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Parameter: Journal of Statistics
Published by Universitas Tadulako
ISSN : -     EISSN : 27765660     DOI : https://doi.org/10.22487/27765660.2021.v1.i2
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Parameter: Journal of Statistics is a refereed journal committed to original research articles, reviews and short communications of Statistics and its applications.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 66 Documents
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Clustering of Province in Indonesia Based on Aquaculture Productivity Using Average Linkage Method Putera, Fachruddin Hari Anggara; Mangitung, Septina F.; Madinawati; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2021.v2.i1.15683

Abstract

Fisheries are one of the agricultural sub-sectors that play an important role in contributing to income figures for the state and the region because most of Indonesia's territory is water so that the fisheries sector is a sub-sector that is feasible to be developed in this country, one of which is through aquaculture. One of the efforts that can increase and maintain productivity in the aquaculture sector is to classify provinces that produce aquaculture production into groups based on the similarity of characteristics possessed by each province in Indonesia. In this study, clustering was carried out using cluster analysis using the average linkage method and based on the analysis results obtained showed that cluster 1 consists of 25 provinces, cluster 2 consists of 5 provinces, cluster 3 consists of 2 provinces, cluster 4 consists of 1 province, and cluster 5 consists of 1 province with a standard deviation value within a cluster of 11,729 and a standard deviation between clusters of 118,745.
The Motivation of Criminality During the Covid-19 Pandemic in Central Sulawesi Fadjryani; Saputra, Wawan
Parameter: Journal of Statistics Vol. 2 No. 2 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i2.15680

Abstract

With the news of the crime were often pushed in a variety of digital platforms and non- digital and criminal acts that are still happening in the community, make this topic endlessly to be discussed. Moreover, during the current pandemic, so many demands for life are not in line with the situation as a result of the implementation of the lockdown policy and the implementation of restrictions on community activities (IRCA) from the government which requires some people to be willing to lose their livelihood. Meanwhile, out of 100,000 people in Indonesia, 140 of them are at risk of being exposed to crime. The high crime rate is influenced by several factors such as education, less strict laws, high unemployment and inadequate wages. The purpose of this study was to determine the characteristics of crime and determine the factors that influence the occurrence of criminal acts in Central Sulawesi during the Covid-19 pandemic. This type of research is a type of descriptive qualitative research and descriptive quantitative. The data used in this study is secondary data from the Central Statistics Agency and the Central Sulawesi Regional Police. The research method used is multiple linear regression. The results of this study show that the characteristics of crime in Central Sulawesi during the pandemic, namely ordinary theft cases became the highest indicator in criminal cases, while theft in the family became the lowest indicator in criminal cases. In addition, it is known that the dominant criminal acts are carried out by men with self-employed and unemployed jobs, with the last education being high school or equivalent. Partially, the variable Number of Poor People has a significant effect on crime that occurs in Central Sulawesi and simultaneously or together the four variables, namely education, unemployment, Gross Regional Domestic Product (GRDP) and Number of Poor Population have an effect on the occurrence of crime in Central Sulawesi. The result of the coefficient of determination in this study was 79.99% it means that the four independent variables are able to explain the dependent variable of 79.99% and the remaining 20.01% are other variables that have not been used as variables in this study.
Factor Analysis for Increasing Reading Literacy in Indonesia Pitri, Rizka; Sofia, Ayu
Parameter: Journal of Statistics Vol. 2 No. 2 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i2.15898

Abstract

Low interest in reading is a problem for our nation that must be solved, because Indonesia to occupy second position from bottom in terms of literacy. Most of the provinces in Indonesia are at low literacy activity levels and none of the provinces are included in the high literacy activity level. The lack of interest in reading can be influenced by many factors. Access of supporting resource where people get literacy materials, such as libraries, bookstores, and mass media, how people to get the information technology, and media devices to access literacy materials are the factor that can be affect the interest of reading. Literacy is one of the important cultures for a country. That is because the culture is able to influence the intelligence and well-being of a country's life. So the study aims to see what factors affect to increasing the literacy reading in the provinces in Indonesia. This study uses k-means clustering before applying factor analysis. Based on k-means clustering, two clusters are formed and showed one of the cluster showed that the second cluster is the provinces that have the highest number of library’s facilities. In addition based on the analysis factor in each cluster, two factors were formed, namely the standard factor for reading literacy levels and supporting the facilities for reading literacy. It can be concluded that the way to increase reading literacy in two clusters of the area in Indonesia are by increasing the standard of reading literacy level and supporting the facilities for reading literacy.
Application of Negative Binomial Regression Analysis to Overcome the Overdispersion of Poisson Regression Model for Malnutrition Cases in Indonesia Setyawan, Yudi; Suryowati, Kris; Octaviana, Dita
Parameter: Journal of Statistics Vol. 2 No. 2 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i2.15903

