One form of evaluation of student learning outcomes is the Final Semester Examination. This exam is designed to measure the extent of achievement of educational objectives. A good evaluation must meet several criteria, including good item validity and reliability, a variety of difficulty levels, and the power of differentiation. This study aims to describe the results of a comparative analysis of the quality of measurement instruments in the form of multiple-choice questions using the classical test theory approach and the Rasch model in terms of validity, reliability, difficulty level, and question differentiation. Data were obtained through a website that presents multiple choice exam results of grade XI students at SMA Negeri 3 Gorontalo, consisting of 26 female students and 10 male students. The results showed that in the instrument validity analysis, the Rasch model showed more valid items with a determination category of 0.4 < pt measure corr < 0.8. This means that the Rasch model provides a better analysis compared to the classical test theory analysis. In the reliability analysis, the reliability value of items in the Rasch model is higher but in almost the same category. In analyzing the difficulty level of the instrument, the classical test theory approach shows that the items are in the easy, medium, and difficult categories, so they are still considered capable of measuring students' abilities. However, in the Rasch model, items are only in the very easy, difficult, and extremely difficult categories. In analyzing the power of differentiation, the classical test theory method and the Rasch model have not provided good enough results to identify respondents in several groups based on their level of understanding
评估学生学习成果的一种形式是期末考试。这个考试的目的是衡量教育目标实现的程度。一个好的评估必须满足几个标准，包括良好的项目效度和可靠性，各种难度水平，以及差异化的力量。本研究旨在运用经典测验理论方法和Rasch模型，从效度、信度、难易程度和问题分化等方面对多项选择题形式的测量工具质量进行比较分析。数据是通过一个网站获得的，该网站显示了SMA Negeri 3 Gorontalo十一年级学生的多项选择考试成绩，其中包括26名女生和10名男生。结果表明，在仪器效度分析中，Rasch模型显示出更多的有效项目，其测定类别为0.4 < pt测量系数< 0.8。这意味着与经典的测试理论分析相比，Rasch模型提供了更好的分析。在信度分析中，Rasch模型中项目的信度值较高，但在几乎相同的类别中。在分析量表的难度等级时，经典的测试理论方法表明，量表的题目分为易、中、难三类，因此仍然认为它们能够衡量学生的能力。然而，在Rasch模型中，项目只属于非常简单、困难和极其困难的类别。在分析分化能力时，经典的测试理论方法和Rasch模型并没有提供足够好的结果来根据他们的理解水平将被调查者划分为几个群体
{"title":"ANALYZING THE QUALITY OF MEASUREMENT INSTRUMENTS OF MULTIPLE CHOICE QUESTIONS ON CLASS XI ECONOMICS MATERIAL IN PUBLIC HIGH SCHOOL 3 GORONTALO THROUGH CLASSICAL TEST THEORY AND RASCH MODELS","authors":"Luthfiah Yulisharyasti, Ansor Nurdin, Nanda Aulia, Fhahnul Aiman H Arfa, Fadjryani","doi":"10.22487/27765660.2023.v3.i1.16417","DOIUrl":"https://doi.org/10.22487/27765660.2023.v3.i1.16417","url":null,"abstract":"One form of evaluation of student learning outcomes is the Final Semester Examination. This exam is designed to measure the extent of achievement of educational objectives. A good evaluation must meet several criteria, including good item validity and reliability, a variety of difficulty levels, and the power of differentiation. This study aims to describe the results of a comparative analysis of the quality of measurement instruments in the form of multiple-choice questions using the classical test theory approach and the Rasch model in terms of validity, reliability, difficulty level, and question differentiation. Data were obtained through a website that presents multiple choice exam results of grade XI students at SMA Negeri 3 Gorontalo, consisting of 26 female students and 10 male students. The results showed that in the instrument validity analysis, the Rasch model showed more valid items with a determination category of 0.4 < pt measure corr < 0.8. This means that the Rasch model provides a better analysis compared to the classical test theory analysis. In the reliability analysis, the reliability value of items in the Rasch model is higher but in almost the same category. In analyzing the difficulty level of the instrument, the classical