cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota adm. jakarta timur,
Dki jakarta
INDONESIA
Jurnal Aplikasi Statistika & Komputasi Statistik
Published by Politeknik Statistika STIS
ISSN : 20864132     EISSN : 26151367     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Redaksi menerima karya ilmiah atau artikel penelitian mengenai kajian teori statistika dan komputasi statistik pada bidang ekonomi dan sosial dan kependudukan, serta teknologi informasi. Redaksi berhak menyunting tulisan tanpa mengubah makna subtansi tulisan. Isi jurnal Aplikasi Statistika dan Komputasi Statistik dapat dikutip dengan menyebutkan sumbernya.
Arjuna Subject : -
Articles 143 Documents
 11   12   13   14   15 
Performance Study of Prediction Intervals with Random Forest for Poverty Data Analysis Valentika, Nina; Notodiputro, Khairil Anwar; Sartono, Bagus
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.542

Abstract

Introduction/Main Objectives: Determine the prediction interval with for analyzing poverty data at the Regency/City level in Indonesia. Background Problems: Poverty will be a topic in various discussion and debates in the future. Novelty: This study’s methods for constructed prediction intervals are LM, Quant, SPI, HDR, and CHDR. This method can improve the prediction interval performance with Random Forests. Research Methods: The method for building forests and obtaining BOP in this study is CART with the LS splitting rule. Finding/Results: The results of this study are that the best method for one replication is HDR with 500 trees. The best method for 100 repetitions is LM. Based on hypothesis testing, there is sufficient evidence to say no difference between the LM, SPI, Quant, HDR, and CHDR methods for 100 replications at a 5% significance level.
Metode Hutan Ordinal untuk Klasifikasi Desa Sesuai dengan Status Indeks Desa Membangun Sirodj, Dwi Agustin Nuriani; Khairil Anwar Notodiputro; Bagus Sartono
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.547

Abstract

Metode hutan acak merupakan metode klasifikasi berbasis pohon yang cukup populer. Metode ini jika diterapkan pada peubah respon ordinal akan memberikan hasil yang mengubah karakteristik dari data ordinal itu sendiri. Dalam tulisan ini akan dibahas kinerja dari metode hutan ordinal dan hutan ordinal Naïve untuk klasifikasi status berbagai desa sesuai dengan Indeks Desa Membangun (IDM) di kabupatenTasikmalaya dan Indramayu. Hasilnya memperlihatkan bahwa untuk Kabupaten Tasikmalaya kinerja metode hutan ordinal mengungguli kinerja hutan ordinal Naïve dengan rataan akurasi sebesar 73.8% dan rataan nilai kappa sebesar 0.18 sedangkan pada Kabupaten Indramayu kinerja metode hutan ordinal naïve yang justeru mengungguli metode hutan ordinal dengan rataan akurasi sebesar 51.6 % dan rataan nilai kappa sebesar 0.162. Selain itu ditunjukkan pula bahwa peubah yang penting dalam proses klasifikasi status IDM di Kabupaten Tasikmalaya dan Kabupaten Indramayu adalah peubah Jumlah Koperasi dan Jarak terdekat ke rumah sakit.
Geographically Weighted Lasso Method in Modeling the Gross Regional Domestic Product of the Bali-Nusra Region Hairunnisa, Hairunnisa; Hadijati, Mustika; Fitriyani, Nurul
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.576

Abstract

Indonesia's Central Bureau of Statistics announced that economic growth in 2020 is still in the negative zone, and the group of provinces in the Bali-Nusra region has the most negligible impact on economic growth. The value of Gross Regional Domestic Product (GRDP) measures Indonesia's economic growth. GRDP is the total added value all regional business units generate at a particular time. This research aims to apply and interpret the results of the Geographically Weighted Lasso (GWL) method for GRDP in the Bali-Nusra region. The GWL method further develops the Geographically Weighted Regression (GWR) approach by adding the Least Absolute Shrinkage and Selection Operator (LASSO) method. The GWL method simultaneously selects insignificant variables by reducing the value of the regression coefficient to zero using the LASSO method. The data used has the effect of spatial heterogeneity and multicollinearity, a prerequisite for modeling with the GWL method. Based on the analysis conducted, there are 41 different GRDP models for each district/city in the Bali-Nusra region. The resulting GWL model provides a coefficient of determination of 95.84 % so that the resulting model can be used and is considered valid.
Application of Geographically Weighted Logistic Regression in Modeling the Human Development Index in East Java Saifudin, Toha; Panjaitan, Leni Sartika; Falasifah, Sabrina; Yan Dwi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.587

