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Dr. Muhammad Ahsan
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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 10 Documents
 1 
Search results for , issue "Vol 8, No 2 (2025)" : 10 Documents clear
Comparison of GMERF and GLMM Tree Models on Poverty Household Data with Imbalanced Categories Bukhari, Ari Shobri; Notodiputro, Khairil Anwar; Indahwati, Indahwati; Fitrianto, Anwar
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21901

Abstract

Decision tree and forest methods have become popular approaches in data science and continue to evolve. One of these developments is the combination of decision trees with Generalized Linear Mixed Models (GLMM), resulting in the GLMM Tree, which is applicable to multilevel and longitudinal data. Another model, Generalized Mixed Effect Random Forest (GMERF), extends the concept of decision forests with GLMM, effectively handling complex data structures with non-linear interactions. This study compares the performance of GLMM Tree and GMERF models in classifying poor households in South Sulawesi Province, characterized by imbalanced categories. GLMM Tree provides a simple, interpretable classification through tree diagrams, while GMERF highlights variable importance. Initial tests show all three models (GLMM, GLMM Tree, and GMERF) achieve high accuracy and specificity but exhibit low sensitivity. By applying oversampling, sensitivity and AUC are significantly improved, though this is accompanied by a decline in accuracy and specificity, revealing a trade-off. The study concludes that while GLMM, GLMM Tree and GMERF have their strengths, using them together offers a more comprehensive understanding of poverty classification. Handling imbalanced data with oversampling is effective in increasing sensitivity, but careful consideration is needed due to its impact on overall accuracy.
Variables Selection Affecting Indonesian Human Development Index Using LASSO Sunandi, Etis; Siswantining, Titin
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.22891

Abstract

According to Statistics Indonesia, the Human Development Index (HDI) is a measure that reflects the level of human development achievement in a region, based on three basic dimensions: a long and healthy life, knowledge, and a decent standard of living. There are many factors that are suspected to influence HDI in Indonesia. Another hand, estimation of parameters in regression analysis using the Least Squares Method will experience problems, if the number of independent variables is greater than the number of observations. One method that can be used to overcome this problem is to use the Least Absolute Shrinkage and Selection Operator (LASSO) method.  The purpose of this study is the selection of variables that affect Indonesia's Human Development Index (HDI) in 2023 using the LASSO. The LASSO method is known as a model used to select independent variables while overcoming multicollinearity problems. The ridge regression model is used as a comparison model. The results showed that LASSO Analysis is better than Ridge Regression. This can be seen from the Mean Squared Error of Prediction (MSEP) of LASSO (0.34) is smaller than the ridge regression (3.61). In addition, the r-squared value of LASSO is higher, which is 97.6%.
Implementing Markov Switching Regression Using Best Subset Approach For BSI Stock Price Prediction Analysis Nurdiansyah, Denny; Ma'ady, Mochamad Nizar Palefi; Wijayanti, Lulud; Novitasari, Diah Ayu; Rohmawati, Siti
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21030

Abstract

Stocks are evidence of ownership of the capital or funds of a company or institution and are represented by a document that includes the par value, the company name, and the rights and obligations described for each owner. Since so many factors affect the rise and fall of stock prices, investors should pay attention to the factors that influence the rise and fall of stock prices to avoid incurring losses or profits when buying and selling stocks. The rise and fall of stock prices can be analyzed with Markov switching regression by trying all possible placements of factors to get the best subset. Public holdings will continue to increase due to nation-building and Sharia Bank Indonesia (BRIS) stock price appreciation. This study aims to determine the impact of increases and decreases in the closing price of BSI stock. The modeling used in this study is Markov switching regression using the best subset approach. The data used in this study are secondary in the form of daily data for the closing price of Bank Syariah Indonesia shares, Inflation, BI Rate, Selling Exchange Rate, Money Supply, and Gross Domestic Product (GDP). Data are obtained from the official BPS website. The results of this study show that Markov switching regression modeling can identify the feasibility of regimes as "bull" and "bear" periods. State 2 indicates an uptrend or "bullish," and state 1 indicates a downtrend or "bearish." The best subset approach obtains the best model with the lowest SSE value. The study concluded that the statistical modeling results of  BSI stock's closing prices during "bull" and "bear" periods provide significant predictors: BI Rate, Selling Exchange Rate, and Money Supply.
Spatio-Temporal Kriging for Monthly Precipitation Interpolation in East Kalimantan Jannah, Friendtika Miftaqul; Fitriani, Rahma; Pramoedyo, Henny
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.22195