test theory approach shows that the items are in the easy, medium, and difficult categories, so they are still considered capable of measuring students' abilities. However, in the Rasch model, items are only in the very easy, difficult, and extremely difficult categories. In analyzing the power of differentiation, the classical test theory method and the Rasch model have not provided good enough results to identify respondents in several groups based on their level of understanding","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127379365","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}
Pub Date : 2023-06-30DOI: 10.22487/27765660.2023.v3.i1.16435
Gustriza Erda, Chairani Gunawan, Zulya Erda
Poverty is an enormous problem in numerous nations including Indonesia. Poverty can be measured using several indicators, including the unemployment rate, the percentage of poor people, expenditures per capita, and the poverty line. The purpose of this study is to categorize Indonesian provinces based on poverty indicators in 2021 using K-Means with the Silhouette Coefficient approach. Based on the silhouette coefficient approach, there are two clusters that are created. The first cluster is a high-poverty-rate regional group that includes the provinces of Aceh, Bengkulu, West Nusa Tenggara, East Nusa Tenggara, Central Sulawesi, Gorontalo, Maluku, West Papua, and Papua. On the other hand, the second cluster is an association of regions with a low poverty rate, and it includes 25 provinces. The greater number of provinces in the low poverty rate cluster implies that the poverty rate in Indonesia in 2021 is included in the low category
{"title":"GROUPING OF POVERTY IN INDONESIA USING K-MEANS WITH SILHOUETTE COEFFICIENT","authors":"Gustriza Erda, Chairani Gunawan, Zulya Erda","doi":"10.22487/27765660.2023.v3.i1.16435","DOIUrl":"https://doi.org/10.22487/27765660.2023.v3.i1.16435","url":null,"abstract":"Poverty is an enormous problem in numerous nations including Indonesia. Poverty can be measured using several indicators, including the unemployment rate, the percentage of poor people, expenditures per capita, and the poverty line. The purpose of this study is to categorize Indonesian provinces based on poverty indicators in 2021 using K-Means with the Silhouette Coefficient approach. Based on the silhouette coefficient approach, there are two clusters that are created. The first cluster is a high-poverty-rate regional group that includes the provinces of Aceh, Bengkulu, West Nusa Tenggara, East Nusa Tenggara, Central Sulawesi, Gorontalo, Maluku, West Papua, and Papua. On the other hand, the second cluster is an association of regions with a low poverty rate, and it includes 25 provinces. The greater number of provinces in the low poverty rate cluster implies that the poverty rate in Indonesia in 2021 is included in the low category","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133873340","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}
The purpose of this study was to examine the instruments used to assess students' abilities in the designer analysis course of the statistics study program at Tadulako University. There were 90 students enrolled in this course, and the questions included 25 multiple-choice items related to survey design content. The test instrument for understanding the survey designer course was the subject of this study. The Rasch method, which is used to get fit items, is used. Winsteps software was used to carry out this analysis. In accordance with the Rasch model, the Winsteps program produced 23 items with an average value of 1.11 and -0.08 for MNSQ Outfit for person and item, respectively. In spite of the fact that the person's and the item's Outfit ZSTD values are 1.11 and 0.26, respectively, and despite the fact that the instrument's reliability, as measured by Cronbach's alpha, is 0.86, 23 of the 25 question items fit and 2 do not.
