is an attempt to systematically analyze and identify the crime trends across the years, the inter-state relations based on crime rates and categories through the data available, which will help in predicting the crime trends in future and will be instrumental for the Government to take informed actions and improve the country’s situation. This paper applies various data mining techniques in order to analyze the crime records in India. The results of analysis have been compared for various algorithms in the domain of Association Rule Mining, Clustering, Outlier Analysis, Regression and Classification. The paper also attempts to predict the future occurrences of crimes using classification and regression algorithms which use data mining techniques . Keywords: Crime Analysis, Data Mining, Association Rule Mining, Clustering, outlier Analysis, Classification, Regression

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 Clustering dalam Mengelompokkan Jumlah Kunjungan Wisatawan Mancanegara Dengan Metode K-Means

2019 ◽  
Vol 1 ◽  
pp. 462
Author(s):  
Edy Satria ◽  
Heru Satria Tambunan ◽  
Ilham Syahputra Saragih ◽  
Irfan Sudahri Damanik ◽  
Fany Than Ervina Sitanggang
Keyword(s):  
Data Mining ◽  
Research Data ◽  
Tourist Attractions ◽  
Data Mining Techniques ◽  
Indonesian Government ◽  
National Statistics ◽  
Foreign Tourist ◽  
Tourism Sector ◽  
The Future ◽  
The Government

The Indonesian tourism sector currently contributes approximately 4% of the total economy. In 2019, the Indonesian Government wants to increase this figure to double to 8% of PDB (Produk Domestik Bruto), a target that implies that within the next 4 years, the number of visitors needs to be doubled to approximately 20 million tourists. This study discusses the Application of Clustering in Grouping the Number of Foreign Tourist Visits by Nationality and Month of Arrival by the K-Means Method. The source of this research data was collected based on data on the number of foreign tourist visits produced by the National Statistics Agency. K-Means clustering is one of the data mining techniques that gives a description of an item's cluster. The purpose of this study is to classify the number of foreign tourists in Indonesia. The results of this study are grouping the number of foreign tourist visits grouped by two clusters (high and low), high clusters of 4 countries and low clusters of 87 countries. Countries that are included in the lower clusters can be used for the Government of Indonesia in terms of improving existing facilities in tourist attractions so that visiting tourists will increase in the future.

scholarly journals PREDICTIVE MODELING OF TECHNOLOGICAL DEVELOPMENT TRENDS

2020 ◽  
Vol 55 ◽  
pp. 103-108
Author(s):  
Alla G. Kravets ◽  
◽  
Natalia A. Salnikova ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Predictive Modeling ◽  
Technological Development ◽  
Data Sources ◽  
Development Trends ◽  
Data Mining Techniques ◽  
The Neural Network ◽  
Existing Data

In the work, the problem of forecasting technological development trends was considered. A review of the sources of the global patent space, an analysis of technological development trends, a survey of data sources for training the neural network were carried out. Existing data mining techniques were analyzed for more accurate and faster forecasting. A module for predictive modeling of trends in technological development was developed, algorithms for the module for predictive modeling of trends in technological development were described.

scholarly journals VILLAGE GROUPING BASED ON THE NUMBER OF HEALTH FACILITIES IN WEST JAVA USING K-MEANS CLUSTERING ALGORITHM

2021 ◽  
Vol 4 (1) ◽  
pp. 71-78
Author(s):  
Frieyadie Frieyadie ◽  
Anggie Andriansyah ◽  
Tyas Setiyorini
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Clustering Algorithm ◽  
National Development ◽  
Health Facilities ◽  
West Java ◽  
The Future ◽  
The Government

