UNP Journal of Statistics and Data Science最新文献_第6页

Grouping Level of Poverty Based on District/City in Indonesia Using K-Harmonic Means 基于k -谐均值的印尼地区/城市贫困分组水平研究

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/60

Nabillah Putri Shalsabila, N. Amalita, Dodi Vionanda, D. Permana

{"title":"Grouping Level of Poverty Based on District/City in Indonesia Using K-Harmonic Means","authors":"Nabillah Putri Shalsabila, N. Amalita, Dodi Vionanda, D. Permana","doi":"10.24036/ujsds/vol1-iss3/60","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/60","url":null,"abstract":"Indonesia still has a relatively high poverty rate, although nationally it has declined in recent years. There are areas that are still experiencing increasing poverty rates. So that the currently planned poverty alleviation plans are no longer uniform, but need to pay attention to the conditions of each dimension that cause poverty in an area, so it is necessary to group districts/cities in Indonesia on poverty. Grouping was performed using K-Harmonic Means analysis. K-Harmonic Means is a non-hierarchical clustering that takes the average of the harmonic distance between each data point and the cluster’s center. The data used in this research is secondary data sourced from BPS publications on poverty and inequality in 2022. The analysis technique is carried out by standardizing the data, conducting cluster analysis, and validating clusters. Based on the results of the K-Harmonic Means analysis, the optimal number of clusters is two clusters that first cluster has 54 districts/cities while second cluster has 460 districts/cities and the Dunn Index value for cluster validation is 0,03492. So that a better grouping level of poverty based on district/city in Indonesia is obtained by using the K-Harmonic Means method with p = 2,25.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129664337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vector Error Correction Model for Cointegration Analysis of Factors Affecting Indonesia's Economic Growth during the Pandemic Period 流行病期间影响印尼经济增长因素协整分析的向量误差修正模型

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/40

Rizqa Fajriaty, MY Fitri, Dina Fitria, Zilrahmi Syafriandi

{"title":"Vector Error Correction Model for Cointegration Analysis of Factors Affecting Indonesia's Economic Growth during the Pandemic Period","authors":"Rizqa Fajriaty, MY Fitri, Dina Fitria, Zilrahmi Syafriandi","doi":"10.24036/ujsds/vol1-iss3/40","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/40","url":null,"abstract":"Pertumbuhan ekonomi yang stabil merupakan tujuan akhir dari kebijakan moneter yang dilihat dari kestabilan rupiah. Keadaan Ekonomi mengalami penurunan akibat penyebaran Covid-19. Dalam upaya menstabilkan perekonomian, dianalisis hubungan faktor pendukung pertumbuhan ekonomi Indonesia menggunakan pendekatan VECM. Pendekatan ini dapat menentukan hubungan jangka panjang dan jangka pendek data deret waktu. Hasil pemodelan setelah memenuhi beberapa pengujian, didapatkan tiga persamaan yang signifikan. Model tersebut menjelaskan adanya pengaruh dalam jangka pendek variabel inflasi dan BI rate terhadap inflasi serta pengaruh terbalik antara BI rate satu periode sebelumnya terhadap kurs. Koefisien kointegrasi bernilai negatif menunjukkan adanya mekanisme penyesuaian jangka pendek ke jangka panjang yang terjadi pada variabel inflasi. Dua persamaan kointegrasi untuk jangka panjang menunjukkan bahwa untuk jangka panjang inflasi dapat dipengaruhi variabel visa secara positif. Variabel BI rate dalam jangka panjang dipengaruhi variabel kurs dan visa. VECM yang dihasilkan dapat menjelaskan lebih dari 50% variabel.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127334802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The SMOTE Application of CART Methods for Coping Imbalanced Data in Classifying Status Work on Labor Force in the City of Padang 巴东市劳动力现状分类中CART方法处理不平衡数据的应用

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/12

A. Yulianti, F. Fitri, N. Amalita, Dodi Vionanda

{"title":"The SMOTE Application of CART Methods for Coping Imbalanced Data in Classifying Status Work on Labor Force in the City of Padang","authors":"A. Yulianti, F. Fitri, N. Amalita, Dodi Vionanda","doi":"10.24036/ujsds/vol1-iss3/12","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/12","url":null,"abstract":"Employment issues are one of the main concerns in every country, especially in developing countries including Indonesia. Employment problems faced by Indonesia are the lack of job opportunities, excess labor, and the uneven distribution of labor. This is because the growth of the labor force is higher than the growth of existing job opportunities, so that many workers do not get jobs which will cause unemployment. The city of Padang is the city that has the highest unemployment rate in West Sumatra from 2013 to 2021. The development of a smart city and identification of factors that influence unemployment is one of the efforts to reduce unemployment. This study uses the CART method to determine the factors that affect the number of the workforce in the city of Padang. The advantage of the CART method is that it is easy to interpret the results of the analysis, but the accuracy of the classification tree is low due to data imbalance. Therefore, this study uses the SMOTE method to overcome these problems. The optimal classification tree is formed from 8 terminal nodes and involves 4 explanatory variables consisting of marital status (X3), education level (X4), gender (X2) and age(X1), 5 terminal nodes which classify the labor force into the working category and 3 terminal nodes which classify the labor force into the unemployed category.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133368563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Factors Influencing the Population Growth Rate in West Sumatra Using Geographically Weighted Logistic Regression 西苏门答腊岛人口增长率影响因素的地理加权Logistic回归分析