{"title":"APPLICATION OF THE RASCH MODEL TO TEST TOOLS IN THE ANALYSIS OF SURVEY DESIGN","authors":"Nini Anggraini, Chantika Nabillah, Herdi Dermawan Lonan, Hartayuni Sain","doi":"10.22487/27765660.2023.v3.i1.16412","DOIUrl":"https://doi.org/10.22487/27765660.2023.v3.i1.16412","url":null,"abstract":"The purpose of this study was to examine the instruments used to assess students' abilities in the designer analysis course of the statistics study program at Tadulako University. There were 90 students enrolled in this course, and the questions included 25 multiple-choice items related to survey design content. The test instrument for understanding the survey designer course was the subject of this study. The Rasch method, which is used to get fit items, is used. Winsteps software was used to carry out this analysis. In accordance with the Rasch model, the Winsteps program produced 23 items with an average value of 1.11 and -0.08 for MNSQ Outfit for person and item, respectively. In spite of the fact that the person's and the item's Outfit ZSTD values are 1.11 and 0.26, respectively, and despite the fact that the instrument's reliability, as measured by Cronbach's alpha, is 0.86, 23 of the 25 question items fit and 2 do not.","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121462889","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}
Pub Date : 2023-06-30DOI: 10.22487/27765660.2023.v3.i1.16423
F. Fauzi, G. H. Wenur, R. Wasono
Unemployment is a labor problem that is often faced by developing countries like Indonesia. The number of unemployed in Indonesia has fluctuated from year to year, including in Central Java Province. One of the efforts made to overcome this problem is to know the factors that influence unemployment. The region effect greatly affects the open unemployment rate. Modeling involving area effects is very precise, one of which is the Spatial Durbin Model (SDM). In this study, modeling of the open unemployment rate was carried out using a spatial approach in each district/city in Central Java. The models used in this study are Ordinary Last Square (OLS), Spatial Auto Regressive (SAR), Spatial Error Models (SEM), Spatial Durbin Model (SDM), Spatial Error Durbin Model (SDEM). The five methods were evaluated using the Akaike Information Criteria (AIC). The spatial weighting used in this study is Queen Contiguity. Based on the smallest AIC value (115.42), the best method in this study is HR. Meanwhile, the significant factors are the percentage of labor force participation rate (X1), the number of poor people (X4), the lag of economic growth, the lag of poverty, and the lag of the district/city minimum wage
失业是印尼等发展中国家经常面临的劳工问题。印度尼西亚的失业人数每年都在波动，中爪哇省也是如此。为克服这一问题所作的努力之一是了解影响失业的因素。区域效应极大地影响了公开失业率。涉及区域效应的建模是非常精确的，其中之一是空间杜宾模型(SDM)。在本研究中，采用空间方法对中爪哇每个地区/城市的公开失业率进行了建模。本研究使用的模型有:普通末方(OLS)、空间自动回归(SAR)、空间误差模型(SEM)、空间杜宾模型(SDM)、空间误差杜宾模型(SDEM)。采用赤池信息标准(Akaike Information Criteria, AIC)对5种方法进行评价。本研究中使用的空间加权是皇后连续度。基于AIC最小值(115.42)，本研究的最佳方法是HR。同时，显著性因素为劳动力参与率百分比(X1)、贫困人口数量(X4)、经济增长的滞后性、贫困人口的滞后性、区/市最低工资的滞后性
{"title":"SPATIAL DURBIN MODEL OF UNEMPLOYMENT RATE IN CENTRAL JAVA","authors":"F. Fauzi, G. H. Wenur, R. Wasono","doi":"10.22487/27765660.2023.v3.i1.16423","DOIUrl":"https://doi.org/10.22487/27765660.2023.v3.i1.16423","url":null,"abstract":"Unemployment is a labor problem that is often faced by developing countries like Indonesia. The number of unemployed in Indonesia has fluctuated from year to year, including in Central Java Province. One of the efforts made to overcome this problem is to know the factors that influence unemployment. The region effect greatly affects the open unemployment rate. Modeling involving area effects is very precise, one of which is the Spatial Durbin Model (SDM). In this study, modeling of the open unemployment rate was carried out using a spatial approach in each district/city in Central Java. The models used in this study are Ordinary Last Square (OLS), Spatial Auto Regressive (SAR), Spatial Error Models (SEM), Spatial Durbin Model (SDM), Spatial Error Durbin Model (SDEM). The five methods were evaluated using the Akaike Information Criteria (AIC). The spatial weighting used in this study is Queen Contiguity. Based on the smallest AIC value (115.42), the best method in this study is HR. Meanwhile, the significant factors are the percentage of labor force participation rate (X1), the number of poor people (X4), the lag of economic growth, the lag of poverty, and the lag of the district/city minimum wage","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133140978","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}
Pub Date : 2023-06-30DOI: 10.22487/27765660.2023.v3.i1.16408
Zulfanita Dien Rizqiana
Poverty is a problem that continues to be faced, especially in developing countries such as Indonesia. Poverty is included in one of the Sustainable Development Goals (SDGs) programs, which is related to hunger and health. The time series data can be clustered based on the characteristics of the time series data and adjusted to the time series pattern. The choice of distance and method used must be adjusted to the dynamic structure of time series data. The purpose of this research is to cluster districts/cities in Central Java Province based on the poverty depth index value from 2017 to 2022. The variable that used in this research is the Poverty Depth Index of 35 districts in Central Java Province from 2017 to 2022. This research used cluster time series with DTW similarity measurment. Based on theDTW and cophenetic coefficient correlation value using three linkage methods, the average linkage method has the highest cophenetic coefficient correlation value of 0.8017988. Testing the quality of clusters using the silhouette coefficient using DTW distance and average linkage method and 2 clusters are included in the good cluster category with a silhouette coefficient value of 0.60. The resulting clusters using the DTW distance and average linkage method are cluster 1 consisting of 25 districts / cities and cluster 2 consisting of 10 districts.