Health is very important for the welfare and development of the Indonesian nation because as a capital for the implementation of national development, it is essentially the development of all Indonesian people and the development of all Indonesian people. Due to the outbreak of the Covid-19 virus, many health facilities must be provided for patients. Of course, the government must pay attention to the health facilities that can be used in every district/city in West Java in the future. Therefore, to determine the level of availability of sanitation facilities in each district/city in West Java, we need a technology that can classify data correctly. One method of data processing in data mining is clustering. The application of clustering to this problem can use the K-Means algorithm method to group the most frequently used data. The purpose of this study is to classify sanitation data on the highest sanitation facilities, medium sanitation facilities, and low sanitation facilities, so that areas/cities that are included in the low cluster will receive more attention from the government to improve/provide sanitation facilities.

scholarly journals Rekonsiliasi Data Keuangan dan Karakterisasi Satuan Kerja dengan Teknik Data Mining

2016 ◽  
Vol 1 (3) ◽  
pp. 49-68
Author(s):  
Dedy Elisa Limbong
Keyword(s):  
Data Mining ◽  
Central Government ◽  
Financial Statements ◽  
Tax Revenue ◽  
Accounting System ◽  
Work Unit ◽  
Data Mining Techniques ◽  
Classification Technique ◽  
Work Units ◽  
The Government

Preparation of Financial Statements of the Central Government is the responsibility of the government. The preparation of the report should be preceded by a reconciliation of financial data between the State General Treasurer (BUN) by KPPN with Work Unit. BPK findings on unmatches as results of reconciliation is a proof that there are still unmatched transactions recorded in the accounting system of  BUN and work units's. Direktorat Jenderal Perbendaharaan can reduce the possibility of unmatch records by performing supervision activities focused on work units that have been characterized. Data mining techniques can be used for characterization by utilizing a database on Sistem Perbendaharan dan Anggaran Negara (SPAN). This research use classification technique by setting two class that are SELISIH and OK. This research uses twenty attributes from work unit obtained through data mining techniques and the result shows work units with high number of SP2D and PNBP records (non-tax revenue) are work units tended to be unmatch work unit. Abstrak Penyusunan Laporan Keuangan Pemerintah Pusat merupakan tanggung jawab pemerintah. Penyusunan laporan tersebut harus didahului oleh proses rekonsiliasi data keuangan antara Bendahara Umum Negara (BUN) melalui KPPN dengan satuan kerja. Temuan BPK atas selisih hasil rekonsiliasi menjadi bukti bahwa masih terdapat selisih pencatatan akuntansi pada sistem BUN dan satuan kerja. Direktorat Jenderal Perbendaharaan (KPPN) dapat menekan kemungkinan terjadinya selisih rekonsiliasi dengan melakukan kegiatan supervisi yang terfokus pada satker-satker yang telah dikarakterisasi. Teknik data mining dapat digunakan untuk melakukan karakterisasi tersebut dengan memanfaatkan database pada Sistem Perbendaharaan Anggaran dan Negara (SPAN). Teknik data mining dilakukan dengan metode klasifikasi dengan menetapkan dua kelas yaitu kelas SELISIH dan OK. Penelitian ini menggunakan dua puluh atribut satuan kerja di mana melalui teknik data mining di mana hasil data mining menunjukkan bahwa satker dengan atribut Jumlah Surat Perintah Pencairan Dana (SP2D) dan Realisasi Penerimaan Negara Bukan Pajak (PNBP) yang tinggi merupakan satker yang cenderung selisih hasil rekonsiliasinya.

scholarly journals Data Mining For Customer Relationship Management

2011 ◽  
Vol 1 (6) ◽  
Author(s):  
Savitha S. Kadiyala ◽  
Alok Srivastava
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Integrated Model ◽  
Customer Relationship ◽  
New Model ◽  
Data Mining Techniques ◽  
Existing Data

Data mining has various applications for customer relationship management. In this article, we introduce a framework for identifying appropriate data mining techniques for various CRM activities. This article attempts to integrate the data mining and CRM models and to propose a new model of Data mining for CRM. The new model specifies which types of data mining processes are suitable for which stages/processes of CRM. In order to develop an integrated model it is important to understand the existing Data mining and CRM models. Hence the article discusses some of the existing data mining and CRM models and finally proposes an integrated model of data mining for CRM.

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