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Inferensi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 0216308X     EISSN : 27213862     DOI : http://dx.doi.org/10.12962/j27213862
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences
The aim of Inferensi is to publish original articles concerning statistical theories and novel applications in diverse research fields related to statistics and data science. The objective of papers should be to contribute to the understanding of the statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims; and any approach in data science. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where the original methodology is involved and original contributions to the foundations of statistical science. It also sometimes publishes review and expository articles on specific topics, which are expected to bring valuable information for researchers interested in the fields selected. The journal contributes to broadening the coverage of statistics and data analysis in publishing articles based on innovative ideas. The journal is also unique in combining traditional statistical science and relatively new data science. All articles are refereed by experts.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 8 Documents
 1 
Search results for , issue "Vol 7, No 2 (2024)" : 8 Documents clear
Implementation of Spatial Autoregressive with Autoregressive Disturbance (SARAR) using GMM to Identify Factors Caused Poverty in West Java Ningtias, Yunita Dwi Ayu; Andriyana, Yudhie; Pravitasari, Anindya Apriliyanti
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20309

Abstract

Poverty is one of the crucial problems that has a negative impact on all sectors. As a developing country, Indonesia has a fairly high poverty rate. The government's efforts to overcome the problem of poverty can be circumvented by detecting the factors that influence it to determine the policies taken by using statistical modeling. There is a spatial effect on poverty in West Java Province. Spatial Data Analysis is the only statistical model that can explain the relationship between an area and the surrounding area. If the response variable contains a lag that correlates with each other, it is called a Spatial Autoregressive with Autoregressive Disturbances (SARAR) model. The Generalized Method of Moment (GMM) approach is used to get an estimator from the model. This method is applied to obtain the factors that influence poverty in West Java Province. The results of this study indicate that the GMM SARAR poverty modeling with customized weights provides relatively better estimation results. In addition, the relationship between locations (spatial lag dependence) is positive and significant. Expected Years of Schooling and Per capita Expenditure have a negative and significant effect on the increase in the percentage of poor people in West Java.
Nonparametric Regression Modeling with Multivariable Fourier Series Estimator on Average Length of Schooling in Central Java in 2023 Ni'matuzzahroh, Ludia; Dani, Andrea Tri Rian
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20219

Abstract

One of the benchmarks to see the quality of education and human resources in Indonesia is the average length of schooling. If the school average is higher, it can positively impact Indonesian society, enabling it to compete globally. There are several factors, both economic and educational factors, that influence the low average length of schooling in Central Java Province. Therefore, this research aims to model and determine what variables can influence the average length of schooling in Central Java in 2023 using a nonparametric regression approach with a multivariable Fourier series estimator. This approach is used when the form of the relationship pattern is unknown and tends to have recurring patterns. The Fourier series estimator depends on the number of oscillations, so in this study, 1 to 4 oscillations were tried, where the minimum GCV value determined the optimal oscillation. The best model was obtained on the analysis results, producing the smallest GCV value, namely the model with 3 oscillations with a GCV value of 1.027. The results of simultaneous and partial hypothesis testing showed that all predictor variables used in this research were proven to influence the Average Length of Schooling. This is also supported by the coefficient of determination value of 85.464%.
Comparison of Logistic Regression and Support Vector Machine in Predicting Stroke Risk Safitri, Lensa Rosdiana; Chamidah, Nur; Saifudin, Toha; Firmansyah, Mochammad; Alpandi, Gaos Tipki
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20420

Abstract

The issue of health is the third goal of Indonesia's Sustainable Development Goals (SDGs) which is state to ensuring a healthy life and promoting prosperity for all people at all ages. One of the SDGs’s concerns is deaths caused by non-communicable diseases (NCDs) including strokes. One prevention that can be done is by making a prediction of stroke for early detection. There are various methods available which are statistical methods and machine learning methods. In this research work, we aim to compare the two methods based on statistical method and machine learning method on stroke risk prediction. The data used in this research is primary data from Universitas Airlangga Hospital (RSUA) from June until August 2023. In this research, we compare the statistical method that is Logistic Regression (LR), and the machine learning method which is Support Vector Machine(SVM). We use Phyton to analyze all methods in this research. The results show that SVM with Radial Basis Kernel is better than LR in predicting stroke risk based on three goodness criteria namely sensitivity, F-1 score and accuracy where these three goodness criteria values of SVM are greater than those of LR.
Risk Analysis Forecasting Models of Poisson Regression, Negative Binomial Regression, Poisson GSARIMA, and Negative Binomial GSARIMA (Case Study: Number of Bicycle Sales) Pramujati, Windya Harieska
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20255

Abstract

The Poisson model is a model that can be applied to count data, where in this research the case study used is the number of bicycle sales. However, there is an equidispersion assumption in the Poisson model, that the response variable has the same mean and variance. A more flexible model is needed if the equidispersion assumption is not met, namely the Negative Binomial model. In this research, two models were applied, namely the regression model and the GSARIMA model, with two different distributions, namely the Poisson distribution and the Negative Binomial distribution. Therefore the models that will be compared are the Poisson Regression, Negative Binomial Regression, Poisson GSARIMA, and Negative Binomial GSARIMA models. The differences in results for each model are due to errors that occur in each model used. Hence, a model with a smaller error can be said to be a model that has a smaller risk than other models. The results of this study show that the error rate in the Negative Binomial GSARIMA ZQ1 model is relatively smaller than other models with a value of AIC = 1058.7. This model is the best model that can be used as a forecasting model in the case of bicycle sales and can minimize the risk of error in a forecasting result.
Ensemble Cluster Method For Clustering Cabbage Production In East Java Maghfiro, Maulidya; Wardhani, Ni Wayan Surya; Iriany, Atiek
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20378