Abstract

Precipitation is one of the factors that can lead to various disasters, such as droughts and floods. Ordinary interpolation methods, such as spatial kriging, cannot accommodate the time element, which is crucial for addressing precipitation-related disasters. Therefore, this study applies a spatio-temporal kriging, which incorporates both spatial and temporal elements. The aim of this study is to develop a spatio-temporal kriging model for precipitation, serving as a basis for interpolating precipitation at unobserved points over various time intervals within the study domain. This model is expected to be an effective tool for disaster mitigation and water conservation strategies. The data used in this study comprises total monthly precipitation recorded at seven precipitation observation posts in East Kalimantan from 2021 to 2023. The findings indicate that the spatio-temporal ordinary kriging model is the most suitable approach, with the best semivariogram model identified as the simple sum-metric. The spatial semivariogram follows an exponential model, while the temporal and joint semivariograms follow Gaussian models. The accuracy of the chosen model yields an RMSE of 2493.687. The interpolation results reveal that West Kutai falls within the medium to high precipitation category, making it the district with the highest flood risk.
Constructing of Decent Work Index of Regency/City in Indonesia and its Influencing Factors Nurshauma, Fiska Alfiyya; Yuniasih, Aisyah Fitri
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21652

Abstract

Currently, there are still many workers in Indonesia who obtain low-quality or inappropriate jobs. This can be seen from inadequate wages, non-standard working hours, and low labour productivity. In fact, decent work is very important to reduce poverty and achieve sustainable development. Therefore, this study aims to develop a comprehensive measure of decent work, the Decent Work Index (DWI), for each regency/city in Indonesia. The DWI is compiled based on the ILO indicator framework using factor analysis method in accordance with the stages of index compilation by the OECD. In addition, this study also uses multiple linear regression to analyze the influence of education and the development of information and communication technology on decent work conditions. The results show that nine indicators are divided into three factors, namely full and productive work, rights at work, and equal opportunity and treatment in employment. Denpasar City is the city with the highest DWI, and Mamberamo Raya Regency is the regency with the lowest DWI. Meanwhile, the results of multiple linear regression shows that Mean Years of Schooling (MYS), the percentage of individuals using computers, and the percentage of individuals using e-commerce can increase the DWI.
Spatial Survival Analysis of Stroke Hospitalizations: A Bayesian Approach Aswi, Aswi; Poerwanto, Bobby; Hammado, Nurussyariah
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.22252

Abstract

Survival analysis encompasses a range of statistical techniques used to evaluate data where the outcome variable represents the time until a specific event occurs. When such data is collected across different spatial regions, integrating spatial information into survival models can enhance their interpretive power. A widely adopted method involves applying an intrinsic conditional autoregressive (CAR) prior to an area-level frailty term, accounting for spatial correlations between regions. In this study, we extend the Bayesian Cox semiparametric model by incorporating a spatial frailty term using the Leroux CAR prior. This approach aims to enhance the model's capacity to analyze stroke hospitalizations at Labuang Baji Hospital in Makassar, with a particular focus on exploring the geographic distribution of hospitalizations, length of stay (LOS), and factors influencing patient outcomes. The dataset, derived from the medical records of stroke patients admitted to Labuang Baji Hospital between January 2022 and June 2024, included variables such as LOS, discharge outcomes, sex, age, stroke type, hypertension, hypercholesterolemia, and diabetes mellitus. The analysis revealed that stroke type was a significant determinant of hospitalization outcomes. Specifically, ischemic stroke patients exhibited faster recovery times than those with hemorrhagic strokes, with a hazard ratio of 1.892, representing an 89% greater likelihood of recovery. Additionally, stroke patients across all districts treated at Labuang Baji Hospital demonstrated similar average recovery rates and discharge durations.
Modeling Youth Development Index in Indonesia Using Panel Data Regression for Binary Response with Random Effect Widyangga, Pressylia Aluisina Putri; Suliyanto, Suliyanto; Mardianto, M. Fariz Fadillah; Sediono, Sediono
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21734

