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Spline Nonparametric Regression Model for Local Revenue in Central Sulawesi Fajri, Mohammad; Wulansari, Eka Rizky; Anggraeni, Ayu; Annisa, Mufitatul
Parameter: Journal of Statistics Vol. 1 No. 2 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Local Own-source Revenue (LOR) is all regional revenue that comes from the region's original economic resources. It is very important to identify it by researching and determining the Regional Local Own-source Revenue (LOR) by properly researching and managing the source of revenue so as to provide maximum results. Central Sulawesi Province itself has Local Own-source Revenue (LOR) in the Regional Revenue and Expenditure Budget of the 2018 Budget Year has reached Rp1 trillion. The increase or decrease in growth of local revenue is influenced by the amount and type of tax, levies collected by local governments and the lack of incentives for the management apparatus to carry out tax collection and levies. This study uses spline regression analysis because the data of the Local Own-source Revenue (LOR) in Central Sulawesi in 2018 does not have a pattern so that it fits perfectly with that method. Then after processing the data obtained the results of spline nonparametric regression modeling using the optimal knots point obtained from the minimum GCV value. The best spline nonparametric regression model is written as follow . It can be concluded that in Central Sulawesi in 2018 the lowest Local Own-source Revenue (LOR) value was Banggai Laut Regency with 21,776 billion rupiahs and the highest Local Own-source Revenue (LOR) value was Palu City at 267,402 billion rupiahs.

Fuzzy Clustering Algorithm to Catching Pattern of Change in District/City Poverty Variables Before and The Beginning of The Covid-19 Pandemic in Sulawesi Island Novidianto, Raditya; Irfani, Rini
Parameter: Journal of Statistics Vol. 1 No. 2 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

The first goal of the SDGs is to end poverty in any form. The COVID-19 pandemic has greatly affected several economic indicators, especially absolute poverty, especially in Sulawesi Island, which has increased poverty indicators, leading to the movement of values between districts/cities. The grouping will show similar characteristics of absolute variable poverty. By the Fuzzy method clustering, each observation has a degree of membership so that from the degree of membership can be identified which areas have vulnerable to move from one cluster to another. Grouping using fuzzy algorithms will get an overview of districts of concern to the government during the pandemic so that the variable indicators of absolute poverty do not worsen due to the pandemic. Comparison with the absolute variables of poverty in 2019 and 2020 in the headcount index (P0), Poverty Gap Index (P1), and Poverty Severity Index (P2) in districts/cities on the island of Sulawesi based on silhouette coefficients shows that optimum clusters formed as many as 2 clusters, with a coefficient of 0.57 and 0.60 respectively. Cluster 1 has characteristics including areas with absolute poverty rates that tend to be more prosperous than cluster 2 in the 2019 and 2020 data groups on the island of Sulawesi. The fuzzy algorithm detects areas prone to displacement from cluster 1 to cluster 2, namely Bombana, Bone, Sangihe Islands, South Konawe, and Siau Tagulandang Biaro in 2019 and Bombana, Bone, Sangihe, and Maros Islands in 2020. The COVID-19 pandemic in March 2020 has not had much impact on the macro indicators of poverty seen in the transfer of membership from 2019 to 2020, which only occurred to 3 districts that changed, namely bolaang mongondouw and konawe selatan from cluster 1 to cluster 2 and Maros from cluster 2 to cluster 1.

Implementation of Constant Elasticity of Substitution Function and Data Envelopment Analysis: Productivity and Technical Efficiency of Large Industry Creative Economy in Craft Subsector Indonesia Zuhoran, Khusnudin Tri Subhi; Budiasih
Parameter: Journal of Statistics Vol. 1 No. 2 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Indonesia has undergone a transformation from agriculture to the manufacturing industry in 1971-1997. In the 2001-2015 period there was de-industrialization in developed countries. Likewise, Indonesia which shows a decrease in the contribution of GDP and manufacturing sector workforce to the economy. However, deindustrialization in Indonesia is premature / too fast. One of the policies to overcome this problem is with attention to the creative industry. With the existence of the creative industry, it can achieve several SDGs goals, namely the 8th goal, namely decent work and economic growth, the creative economy can increase economic growth, create jobs and increase exports (UNCTAD, 2010). However, the creative industry GDP growth that is below the RPJMN target is thought to indicate low productivity of the creative industry. According to IBS data, the craft sub-sector creative industry has a low production value. To see productivity can use technical efficiency. This study aims to evaluate the technical efficiency of creative industry in the craft sub-sector as well as to analyze the trend of variables that affect technical judgments. The method used is the CES production function and Data Envelopment Analysis. The results show that industries that have high total production factor values have a tendency to have high technical efficiency values as well. Furthermore, those arranged according to technical categories and binary logistic regression are used to determine trend variables that affect technical judgments. The technical efficiency of the craft, significantly by wages, investment status and company scale.