Abstract

Cluster analysis is a multivariate analysis method classified under interdependence methods, where explanatory variables are not differentiated from response variables. The methods used include hierarchical cluster analysis, such as agglomerative and divisive, and non-hierarchical methods such as Self Organizing Maps (SOM) based on Artificial Neural Networks (ANN). Various cluster analysis methods often yield diverse solutions, making it challenging to determine the optimal solution. Therefore, the ensemble cluster method is employed to combine various clustering solutions without considering the initial data characteristics with providing better results. One case study of clustering is the grouping of cabbage production. East Java Province has become the third-highest cabbage-producing province in Indonesia with a production of 210,454 tons. Clustering of cabbage-producing regencies/cities was conducted to optimize production and identify areas that have not yet reached their maximum potential. This study compares five clustering methods which are hierarchical analysis (complete linkage, single linkage, average linkage), Self-Organizing Map (SOM), and Ensemble Cluster. The quality of clustering was evaluated using the Silhouette Coefficient (SC), Dunn Index (DI), and Connectivity Index (CI). The results indicate that the Ensemble Cluster method showed the best performance, with an SC value of 0.9124, a DI value of 1.3734, and a CI value of 2.9290, indicating excellent cluster separation. Therefore, the ensemble cluster method is recommended as the best clustering method in this study.
Modeling the Percentage of Tuberculosis Cure in Indonesia Using a Multivariate Adaptive Regression Spline Approach Novianti, Dita Aris; Marwanda, Nadia Dwi; Saifudin, Toha; Suliyanto, Suliyanto
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20344

Abstract

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium Tuberculosis. After India, Indonesia is the country with the second highest number of TB sufferers in the world. TB prevention efforts in Indonesia have been carried out, even since 1995. However, in general, 2006-2022 the TB cure in Indonesia tends to experience a downward trend. Therefore, it is important to know what variables have a significant effect and how the pattern relates to the percentage of TB cures. We urgently need this information to optimize TB handling efforts and achieve Sustainable Development Goals (SDGs) point 3, which focuses on good health and well-being. For that purpose, this study used the Multivariate Adaptive Regression Spline (MARS) approach. MARS is considered more flexible in overcoming cases of predictor variables that do not form a certain pattern to their response variables and can accommodate possible interactions between predictor variables. The best model was obtained at BF=18,MI=2, and MO=0 with minimum GCV value is 37.053 and R^2 is 91.6%, with significant predictor variables are food management sites meet the requirements according to standards, complete treatment, smoking population over 15 years, families with healthy latrines, and districts/municipalities implement healthy living germas policy. The significance of the nine predictors should prioritize enhancing the quality of health services for example ensuring a fair distribution of complete treatment for TB patients.
Generalized Linear Mixed Models for Predicting Non-Life Insurance Claims Saputra, Kie Van Ivanky; Margaretha, Helena; Ferdinand, Ferry Vincenttius; Budhyanto, Johana Daniella
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20447

Abstract

Generalized linear mixed models (or GLMMs) are an extension of linear mixed models to allow response variables from different distributions. Alternatively, GLMMs are an extension of generalized linear models (GLMs) to include both fixed and random effects (hence mixed models) that can be used as a modeling approach that allows the modeling of nonlinear behaviors and non-Gaussian distributions of residues. These models are very useful for general insurance claim predictions, where the frequency and the severity of claims distributions are usually non-Gaussian. In our research, we shall compare the performance of GLMS and that of GLMMS to estimate the aggregate of claims of auto insurance. The data used are a secondary dataset which is the motor vehicle dataset from Australia named ausprivauto0405. The results of our research suggest that GLMMs approach does not always give the best estimations and even in some cases GLMs outperform GLMMs. The accuracy of the models was compared to choosing the best model for determining pure insurance premiums using R software. More investigation using different models is needed to ensure which model is more appropriate for estimating the aggregate of insurance claims.
Risk Factors for Lymphatic Filariasis in Endemic Areas of Papua Using Binary Logistic Regression Based on Synthetic Minority Over-sampling Technique Simangunsong, Sri Rohmanisa; Oktora, Siskarossa Ika
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20283

Abstract

Neglected tropical diseases (NTDs), such as lymphatic filariasis (LF), are a significant issue in Indonesia. The high percentage of LF in Papua highlights the urgency of addressing LF in the area due to its devastating impact on the health and economy of the poor. Moreover, imbalanced outcome variable categories are a common issue in logistic regression analysis using medical data. One of the solutions to this problem is using Synthetic Minority Over-sampling Technique (SMOTE). Therefore, this study aims to provide an overview of LF cases in endemic areas of Papua and identify the factors that influence its occurrence using binary logistic regression analysis and the SMOTE method. The data utilized was the LF diagnosis status of individuals in endemic areas of Papua Province, Indonesia as contained in the Riset Kesehatan Dasar (Riskesdas) 2018. It was found that the SMOTE approach in binary logistic regression analysis can be used to address data imbalance. The following factors are significant: sex, age, occupation, education level, use of mosquito bite preventive measures, use of latrines for defecation, and participation in Mass Drug Administration (MDA).

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