Abstract

Main Objectives: pinpoint the factors influencing HDI, taking into consideration location and spatial factors. Problems: The Human Development Index (HDI) in East Java often fails to reflect actual conditions accurately, as disparities exist among districts and cities, with some falling below government expectations. Novelty: GWLR extends logistics regression by incorporating spatial factors, allowing for the identification of regional differences and influential factors affecting HDI based on actual data. Methods: To address this issue, the Geographically Weighted Logistic Regression (GWLR) method is employed. The independent variables used are Expected Years of Schooling (X1), Open Unemployment Rate (X2), and Morbidity Rate ( X3) in 2021, while dependent variable is the Human Development Index (Y). Finding/Results: The study reveals that GWLR provides a superior model compared to Ordinary Logistic Regression, indicated by a lower Akaike Information Criterion (AIC) of 28.72. Additionally, the GWLR model with Fixed Gaussian Kernel weights outperforms other weighting methods. At 90% confidence level, the significant variables influencing HDI are expected years of schooling (X1) and the open unemployment rate (X2). Given the relatively low HDI in Indonesia, the East Java Government should focus on improving these key areas to enhance HDI across districts and cities in the region.
Geographically Weighted Poisson Regression for Modeling the Number of Maternal Deaths in Papua Province Saifudin, Toha; Nur Rahmah Miftakhul Jannah; Risky Wahyuningsih; Gaos Tipki Alpandi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.598

Abstract

Introduction/Main Objectives: Maternal Mortality Rate (MMR) in Indonesia is one of the main focuses in achieving the third Sustainable Development Goals (SDGs) in 2030. Background Problems: The Central Statistics Agency states that the MMR in Papua Province is the highest, reaching 565. Novelty: Given the diverse geographical conditions of each district/city in Papua Province, an analysis was carried out. Research Methods: Using the Geographically Weighted Poisson Regression (GWPR) method with the response variable being maternal mortality rates and variables predictors of health, social, and environmental factors. Finding/Results: Fixed Gaussian kernel GWPR is the best model with an AIC value of 27.6. Variable significantly influencing MMR include the percentage of households with access to adequate sanitation, the number of recipients of food assistance programs, and the number of doctors.
Pemodelan Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) pada Kasus HIV di Indonesia Djuraidah, Anik; Anisa, Rahma; Ristiyanti Tarida, Arna; Alwi Aliu, Muftih; Septemberini, Cintia; Putri Astrini, Yufan Putri Astrini; Tasya Meilania, Gusti
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.608

Abstract

In general, spatial regression is used to model one of the spatial effects, namely spatial dependency or heterogeneity. For the effects of spatial dependencies, the models that have been used frequently follow Elhost's taxonomy, with the spatial dependencies being on the response, predictor, or error. Whereas for the effect of spatial heterogeneity generally use geographically weighted regression models (GWR) or if there are global predictors use mixed geographically weighted regression (MGWR). The data used in this study are cases of Human Immunodeficiency Virus (HIV) per 100,000 population as a response variable, and key populations, positive cases in pregnant women, tuberculosis patients, poverty rate, and unemployment rate as predictors. In the data used, there are spatial dependencies and heterogeneity. The MGWR-SAR is a model that can be used if the data has both spatial effects. This study aims to determine the factors influencing HIV cases in districts/cities in Indonesia using a spatial model. The results showed that the combined model of GWR and spatial autoregressive regression (SAR) was the best model. Key population explanatory variables have a global and significant influence. Other explanatory variables that have local influence are positive cases in pregnant women, tuberculosis patients, poverty rates, and unemployment rates.
Digital Literacy in Mediating the Influence of Education, Demography, Employment on Poverty Liswanda, Satria; Oktavia, Rini; Zuhra, Rahma
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.666

Abstract

This study investigated the influence of education, demography, and employment on poverty with digital literacy as a mediating variable. A structural Equation Modeling (SEM) with the Partial Least Square (PLS) method was applied. Significant indicators found are four indicators of education and digital literacy variables, two indicators of demographic and employment variables, and three indicators of poverty variables. It was found that education and employment variables had a significant influence on poverty with a negative influence. We found that no variable has a significant effect on digital literacy and there is no significant effect of digital literacy on Poverty.
Modeling the Stunting Prevalence Rate in Indonesia Using Multi-Predictor Truncated Spline Nonparametric Regression Alda Fuadiyah Suryono; Kurniawan, Ardi; Widyangga, Pressylia Aluisina Putri; Dewanti, Maria Setya
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.719