Expenditure Per Capita Model with Spatial Small Area Estimation Risal, Ahmad
Parameter: Journal of Statistics Vol. 1 No. 2 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Indonesia is one of many countries around the world that attempt to suffer from high poverty rates. Since, poverty information in a certain area is a point of interest to researchers and policy makers. One problem faced is for the development program to be carried out more effectively and efficiently, it is necessary to have data availability up to the micro-scale. The technique used to reach the goal is Small Area Estimation (SAE). Fay-herriot (FH) model is one method on Small Area Estimation. Since, the SAE techniques require â€œborrow strengthâ€ across neighbor areas so thus Fay-Herriot model approach was developed by integrating spatial information into the model. This method known as Spatial Fay-Herriot Model (SFH) or Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). This study aims to compare MSE of direct estimation, FH, and SFH Model to see which method gives the best result in estimating expenditure. The MSE value of the estimated SFH is smaller than direct estimation and FH, but it does not significant. It means adding spatial information in the small area estimation model does not give a better prediction than the simple small area estimation which is takes account the area as a specific random effect.

Application Biplot Analysis on Mapping of Non-Convertive Diseases in Indonesia Suryowati, Kris; JP, Maria Titah; Nasution, Nurzaidah
Parameter: Journal of Statistics Vol. 1 No. 2 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Non-communicable diseases is diseases that are not caused by germs but rather because of physiological or metabolic problems in human body tissues. Usually, this disease occurs due to unhealthy lifestyle. One way to find out how large the spread of non-communicable diseases is by mapping the disease using biplot analysis. Biplot analysis is applied to determine the proximity information between objects, the length of the change vector, the correlation between modifiers, and the value of the change in an object. The study was conducted in 33 provinces with twelve non-communicable diseases. Descriptive analysis of twelve non-communicable diseases averaged the highest joint disease of 10.51 followed by hypertensive disease 8.85 and Stroke 6.42. While the lowest average disease is Heart Failure disease by 0.10 it is still open to research with other methods and also need to add supporting variables

Forecasting the Consumer Price Index in Yogyakarta by Using the Double Exponential Smoothing Method Febriyanti, Syintya; Pradana, Wahyu Aji; Muhammad, Juliana Saputra; Widodo, Edy
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.i1.15641

The Consumer Price Index (CPI) is an indicator that is often used to measure the inflation rate in an area, or can be interpreted as a comparison between the prices of a commodity package from a group of goods or services consumed by households over a certain period time. The spread of COVID-19 throughout the world affects the economy in Indonesia, especially Yogyakarta. Forecasting CPI data during the COVID-19 pandemic has the benefit of being an illustration of data collection in the CPI of D.I Yogyakarta Province in the predicted period. This is useful as a comparison with the original data at the time of data collection and publication, as well as a consideration in making policies and improving the economy. Researchers use the Double Exponential Smoothing (DES) method to predict the CPI of Yogyakarta D.I Province, which aims to determine the best forecasting model and forecasting results. This method is rarely used in research on CPI data forecasting in Yogyakarta. The data in this study are monthly data from March 2020 to August 2021. The highest CPI in Yogyakarta occurred in August 2021 at 107.21 or 107.2, while the lowest CPI in Yogyakarta occurred in April 2020 at 105.15 or 105.2. The average CPI in Yogyakarta per month is 106.1. The Mean Absolute Percentage Error (MAPE) value obtained from the DES method is 0.1308443%, so that the accuracy of the model is 99.869%. Forecasting with the DES method is quite well used in forecasting the CPI data of Yogyakarta in September 2020 - November 2021. The results of CPI forecasting in Yogyakarta using the DES method were 107.2602, 107.3104, and 107.3606 from September-November.

K-Means Clustering for Grouping Indonesia Underdeveloped Regions in 2020 Based on Poverty Indicators Resti Wahyuni
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.i1.15675

Poverty is still a problem in Indonesia, especially in underdeveloped areas. Underdeveloped areas are areas where the region and its people are less developed than other regions on a national scale. The classification of disadvantaged areas is determined by the president in the Presidential Regulation of the Republic of Indonesia Number 63 of 2020 concerning the Determination of Underdeveloped Regions of 2020-2024. Various policies need to be set by the government to overcome poverty in underdeveloped areas. Program planning strategies may be different for each region. Therefore, in order to achieve an optimal implementation of poverty alleviation programs, it is necessary to group the districts covered in underdeveloped areas in Indonesia based on poverty indicators. The data used is macro data from the characteristics of each region in disadvantaged areas obtained from regional publications in the figures for each district. From the results of the analysis of k means clustering formed three groups with different characteristics in each cluster. In cluster one, the focus of government policies is on employment and sanitation aspects, cluster two is on health, education, and employment aspects, cluster three is on all aspects because cluster three is the area with the highest percentage of poor people compared to the other two clusters. The high percentage of poor people is also followed by other poor aspects.