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/59

Rizqia Salsabila, A. A. Putra, N. Amalita, F. Fitri

{"title":"Analysis of Factors Influencing the Population Growth Rate in West Sumatra Using Geographically Weighted Logistic Regression","authors":"Rizqia Salsabila, A. A. Putra, N. Amalita, F. Fitri","doi":"10.24036/ujsds/vol1-iss3/59","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/59","url":null,"abstract":"The model of Geographically Weighted Logistic Regression (GWLR) was the development of a model of logistic regression that was implemented to data in spatial. GWLR model parameter estimation was carried out at each location for observation using spatial weighting. The research purposes was to reveal the GWLR model on the dichotomous data of the Population Growth Rate (PGR) indicator in each Districts/Cities in West Sumatra in 2020 and learn more factors that influence the probability that the population growth rate will increase in 19 Districts/Cities in West Sumatra in 2020. The parameters estimation of the GWLR model uses the Maximum Likelihood Estimation (MLE) method. Spatial weighting for parameter estimation is determined using the Fixed Gaussian Kernel weighting function and determining the optimal bandwidth using Akaike's Information Citerion (AIC) criteria. The variable of response that is categorical in this study is the rate of population growth in each districts/cities in West Sumatra in 2020 and the predictor variables are the couples number of childbearing age, the live births number, the in-migration number, and the out-migration number. The reseacrh result obtained from research were that the GWLR model is better than the logistic regression model and 4 groups of Districts/Cities are formed based on factors that affect the increase in population growth rate.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128883594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Human Development Index in Papua and West Sumatera with Multivariate Adaptive Regression Spline 用多元自适应样条曲线模拟巴布亚和西苏门答腊地区人类发展指数

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/54

Y. Pertiwi, D. Permana, N. Amalita, Admi Salma

{"title":"Modeling Human Development Index in Papua and West Sumatera with Multivariate Adaptive Regression Spline","authors":"Y. Pertiwi, D. Permana, N. Amalita, Admi Salma","doi":"10.24036/ujsds/vol1-iss3/54","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/54","url":null,"abstract":"The Human Development Index (HDI), which is an indicator as a comprehensive description of the measure of achievment development in the quality of human life in a region. In Indonesia, there are still many areas with low HDI, especially in Papua Province. This study aims to model and find out what factors affect HDI in Papua Province and West Sumatera Province, using Multivariate Adaptive Regression Spline (MARS). MARS is a modeling methods that can handle high-dimensional data, namely data that has independent variables and a sampel size of data with unknown data patterns in advance, and can be applied to see interaction between the variables used. The result of this study obtained that the best MARS model for Papua Province is a combination of BF=24, MI=2, and MO=0 with GCV=0.55953. while the best MARS model for West Sumatera Province ia a combination of BF=24, Mi=2, and MO=0 with GCV=0.02697. the factors that significantly affect HDI in Papua Province and West Sumatera Province are average lenght of schooling (X2), adjusted per-capita income (X6), life expectancy (X1), percentage of poor population (X4), anf gross regional domestic product (X3). The level of importance of each variable for Papua Province is 100%, 45.26%, 29.24%, 6.55%, and 6.27%. while for West Sumatera Province it is 100%, 96.73%, 57.54%, 34.13%, and 29.6%,respectively. So that in this case based on research results the average lenght schooling (X2) is the variable that most influences HDI in the two regions.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128908821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rainfall Forcasting in Medan City Using Singular Spectrum Analysis (SSA) 基于奇异谱分析(SSA)的棉兰市降水预报

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/52

Silvia Agustina, F. Fitri, Dodi Vionanda, Admi Salma

引用次数: 0

Comparison of Distance Function in K-Nearest Neighbor Algorithm to Predict Prospective Customers in Term Deposit Subscriptions k -最近邻算法中距离函数在定期存款客户预测中的比较

