cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dr. Muhammad Ahsan
Contact Email
muh.ahsan@its.ac.id
Phone
+6281331551312
Journal Mail Official
inferensi.statistika@its.ac.id
Editorial Address
Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Kampus ITS Keputih Sukolilo Surabaya Indonesia 60111
Location
Kota surabaya,
Jawa timur
INDONESIA
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 147 Documents
 11   12   13   14   15 
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.
Estimation of Stunting and Wasting in Sumatra 2022 with Nadaraya-Watson Kernel and Penalized Spline Oktarina, Cinta Rizki; Nugroho, Sigit; Sriliana, Idhia; Novianti, Pepi; Sunandi, Etis; Pahlepi, Reza
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23330

Abstract

This study aims to estimate the prevalence of Stunting and Wasting in Sumatra in 2022 using nonparametric regression methods, specifically the Nadaraya-Watson Kernel and Penalized Spline regression models. Both models were applied to assess the relationship between these two correlated response variables and various predictor variables, such as low birth weight, sanitary facilities, poor population, and exclusive breastfeeding. The results showed that the Nadaraya-Watson Kernel regression, particularly using the Gaussian kernel, provided the best fit with minimal prediction error, as indicated by its low Generalized Cross-Validation (GCV) value of 0.024 and high R-squared values (0.9992 for Stunting and 0.9995 for Wasting). In contrast, the Epanechnikov kernel and Biweight kernel produced higher GCV values (0.110 and 0.356, respectively), indicating less optimal performance. For the Penalized Spline model, optimal parameters were determined with a smoothing parameter λ of 5 and 3 knots, which balanced model flexibility and smoothness. This research underscores the potential of nonparametric regression techniques in capturing complex relationships in health data and provides insights for improving interventions aimed at addressing child malnutrition in Indonesia.
Forecasting Tourist Arrivals in Bali: A Grid Search-Tuned Comparative Study of Random Forest, XGBoost, and a Hybrid RF-XGBoost Model Waciko, Kadek Jemmy; Susanti, Leni Anggraini; Muayyad, Muayyad; Fakhrurozi, Rifqi Nur
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23334

Abstract

Tourism planning, infrastructure growth, and economic stability. This study presents an extensive comparative evaluation of Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and a novel Hybrid RF-XGBoost model in the prediction of monthly international tourist arrivals. A full time series dataset of a ten-year period (2014–2024) from the Central Bureau of Statistics of Bali was used for training and testing the models. Hyperparameter optimization using Grid Search with cross-validation (Grid Search CV) was used for all the machine learning models to obtain best predictive performance. Two robust metrics, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), were used to assess forecasting accuracy. Results show that the Random Forest model outperforms all competitors with lowest RMSE (41,772.68) and MAPE (6.30%), indicating high forecasting precision and robustness, especially during structural breaks such as the COVID-19 pandemic. The hybrid model also performs well, with LSTM indicating higher error rates, illustrating its shortcomings on small-to-medium-scale tourism time series. Besides, the study provides six-month ahead predictions (January–June 2025) with 95% prediction intervals, showing an ongoing trend of recovery. The findings affirm the superiority of bagging-based ensemble methods over polynomial-based methods in capturing nonlinearity, seasonality, and exogenous shocks in tourist demand. The study plugs the growing amount of data-driven tourism analytics by offering a reproducible, high-precision forecasting model for developing countries and seasonally driven destinations.
The Application of the K-Medoid Classification Method for Analyzing Crime Rates in South Sulawesi Annas, Suwardi; Aswi, Aswi; Irwan, Irwan
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.21464

Abstract

This research employs the k-medoid clustering method to analyze districts and cities in South Sulawesi based on their crime rates. As the population grows, employment opportunities tend to diminish, which can increase stress levels and, consequently, the likelihood of criminal behavior. To evaluate the distribution of criminal incidents across South Sulawesi, the k-medoid method is used to cluster regions. Unlike other clustering methods, k-medoid utilizes the median as the cluster center (medoid), which enhances its robustness against outliers. Specifically, the Partitioning Around Medoids (PAM) algorithm is applied, where initial objects are randomly selected to represent clusters. If the error value is high, the cluster centers are adjusted until the error is minimized. The dataset comprises crime incidence data for South Sulawesi in 2020, focusing on various types of crime. The analysis identified an optimal number of three clusters based on the Silhouette coefficient. Cluster 1 includes 11 regions, Cluster 2 consists of 8 regions, and Cluster 3 contains 5 regions. These clusters provide a comprehensive overview of the crime conditions across different regions within each cluster.
Application of Bisecting K-Means Method in Grouping Earthquake Data (Case Study: Earthquakes in Indonesia 2023) Rais, Zulkifli; Hafid, Hardianti; Risqi, Shopia
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23335

Abstract

Earthquakes are natural disasters that frequently occur in Indonesia, threatening the safety and resilience of its communities. This study aims to analyze the descriptive and clustering results of earthquake data in Indonesia. The data used in this study include various variables such as latitude, longitude, magnitude, and depth as the main features. The method used in this study is Bisecting K-means, and the Davies Bouldin Index test is used to determine the number of clusters. The study results indicate the formation of 3 groups, where cluster 1 falls into the deep earthquake category, cluster 3 falls into the intermediate earthquake category, and cluster 2 falls into the shallow earthquake category, with an average Davies-Bouldin Index value of 0.4758.

Page 14 of 15 | Total Record : 147

 11   12   13   14   15 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol 8, No 3 (2025) Vol 8, No 2 (2025) Vol 8, No 1 (2025) Vol 7, No 3 (2024) Vol 7, No 2 (2024) Vol 7, No 1 (2024) Vol 6, No 2 (2023) Vol 6, No 1 (2023) Vol 5, No 2 (2022) Vol 5, No 1 (2022): Inferensi Special Issue: Seminar Nasional Statistika XI 2022 Vol 4, No 2 (2021): Inferensi Vol 4, No 1 (2021): Inferensi Vol 3, No 2 (2020): Inferensi Vol 3, No 1 (2020): Inferensi Vol 2, No 2 (2019): Inferensi Vol 2, No 1 (2019): Inferensi Vol 1, No 2 (2018): Inferensi Vol 1, No 1 (2018): Inferensi More Issue