Individual and Contextual Factors Affecting DPT Immunization in Indonesia Resti Wahyuni; Titik Harsanti
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.i1.15677

Nowadays, diphtheria cases always increase from year to year. Until now, no drug has been found to cure diphtheria, but there is the most effective way of prevention through immunization. It is known that diphtheria sufferers who donâ€™t get immunizations increase every year. The purpose of this study is to determine the individual and contextual factors that influence the status of DPT immunization in Indonesia and its trends and to know the diversity between cities. The data used in this study are Susenas KOR and consumption and expenditure (KP) modules. The results of multilevel binary logistic regression analysis indicate that individual factors that influence the status of DPT immunization are residence classification, highest maternal education, ownership of immunization cards, birth order, and household poverty status. While the contextual are the ratio of posyandu to 100,000 population and PDRB. Characteristics of children aged 12-59 who do not get immunizations tend to live in rural areas, have mothers with the highest education in junior high school, donâ€™t have immunization cards, who born late in households with many children, and come from poor households. Besides that, there is a diversity of characteristics between cities, which amounted to 22,19%.

Forecasting of the Amount of Rupiah Banknotes Flows in the East Region of Indonesia Using Circular Regression Jassinca Chrissma Audina; Rais; 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.i1.15681

Money is a tool that can be used in exchanging goods and services in a certain area. Increasing and decreasing in the money supply excessively can have a negative impact on the economy. For this reason, in order to maintain financial system stability in Indonesia, it is necessary to conduct an analysis of the data on the amount of outflows of rupiah currency at each Bank Indonesia office. In this study, a relationship analysis will be carried out between the eastern region of Indonesia and the amount of outflows of Bank Indonesia banknotes during the 2016-2018 period using circular regression analysis. The results showed that 83.03% of the variation in the amount of outflows of BI banknotes could be explained by the circular regression model that was formed. In addition, in the process of forecasting data on the amount of outflows of BI banknotes in the eastern region of Indonesia for the 2019-2020 period, the time series forecasting method is used which is based on the use of analysis of the relationship pattern between the estimated variables and the time variable.

Analysis of Skin Disease Infection After the Palu Earthquake Using Binary Logistic Regression Wahyuni, Selvia Anggun; Lilies Handayani; Muhammad Akriyaldi Masdin; Salmia
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.i1.15682

The incidence of skin disease in Indonesia is still relatively high and is a significant problem. This is evidenced by the 2010 Indonesian Health Profile data which shows that skin and subcutaneous tissue diseases are the third rank of the 10 most common diseases among outpatients in hospitals throughout Indonesia. Skin disease is growing, as evidenced by data from the Indonesian Ministry of Health, the prevalence of skin disease throughout Indonesia in 2012 was 8.46%, then increased in 2013 by 9 %. Palu City is an area that has a high skin disease problem. According to the 2016 BPS of Palu City, skin diseases are among the top 10 diseases in Palu City with a total of 11,363 sufferers. The method used in this research is binary logistic regression. Based on the analysis that has been done, it can be concluded that the best model is formed as follows:. Based on the best model, it is found that the factors that influence the transmission of skin diseases after the Palu earthquake are genetic factors.

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Iman Setiawan

Contact Email
npl.untad@gmail.com

Phone
+6281282206923

Journal Mail Official
jparameter.untad@gmail.com

Editorial Address
Jl. Soekarno Hatta No.KM. 9, Tondo, Mantikulore,Kota Palu, Sulawesi Tengah 94119

Location
Kota palu,

Sulawesi tengah

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Iman Setiawan

Contact Email npl.untad@gmail.com

Phone +6281282206923

Journal Mail Official jparameter.untad@gmail.com

Editorial Address Jl. Soekarno Hatta No.KM. 9, Tondo, Mantikulore,Kota Palu, Sulawesi Tengah 94119

Location Kota palu, Sulawesi tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Iman Setiawan

Contact Email
npl.untad@gmail.com

Phone
+6281282206923

Journal Mail Official
jparameter.untad@gmail.com

Editorial Address
Jl. Soekarno Hatta No.KM. 9, Tondo, Mantikulore,Kota Palu, Sulawesi Tengah 94119

Location
Kota palu,

Sulawesi tengah

INDONESIA