Abstract

Indonesia is one of the developing countries that is struggling to eradicate the malnutrition problem. Malnutrition that occurs over a long period of time can have an impact on the deaths of sufferers and decrease human quality of life. This study aims to model the case of malnutrition that occurred in Indonesia Provinces during 2015 and get the main factors that cause the malnutrition problem. Variables studied consist of Malnutrition (Y), Vitamin A consumption (X1), Exclusive breastfeeding (X2), Immunization (X3), Water quality (X4), Healthcare center (X5), and Poverty level (X6). Based on the Kolmogorov-Smirnov test, the results of malnutrition data in Indonesia Province in 2015 do not follow Poisson distribution because of overdispersion. The presence of overdispersion cases in the Poisson regression model will have an impact on the inappropriateness of inferences. An alternative model that accommodates this case is the negative binomial regression model. By using this model, factors that are considered influencing malnutrition cases in Indonesia provinces in 2015 are Immunization (X3), Water quality (X4), and Poverty level (X6).
Comparison of Cochrane-Orcutt and Hildreth-Lu Methods to Overcome Autocorrelation in Time Series Regression (Case Study of Gorontalo Province HDI 2010-2021) Tri Subhi, Khusnudin; Al Azkiya, Azka
Parameter: Journal of Statistics Vol. 2 No. 2 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i2.15913

Abstract

Time series data is data that is prone to autocorrelation. Autocorrelation is a violation of assumptions in Ordinary Least Square regression. The presence of autocorrelation can make parameter estimates, not BLUE (Best, Linear, Unbiased Estimator). Several methods to overcome autocorrelation include Cochrane-Orcutt and Hildreth-Lu methods. Therefore, this study aimed to compare the Cochrane-Orcutt and Hildreth-Lu methods to deal with autocorrelation in the time series regression of the Gorontalo Human Development Index case in 2010 2021. We used HDI data for Gorontalo Province from 2010-to 2021, taken from the BPS-Statistics Indonesia Gorontalo Province. The method we used was Cochrane-Orcutt and Hildreth-Lu in the case of regression using Ordinary Least Squares (OLS) parameter estimation. The results obtained are that the Cochrane-Orcutt and Hildreth-Lu could overcome autocorrelation. The results of the Durbin Watson test after using both methods show no autocorrelation. However, the Hildreth-Lu method resulted in a lower Root Mean Square Error (RMSE) of 0.147 compared to the RMSE of the OLS model of 0.165 and the RMSE of the Cochrane-Orcutt model of 0.196. Therefore, the Hildreth-Lu method was the best method to overcame autocorrelation in this case.
Corn Production Exploration of Central Sulawesi Using Multiplicative Winter Model Putera, Fachruddin Hari Anggara; Amelia, Rezi; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 2 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i2.15943

Abstract

Corn is a very important food ingredient after rice. Central Sulawesi corn production data is in the form of time series data which every year in certain months increases or decreases in production. Therefore, the method that can be used for forecasting is the winter multiplicative method. This study aims to build the best model for forecasting corn production in Central Sulawesi using the winter multiplicative method. The results of this study are used to explore corn production for the next period. Modeling is done by selecting the best combination of parameters and the best combination of model parameters is obtained with a mean absolute percentage error (MAPE) of 18% with a value of α = 0,5; γ = 0,1; and β = 0,1. The data plot of the forecasted corn production shows fluctuations which indicate seasonal factors and trends in it
Modeling of Poverty Level in Central Sulawesi Using Nonparametric Kernel Regression Analysis Approach Sakinah, Nur; Nurfitra; Ihlasia, Nurmasyita; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.15743

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.
Unpacking Outlier with Weight Least Square (Implemented on Pepper Plantations Data) Prasetya, Rizka Pradita
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.16138

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.
Classifiying The Factors Influencing The Human Development Index in Riau Province using Principal Component Analysis Erda, Gustriza; Mega Aulia, Sartika; Erda, Zulya
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.16203

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.
Implementation of Etlingera Elatior for Unique Branding of Central Sulawesi Batik Motif Ikram; Abdi; Mutmainna, Nurul; Khasmawati, Julia; Wahyuli, Diana; Sudarsana, I Wayan; Junaidi
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.16240

Abstract

Batik is the art work of the Indonesian people which is a cultural heritage from their ancestors which has become one of the world's recognized cultural heritages. Batik itself has a variety of patterns that are influenced by the customs of the local community and contains deep meaning and philosophy. Endemic flora and fauna are often used as patterns for batik motifs. In the process of forming batik motifs, mathematical knowledge is often required which sometimes appears naturally. Mathematics that is closely related to culture is called ethnomathematics as a branch of mathematics. Ethnomathematics can be used in forming batik patterns, especially fractal forms. A fractal shape is an object that appears to have a symmetric self-resemblance to one another when viewed at a certain scale and is the smallest part of the overall structure of the object. The purpose of this research is to make fractals of local batik motifs from Central Sulawesi using the endemic plant of Bunga Katimong (Etlingera Elatior) with the help of the j-Batik application so that new motifs are obtained to add to the diversity of existing batik motifs. The new batik motifs produced in this research are Katimong, Kantan, Kincung and Honje.

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