Gross Regional Domestic Product (GRDP) is an indicator used to measure economic performance in a period. GRDP is the amount of added value generated by all business units in a particular area. It can also be said to be the sum of the value of the final goods and services produced by all economic units. Therefore, this study aims to cluster the GRDP Growth Rate according to business fields in the city of Surabaya, so that it is known which sectors have high or low growth. The clustering algorithm used is K-Means. By using this method, the data will b,e grouped into several clusters, where the implementation of the K-Means Clustering process uses the Rapid Miner tools. The data used is the GRDP Growth Rate in Surabaya City by Business Field, 2010-2019 (Percent). The data is divided into 3 clusters: high, medium, and low. The results obtained are nine categories/sectors with high clusters, 5 categories / sectors with medium clusters, and three categories,s / sectors with low clusters. This can be input and information for the Surabaya City government to further maximize efforts to increase the GRDP Growth Rate in the area.

http://ijistech.org/ijistech/index.php/ijistech/article/download/60/60

GRDP Growth Rate Clustering in Surabaya City uses the K-Means Algorithm

The poverty line is useful as an economic tool that can be used to measure the poor and consider socio-economic reforms, such as welfare programs and unemployment insurance to reduce poverty. Therefore, this study aims to classify poverty lines according to regencies/cities in North Sumatra Province, so that it is known which districts/cities have high or low poverty lines. The grouping algorithm used is K-Means data mining. By using this algorithm, the data will be grouped into several parts, where the process of implementing K-Means data mining uses Rapid Miner. The data used is the poverty line data by district/city (rupiah/capita/month) in the province of North Sumatra in 2017-2019. Data sourced from the North Sumatra Central Statistics Agency. The grouping is divided into 3 clusters: high category poverty line, medium category poverty line, and the low category poverty line. The results for the high category consisted of 5 districts/cities, the medium category consisted of 18 districts/cities and the medium category consisted of 10 districts/cities. This can provide input and information for the North Sumatra government to further maximize efforts to overcome the poverty line in the area.

http://ijistech.org/ijistech/index.php/ijistech/article/download/66/66

Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra

Until now, Covid-19 is a phenomenal problem faced by almost all over the world, especially countries on the Asian continent. Not only causing casualties, but this virus also affects the wheels of the country's economy. The purpose of this paper is to view and map the spread of the Covid-19 virus in Asia based on Total Cases, Total Deaths, Total Cured and Active Cases from 49 Countries. The research data in this paper were obtained from the Worldometer website sourced from WHO, CDC, NHC and others. In this proposed paper, the algorithm used is X-Means Clustering with the help of Rapidminer. The results of this proposed paper are in the form of grouping or mapping the spread of the Covid-19 virus in Asia which is divided into 4 zones, including the Red Zone (the number of active cases of Covid -19 and the death rate is very high) which consists of 1 country, the Orange Zone (number of cases active covid -19 and the mortality rate is quite high) consisting of 1 countries, the Yellow Zone (active case rate of Covid -19 and moderate mortality) consisting of 39 countries, and the Blue Zone (active case rate of covid -19 and low mortality rate) which consists of 8 countries.

Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms

A birth certificate is a document that must be owned by a citizen. This document contains information about the birth of a person and is an official record of proof of state recognition of the person's existence. The purpose of this study is to group birth certificate documents in each district in Simalungun Regency. The research data was obtained from the Population and Civil Registry Office of Simalungun Regency. The grouping algorithm used is the K-means algorithm which is one of the Data Mining algorithms that is good for the case of grouping. By using this algorithm the data that has been obtained can be grouped into several clusters, where the application of the K-Means Clustering process uses the RapidMiner tool. Data is divided into 3 groups: high (C1), medium (C2) and low (C3). The results obtained from this study are in December entered into a high level cluster (C1), in August, September and October entered into a medium cluster (C2), and in January, February, March, April, May, June, July and November entered into the low cluster (C3).

/pdf/algoritma-k-means-untuk-pengelompokkan-dokumen-akta-hq82zhilao.pdf

Algoritma K-Means untuk Pengelompokkan Dokumen Akta Kelahiran pada Tiap Kecamatan di Kabupaten Simalungun

Population growth in an area is important for development and is a benchmark for an area to develop. The way to predict population growth is to use Data Mining. Data mining is able to analyze data into information. This study will discuss the amount of population growth in the District of Gunung Malela. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make an estimate/prediction by utilizing old data regarding population growth so that it can produce a pattern of relationships. This Multiple Linear Regression method aims to make the best predictions. The research data used is the population in the Gunung Malela sub-district in 2016-2020. Based on the research that has been done using the Multiple Linear Regression method, the results of the population growth are 40078 residents. This means that there is an additional population of 469 people in Gunung Malela District. The results of this study can be input to the Gunung Malela Sub-District Office to anticipate the rate of population growth and it can be concluded based on this study that the Multiple Linear Regression method can be used to estimate the population.

/pdf/the-application-of-multiple-linear-regression-method-for-fy3cg52z.pdf

The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District

Classification is a method of data analysis that is used to create models that describe data classes that are considered important. For the classification of the classification process, the data used is THPS Visitor data which consists of 4 classes including Education, Gender, Age and Visit. The classification used as a comparison of results is the Naive Bayes Classifier. By classifying the number of visitors who are most dominant visiting by age category consisting of adults, adolescents and children. This study aims to classify the highest number of values for visitors by age category. This study was reviewed using the Naive Bayes algorithm. The results of this study indicate that the visitor data that is the most dominant visiting by age category is children who have 77% accuracy is the age of children. This accuracy value is the age that most often visits THPS.

Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Susiani

Author Tools

Chat about Author

Papers

Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category