{"title":"APPLICATION OF TIME SERIES CLUSTER ANALYSIS IN CLUSTERING THE CENTRAL JAVA PROVINCE BASED ON THE POVERTY DEPTH INDEX","authors":"Zulfanita Dien Rizqiana","doi":"10.22487/27765660.2023.v3.i1.16408","DOIUrl":"https://doi.org/10.22487/27765660.2023.v3.i1.16408","url":null,"abstract":"Poverty is a problem that continues to be faced, especially in developing countries such as Indonesia. Poverty is included in one of the Sustainable Development Goals (SDGs) programs, which is related to hunger and health. The time series data can be clustered based on the characteristics of the time series data and adjusted to the time series pattern. The choice of distance and method used must be adjusted to the dynamic structure of time series data. The purpose of this research is to cluster districts/cities in Central Java Province based on the poverty depth index value from 2017 to 2022. The variable that used in this research is the Poverty Depth Index of 35 districts in Central Java Province from 2017 to 2022. This research used cluster time series with DTW similarity measurment. Based on theDTW and cophenetic coefficient correlation value using three linkage methods, the average linkage method has the highest cophenetic coefficient correlation value of 0.8017988. Testing the quality of clusters using the silhouette coefficient using DTW distance and average linkage method and 2 clusters are included in the good cluster category with a silhouette coefficient value of 0.60. The resulting clusters using the DTW distance and average linkage method are cluster 1 consisting of 25 districts / cities and cluster 2 consisting of 10 districts.","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126835955","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}
Pub Date : 2023-06-30DOI: 10.22487/27765660.2023.v3.i1.16445
I. Usman
The SIQS (Susceptible, Infective, Quarantine, and Susceptible) non-linear model is used to describe the dynamics of infectious diseases, especially optimizing individuals who are quarantined. Discretization of the SIQS model using the Runge-Kutta method and its physical interpretation is very useful if the model parameters can be estimated. Bayesian Markov Chain Monte Carlo for its numerical simulation. After 10,000 iterations, convergent and significant parameters were obtained, namely beta = 94.37, beta0 = -10.19, mu = -0.23 and b = 0.5.
SIQS(易感、感染、隔离和易感)非线性模型用于描述传染病的动态，特别是优化被隔离的个体。利用龙格-库塔方法对SIQS模型进行离散化及其物理解释是非常有用的，如果模型参数可以估计。贝叶斯马尔可夫链蒙特卡罗对其进行数值模拟。经过10000次迭代，得到收敛且显著的参数，即beta = 94.37, beta0 = -10.19, mu = -0.23, b = 0.5。
{"title":"BAYESIAN MARKOV CHAIN MONTE CARLO SIMULATION OF NONLIENAR MODEL FOR INFECTIOUS DISEASES WITH QUARANTINE","authors":"I. Usman","doi":"10.22487/27765660.2023.v3.i1.16445","DOIUrl":"https://doi.org/10.22487/27765660.2023.v3.i1.16445","url":null,"abstract":"The SIQS (Susceptible, Infective, Quarantine, and Susceptible) non-linear model is used to describe the dynamics of infectious diseases, especially optimizing individuals who are quarantined. Discretization of the SIQS model using the Runge-Kutta method and its physical interpretation is very useful if the model parameters can be estimated. Bayesian Markov Chain Monte Carlo for its numerical simulation. After 10,000 iterations, convergent and significant parameters were obtained, namely beta = 94.37, beta0 = -10.19, mu = -0.23 and b = 0.5.","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114478221","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}
Pub Date : 2023-01-10DOI: 10.22487/27765660.2022.v2.i3.15373
Nur Sakinah, Nurmasyita Ihlasia, Nurfitra, Marni Sagap, Rohis Rachman, L. Handayani
A measurement of a nation's human resource condition is the human development index (HDI). The three components of the human development index are living standards, often known as economics, and health. In Central Sulawesi Province in 2019, this study seeks to ascertain the impact of life expectancy (AHH) and per capita spending on the human development index (HDI). Secondary data from the Central Statistics Agency (BPS) of Central Sulawesi Province, corroborated by additional sources, was used in this study. The multiple linear regression analysis methods were the analysis technique used in this study.The findings demonstrated a positive and significant impact of partially variable Life Expectancy (AHH) and per capita spending variables on the Human Development Index (HDI). The Human Development Index (HDI) in Central Sulawesi Province is thereafter significantly impacted by the combination of the two independent factors in 2019.