Abstract

Indonesia has the largest youth population in Southeast Asia, yet its Youth Development Index (YDI) ranks only fifth in the region. This study aims to fill the gap in empirical research by modeling the YDI in Indonesia using binary logit and binary probit regressions with random effects, based on panel data from 34 provinces during 2020–2022. The YDI categories are defined according to the national target of 57.67 set by the Ministry of Youth and Sports Affairs. The analysis reveals that the binary probit model performs better than the binary logit model, with a classification accuracy of 93.14% and a McFadden R-squared of 0.4064. Gender Inequality Index (GII) and Expected Years of Schooling (EYS) significantly affect the likelihood of achieving the YDI target. These results highlight the critical role of gender equality and education in advancing youth development in Indonesia. The binary probit model provides a practical tool for policymakers to predict and evaluate the effectiveness of development programs targeting youth outcomes. This research not only contributes methodologically to the study of youth development using advanced econometric models but also offers policy-relevant insights that support the strategic goals of Indonesia Emas 2045. By identifying key leverage points such as gender equity and education access, the findings reinforce the importance of inclusive and evidence-based planning to nurture a generation of resilient, empowered, and high-performing youth who can lead Indonesia toward a prosperous future.
Deep Learning and Statistical Approaches for Forecasting the Indonesian Rupiah Exchange Rate Firdausanti, Neni Alya; Forestryani, Veniola; Nuroini, Husna Mir’atin
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.22709

Abstract

Accurate forecasting of exchange rates is essential for economic stability, investment strategy, and policy formulation. This study presents a comparative analysis of two distinct modeling approaches for predicting the Indonesian Rupiah (IDR) exchange rate against the US Dollar (USD): the Markov Switching Generalized Autoregressive Conditional Heteroskedasticity (MS-GARCH) model and the Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The MS-GARCH model captures volatility clustering and regime shifts, while the LSTM-Attention model learns complex nonlinear temporal dependencies. Using historical USD/IDR exchange rate data, both models are evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Empirical results show that the LSTM-Attention model achieves higher forecasting accuracy; however, the MS-GARCH model provides superior interpretability and insight into structural volatility. These findings underscore the importance of aligning model choice with forecasting objectives—highlighting that while deep learning offers enhanced predictive capability, statistical models remain valuable for risk analysis and financial diagnostics. The results support a complementary use of both methods in financial forecasting applications.
Prediction of Nike’s Stock Price Based on the Best Time Series Modeling Sari, Adma Novita; Zuleika, Talitha; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21737

Abstract

Nike is one of the world's largest shoe, clothing, and sports equipment companies. The more modern the development of the era, the more diverse the fashion. Of course, investors can consider this when deciding whether to invest in Nike's brand shares. Stock prices constantly fluctuate up and down, so investors need to implement strategies to minimize losses in investing to achieve economic growth. This supports the Sustainable Development Goals (SDGs) in point 8 regarding the importance of sustainable economic growth and investment in infrastructure development to improve economic welfare. Investors can minimize losses by predicting or forecasting stock prices. Stock prices can be analyzed using specific methods. The update that will be brought in this study is the Nike brand stock price prediction for the 2020-2024 period using the best model from the time series method comparison conducted using classical nonparametric, which consists of the kernel estimator method and the Fourier series estimator method and modern nonparametric using the Support Vector Regression (SVR) method. Based on the analysis method, the best method is selected through the minimum MAPE value. A comparison of the results of Nike brand stock price predictions using several methods shows that the MAPE value of the Nike brand stock price data analysis is the minimum obtained using the kernel estimator approach, which is 1.564%. Thus, the kernel estimator approach predicts the Nike brand stock price much better. Predictions using the best methods can be recommendations and evaluations for economic actors to prepare better economic planning.
Estimating Confidence Intervals for Hazard Ratio with Composite Covariates in the Cox Models Andari, Shofi
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.22710

Abstract

Hazard ratio (HR) estimation is fundamental in survival analysis, particularly in Cox proportional hazards models, where covariates influence time-to-event outcomes. When covariates are combined into composite variables, constructing confidence intervals (CIs) for the resulting HRs becomes challenging due to potential multicollinearity, interaction effects, and violations of the proportional hazards assumption. This paper presents a systematic approach for constructing confidence intervals for HRs associated with composite covariates, comparing standard methods such as the Wald, likelihood ratio, and bootstrap-based intervals. Through simulation studies for different scenarios of Cox regression models, we evaluate the performance of these methods in terms of bias, coverage probability, and robustness under various data conditions. The findings of this study provide practical recommendations for researchers dealing with composite covariates in survival analysis, ensuring reliable inference in epidemiological and clinical studies.

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