Abstract

Introduction/Main Objectives: Stunting is the impaired growth and development that children experience from poor nutrition, repeated infection, and inadequate psychosocial stimulation. Background Problems: Based on data from the National Nutrition Status Survey (SSGI) in 2022, the prevalence of stunting in Indonesia was 21.6%, which is still above the WHO standard of below 20%. Novelty: This study was conducted with the aim of analysing the factors that influence the stunting prevalence rate in Indonesia using multi-predictor truncated spline nonparametric regression. Research Methods: The research data is secondary data taken from Health Statistics 2022 with response variables in the form of stunting prevalence. Finding Result: Based on the analysis, the best model to model the stunting prevalence rate is a multi-predictor truncated spline with three knots. In addition, it was found that four predictor variables which are the percentage of infants under 6 months old receiving exclusive breastfeeding, the average age of a mother's first pregnancy, the percentage of married women aged 15-49 using contraception, and the percentage of mothers who gave birth to a live child in the past two years and initiated early breastfeeding had a significant effect simultaneously and partially on the stunting prevalence rate in Indonesia.
Patterns, Determinants, and Elasticity of Household Food Consumption in Indonesia (Period 2021-2022) Aulia, Wifa Darma; Yuliana, Rita
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.652

Abstract

Introduction/Main Objectives: The increase in strategic food commodity prices significantly contributed to inflation, with the food, beverage, and tobacco category reaching 3.59% in March 2022. This price hike reduced household purchasing power, affecting welfare. This research examines how rising food prices impact household food consumption patterns in Indonesia. Background Problems: This study explores the effects of rising food prices on household consumption patterns. It addresses two key questions: What are household food consumption patterns, and what factors influence them? What is the elasticity of food demand in Indonesia between March 2021 and March 2022? Novelty: The study’s novelty lies in calculating food demand elasticity using demand theory assumptions, ensuring reliable results—unlike many studies that overlook these assumptions. Research Methods: The study uses the Linear Approximated-Almost Ideal Demand System (LA-AIDS) model with the Seemingly Unrelated Regression (SUR) method. Findings/Results: The results show that rising food prices in March 2022 changed household food consumption patterns. Own-price elasticity was negative, indicating reduced demand. Cross-price elasticity varied, with some food groups showing negative and others positive effects. All food groups were classified as normal goods based on expenditure elasticity.
The Utilization of Model Output Statistic (MOS) in Improving Weather Prediction Model Accuracy of Integrated Forecasting System (IFS) Isnaini Anjelina Ramadhan; Deni Septiadi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i2.732

Abstract

Introduction/Main Objectives: Integrated Forecasting System (IFS) is one of the most accurate numerical weather prediction (NWP) model for Indonesia region. Background Problems: However, in fact, each model always has bias potential against observation which causes inaccuracy in weather prediction. Novelty: This research intends to overcome this problem by building a weather prediction model based on Model Output Statistic (MOS) to minimize bias and improve NWP accuracy. Research Methods: Provide an outline of the research method(s) and data used in this paper. Explain how did you go about doing this research. Again, avoid unnecessary content and do not make any speculation(s). Finding/Results: Analysis result states that compared to IFS, MOS fluctuation pattern is more relevant to observation. MOS has higher correlation to observation and lower error. However, the variance of observation value tends to be better represented by IFS. The test result of heavy rain cases prove that the application of MOS is able to provide fairly accurate prediction. This weather prediction will be able to be the basis for decision-making and preventive measure in dealing with extreme condition that may occur.

Page 13 of 15 | Total Record : 143

 11   12   13   14   15 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics Vol 15 No 1 (2023): Journal of Statistical Application and Computational Statistics Vol 14 No 2 (2022): Journal of Statistical Application and Computational Statistics Vol 14 No 1 (2022): Jurnal Aplikasi Statistika dan Komputasi Statistik Vol 13 No 2 (2021): Jurnal Aplikasi Statistika dan Komputasi Statistik Vol 13 No 1 (2021): Jurnal Aplikasi Statistika dan Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus Vol 12 No 2 (2020): Journal of Statistical Application and Computational Statistics Vol 12 No 1 (2020): Journal of Statistical Application and Computational Statistics Vol 11 No 2 (2019): Journal of Statistical Application and Computational Statistics Vol 11 No 1 (2019): Journal of Statistical Application and Computational Statistics Vol 10 No 2 (2018): Journal of Statistical Application and Computational Statistics Vol 10 No 1 (2018): Journal of Statistical Application and Computational Statistics Vol 9 No 2 (2017): Journal of Statistical Application and Computational Statistics Vol 9 No 1 (2017): Journal of Statistical Application and Computational Statistics Vol 8 No 2 (2016): Journal of Statistical Application and Computational Statistics Vol 8 No 1 (2016): Journal of Statistical Application & Statistical Computing Vol 7 No 2 (2015): Journal of Statistical Aplication and Statistical Computing Vol 7 No 1 (2015): Journal of Statistical Application and Computational Statistics More Issue