{"title":"Analysis of The Effect of Life Expectancy (AHH) and Per Capita Expenditure on The Human Development Index (HDI) in Central Sulawesi Province in 2019","authors":"Nur Sakinah, Nurmasyita Ihlasia, Nurfitra, Marni Sagap, Rohis Rachman, L. Handayani","doi":"10.22487/27765660.2022.v2.i3.15373","DOIUrl":"https://doi.org/10.22487/27765660.2022.v2.i3.15373","url":null,"abstract":"A measurement of a nation's human resource condition is the human development index (HDI). The three components of the human development index are living standards, often known as economics, and health. In Central Sulawesi Province in 2019, this study seeks to ascertain the impact of life expectancy (AHH) and per capita spending on the human development index (HDI). Secondary data from the Central Statistics Agency (BPS) of Central Sulawesi Province, corroborated by additional sources, was used in this study. The multiple linear regression analysis methods were the analysis technique used in this study.The findings demonstrated a positive and significant impact of partially variable Life Expectancy (AHH) and per capita spending variables on the Human Development Index (HDI). The Human Development Index (HDI) in Central Sulawesi Province is thereafter significantly impacted by the combination of the two independent factors in 2019.","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121605110","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}
Pub Date : 2023-01-04DOI: 10.22487/27765660.2022.v2.i3.16138
Rizka Pradita Prasetya
Outliers in regression analysis can cause large residuals, the diversity of the data becomes greater, causing the data to be heterogenous. If an outlier is caused by an error in recording observations or an error in preparing equipment, the outlier can be ignored or discarded before data analysis is carried out. However, if outliers exist not because of the researcher's error, but are indeed information that cannot be provided by other data, then the outlier data cannot be ignored and must be included in data analysis. There are several methods to deal with outliers. The Weight Least Square method produces good results and is quite resistive to outliers. The WLS method is used to overcome the regression model with non-constant error variance, because WLS has the ability to neutralize the consequences of violating the normality assumption caused by the presence of outliers and can eliminate the nature of unusualness and consistency of the OLS estimate. To compare the level of estimator accuracy between regression models, the mean absolute percentage error (MAPE) is used. Based on the results of this study, it was concluded that the WLS method produced a smaller Mean Absolute Percentage Error value so that the use of this method was more appropriate because it was not susceptible to the effect of outliers.