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/47

Muhammad Tibri Syofyan, N. Amalita, Dodi Vionanda, Dina Fitria

{"title":"Comparison of Distance Function in K-Nearest Neighbor Algorithm to Predict Prospective Customers in Term Deposit Subscriptions","authors":"Muhammad Tibri Syofyan, N. Amalita, Dodi Vionanda, Dina Fitria","doi":"10.24036/ujsds/vol1-iss3/47","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/47","url":null,"abstract":"Data mining is often used to analysis of the big data to obtain new useful information that will be used in the future. One of the best algorithms in data mining is K-Nearest Neighbor (KKN). K-NN classifier is a distance-based classification algorithm. The distance function is a core component in measuring the distance or similarity between the tested data and the training data. Various measure of distance function exist make this a topic of kind literature problems to determining the best distance function for the performance of the K-NN classifier. This study aims to compare which distance function produces the best K-NN performance. The distance function to be compared is the Manhattan distance and Minkowski distance. The application of K-NN classifier using bank dataset about predict prospective customers in Term Deposit Subscriptions. This study show that Minkowski distance on K-NN algorithm achieved the best result compared to Manhattan distance. Minkowski distance with power p = 1.5 produces an accuracy rate of 88.40% when the K value is 7. Thus, performance of K-NN algorithm using Minkowski distance (p=1,5, K=7) is best algorithm in predicting prospective costumers in Term Deposit Subscription","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Grouping The Regencies/Cities in Indonesia Based on Expenditure Groups Inflation Value Using DBSCAN Method 使用DBSCAN方法基于支出组通货膨胀值对印度尼西亚的县/城市进行分组

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/61

Meliani Putri, Dony Permana, Syafriandi Syafriandi, Zilrahmi

{"title":"Grouping The Regencies/Cities in Indonesia Based on Expenditure Groups Inflation Value Using DBSCAN Method","authors":"Meliani Putri, Dony Permana, Syafriandi Syafriandi, Zilrahmi","doi":"10.24036/ujsds/vol1-iss3/61","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/61","url":null,"abstract":"Inflation is one of the important problems faced by a country in achieving economic goals and targets. The amount of inflation is measured using the Consumer Price Index for eleven expenditure groups in 90 regencies/cities in Indonesia. The occurrence of differences in inflation rates between regencies/cities in Indonesia will affect Indonesia's national inflation. The purpose of this research is to grouping regencies/cities based on expenditure groups inflation value and to identify the characteristics of the resulting groups. DBSCAN is a density-based non-hierarchical cluster method that can be used in data conditions that contain outliers. The data used in this study is secondary data obtained from the publication of the Badan Pusat Statistik Republic of Indonesia (BPS RI) regarding inflation by expenditure group. The analysis includes outlier detection, grouping using the DBSCAN method, performing cluster validation with silhouette coefficient, and identifying the characteristics of the clusters formed. Based on the grouping that has been done, two clusters are produced with a silhouette coefficient value of 0.65. The resulting cluster is cluster 0 in the form of a noise cluster consisting of 3 regencies/cities with regencies/cities that have a high category expenditure group inflation rate. Cluster 1 consisting of 87 regencies/cities is a cluster with regencies/cities that have a low category expenditure group inflation rate.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134520440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geographically Weighted Panel Regression Modeling on Human Development Index in West Sumatra 西苏门答腊岛人类发展指数的地理加权面板回归模型

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/63

Amelia Fadila Rahman, Syafriandi, N. Amalita, Zilrahmi

引用次数: 0

Comparison of Queen Contiguity and Customized Weighting Matrices on Spatial Regression to Identify Factors Impacting Poverty in East Java 皇后邻近度与自定义加权矩阵在东爪哇影响贫困因素空间回归中的比较

UNP Journal of Statistics and Data Science Pub Date : 2023-05-31 DOI: 10.24036/ujsds/vol1-iss3/67

Gezi Fajri, S. Syafriandi, N. Amalita, Zamahsary Martha

{"title":"Comparison of Queen Contiguity and Customized Weighting Matrices on Spatial Regression to Identify Factors Impacting Poverty in East Java","authors":"Gezi Fajri, S. Syafriandi, N. Amalita, Zamahsary Martha","doi":"10.24036/ujsds/vol1-iss3/67","DOIUrl":"https://doi.org/10.24036/ujsds/vol1-iss3/67","url":null,"abstract":"Poverty is crucial problem that negative impact on all sectors, including economic, social, and cultural development in East Java Province. Poverty can also increase unemployment, crime, trigger social disasters and hinder progress East Java province. One efforts overcome problem of poverty in East Java province is detect factors that influence. This effort can be done through statistical modeling to determine factors that influence poverty in East Java province. statistical model that can identify factors that influence poverty and explain relationship between region and surrounding area is spatial regression analysis. In spatial regression analysis, spatial weighting matrix is needed to determine spatial influences between regions where one region influences neighboring regions. spatial weighting matrices that is often used is queen contiguity, and according to Anselin (1988:20), this spatial weighting also considers initial information, purpose of case studied, and theory underlying the research. This weighting uses social and economic variables case under study, namely customized weighting matrix. Based on results of this study, shows that best spatial regression and spatial weighting models are General Spatial Model (GSM) with customized weighting because customized weighting produces better estimation results than SAR, SEM and GSM models with queen contiguity weighting in district and city poverty modeling in East Java province with Akaike Infomation Criterion (AIC) value of 188.77 and detemination coefficient (R2) of 84.95%. School's Expected Time, Life Expectancy Score, and Employment Participation Rate are factors that will have substantial impact on percentage of population living in poverty East Java's districts and cities in 2021.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134542091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0