{"title":"Unpacking Outlier with Weight Least Square (Implemented on Pepper Plantations Data)","authors":"Rizka Pradita Prasetya","doi":"10.22487/27765660.2022.v2.i3.16138","DOIUrl":"https://doi.org/10.22487/27765660.2022.v2.i3.16138","url":null,"abstract":"Outliers in regression analysis can cause large residuals, the diversity of the data becomes greater, causing the data to be heterogenous. If an outlier is caused by an error in recording observations or an error in preparing equipment, the outlier can be ignored or discarded before data analysis is carried out. However, if outliers exist not because of the researcher's error, but are indeed information that cannot be provided by other data, then the outlier data cannot be ignored and must be included in data analysis. There are several methods to deal with outliers. The Weight Least Square method produces good results and is quite resistive to outliers. The WLS method is used to overcome the regression model with non-constant error variance, because WLS has the ability to neutralize the consequences of violating the normality assumption caused by the presence of outliers and can eliminate the nature of unusualness and consistency of the OLS estimate. To compare the level of estimator accuracy between regression models, the mean absolute percentage error (MAPE) is used. Based on the results of this study, it was concluded that the WLS method produced a smaller Mean Absolute Percentage Error value so that the use of this method was more appropriate because it was not susceptible to the effect of outliers.","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133561053","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}
Pub Date : 2023-01-02DOI: 10.22487/27765660.2022.v2.i3.16203
Gustriza Erda, Sartika Mega Aulia, Zulya Erda
The Human Development Index is a critical indicator of economic growth. Several factors, including average length of schooling (X1), expected length of schooling (X2), life expectancy at birth (X3), number of health workers (X4), number of health facilities (X5), spending per capita (X6), open unemployment rate (X7), number of poor people (X8), percentage of households with proper drinking water sources (X9), and GRDP growth rate (X10), can influence the Human Development Index. The purpose of this research was to simplify the factors that influence the human development index in Riau Province in 2021. Data analysis used R-Studio software by applying descriptive statistical analysis, Principal Component analysis, and Biplot analysis. The analysis revealed that the ten variables that influence human development index in Riau in 2021 can be divided into three categories: community service quality, health facilities, access, and economic conditions. These three factors can describe up to 80% of the diversity of the data.
{"title":"Classifiying The Factors Influencing The Human Development Index in Riau Province using Principal Component Analysis","authors":"Gustriza Erda, Sartika Mega Aulia, Zulya Erda","doi":"10.22487/27765660.2022.v2.i3.16203","DOIUrl":"https://doi.org/10.22487/27765660.2022.v2.i3.16203","url":null,"abstract":"The Human Development Index is a critical indicator of economic growth. Several factors, including average length of schooling (X1), expected length of schooling (X2), life expectancy at birth (X3), number of health workers (X4), number of health facilities (X5), spending per capita (X6), open unemployment rate (X7), number of poor people (X8), percentage of households with proper drinking water sources (X9), and GRDP growth rate (X10), can influence the Human Development Index. The purpose of this research was to simplify the factors that influence the human development index in Riau Province in 2021. Data analysis used R-Studio software by applying descriptive statistical analysis, Principal Component analysis, and Biplot analysis. The analysis revealed that the ten variables that influence human development index in Riau in 2021 can be divided into three categories: community service quality, health facilities, access, and economic conditions. These three factors can describe up to 80% of the diversity of the data.","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127618681","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}
Pub Date : 2022-12-31DOI: 10.22487/27765660.2022.v2.i3.15743
Nur Sakinah, Nurfitra, Nurmasyita Ihlasia, L. Handayani
Poverty is defined as a person's inability to meet their basic needs. The level of poverty that exists can be used to assess the good or bad of a country's economy. The kernel regression method is used in this study to model the poverty rate in Central Sulawesi in 2020. According to the findings of this study, comparing poverty rate predictions for the Gaussian Kernel function and the Epanechnikov Kernel function with optimal bandwidth can be said to use different kernel functions with optimal bandwidth for each - each of these kernel functions will produce the same curve estimate. So, in kernel regression, the selection of the optimal bandwidth value is more important than the selection of the kernel function. Because of the use of various kernels functions with optimal bandwidth values results in almost the same curve estimation.
{"title":"Modeling of Poverty Level in Central Sulawesi Using Nonparametric Kernel Regression Analysis Approach","authors":"Nur Sakinah, Nurfitra, Nurmasyita Ihlasia, L. Handayani","doi":"10.22487/27765660.2022.v2.i3.15743","DOIUrl":"https://doi.org/10.22487/27765660.2022.v2.i3.15743","url":null,"abstract":"Poverty is defined as a person's inability to meet their basic needs. The level of poverty that exists can be used to assess the good or bad of a country's economy. The kernel regression method is used in this study to model the poverty rate in Central Sulawesi in 2020. According to the findings of this study, comparing poverty rate predictions for the Gaussian Kernel function and the Epanechnikov Kernel function with optimal bandwidth can be said to use different kernel functions with optimal bandwidth for each - each of these kernel functions will produce the same curve estimate. So, in kernel regression, the selection of the optimal bandwidth value is more important than the selection of the kernel function. Because of the use of various kernels functions with optimal bandwidth values results in almost the same curve estimation.","PeriodicalId":337689,"journal":{"name":"Parameter: Journal of Statistics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125300729","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}
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