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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 176 Documents
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A Comparative Study of The Weighted High Order Fuzzy Time Series and ARIMA Method in Forecasting Tourist Visits Istin Fitriana Aziza; Siti Soraya; Adawiyah Asti Khalil; Ardiana Fatma Dewi; Annisa Ramadhan
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9187

Abstract

The tourism sector is a strategic sector that plays a crucial role in driving regional economic growth. Tourism is a leading sector in West Nusa Tenggara Province, contributing significantly to regional income, job creation, and community welfare. The presence of leading tourist destinations such as the Mandalika Special Economic Zone, Mount Rinjani, and Gili makes West Nusa Tenggara one of the leading tourist destinations in Indonesia. Local governments, tourism businesses, and other relevant parties need information on future tourist visits to plan the provision of facilities and infrastructure, manage tourist destinations, promote tourism, and develop human resources. This study aims to forecast tourist visits to West Nusa Tenggara. The methods used in this study are the weighted high-order fuzzy time series (WHOFTS) and Autoregressive Integrated Moving Average (ARIMA) methods, and these two forecasting methods are compared. The results of this study showed that WHOFTS performs better than ARIMA, as indicated by the lower MAPE value (WHOFTS is 6.17% and ARIMA is 14.67%). The forecasting results will be useful for stakeholders, especially the government, in formulating policies. The WHOFTS method used in this study cannot be applied to data with long-term seasonal patterns. Suggestion that can be given to future researchers is develop the WHOFTS that can capture additional long-term seasonal patterns
Spatial Flood Risk Mapping in West Sulawesi Using a Regression Model with Moran’s Index and Its Implications for Risk-Based Disaster Insurance Apriyanto; Darma Ekawati; Rahmah Abubakar; Yalgianto; Nurmalia; Khairul Zaman Musliadi
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9310

Abstract

This study mapped the risk of flood disasters in six districts on West Sulawesi for the period 2015–2024 using a regression model combined with Moran's Index. The results of flood risk zone mapping are used to develop spatial risk-based flood insurance. The flood risk index (Y) was analyzed against the influence of five independent variables. However, in multiple linear regression tests, there are only two significant variables, namely the variables X2 and X3. Meanwhile, Moran's I test in six counties showed no statistically significant spatial autocorrelation for any variable (all p > 0.05), reflecting a very low test power with only six spatial units. Therefore, spatial dependency was tested through a spatial panel model on the observation of 60 data (10 years for six districts). The results of the Lagrange Multiplier test detected significant spatial-lag dependence, in addition to that the Spatial Durbin Model also provided the best stable match, including significant spillovers from neighboring population densities. Based on the normalized flood risk index, it is known that Majene is the area most at risk of flooding, as well as Mamasa which is the least at risk of flooding, with Pasangkayu, Mamuju, Central Mamuju, and Polewali Mandar at moderate risk. This spatial result rests on only six units and is interpreted as exploration. As a practical implication, the resulting hotspot-coldspot zoning is used to outline risk-based premium structures where high-risk districts attract higher risk burdens, while premium affordability is assessed relative to regional economic capacity (GDP), providing an actuarial bridge between spatial risk maps and disaster insurance designs.
Estimation of Adaptive Truncated Spline Nonparametric Regression Models Based on Linear Mixed Models Rahmat Hidayat; Sifriyani; Ma’rufi; Muhammad Ilyas
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9846

Abstract

Spline nonparametric regression is a flexible approach for modeling data that do not follow standard curve patterns. However, a major challenge arises when data exhibit spatial heterogeneity specifically, varying curvature across different locations as seen in drug pharmacokinetics data. Standard Spline approaches with a single global smoothing parameter often fail to capture these characteristics, leading to over smoothing at peak concentrations or undersmoothing during the elimination phase. This study aims to construct an adaptive truncated spline regression estimator using the Linear Mixed Model (LMM) approach. Within this framework, the Spline function is represented as a combination of fixed and random effects, where the variance of the random effects is allowed to vary locally. Parameter estimation is conducted using the Restricted Maximum Likelihood (REML) method. Application to Theophylline concentration data shows that the adaptive model provides the best balance between accuracy and efficiency. This model yielded an AIC value of 447,83 and a GCV of 2,4080, which are comparable to the standard Spline, but with significantly lower complexity (Effective Degrees of Freedom / EDF = 4,46) compared to the standard Spline (EDF = 9.26). These results demonstrate that the adaptive method is capable of producing a parsimonious and biologically representative model.
Modeling with Robust Kernel Nonparametric Regression on Childhood Stunting in Kalimantan Samsul Arifin; Selvi Annisa; Siswanto
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.9914

Abstract

This study models stunting prevalence across 56 regencies/cities in Kalimantan using robust kernel nonparametric regression. This approach addresses the nonlinear relationship between stunting and four predictors: access to improved sanitation, low birth weight, population density, and poverty rate. An examination of influential observations using DFFITS identified five regencies as outliers; thus, the robust MM-estimator approach was applied to mitigate the influence of these extreme observations on the estimation results. Optimal bandwidth selection was performed using the Cross-Validation (CV) method across several kernel functions, namely Epanechnikov, Gaussian, and Uniform. The results demonstrated that the Uniform kernel function yielded the smallest CV value with a bandwidth combination of h1=0.6, h2=0.2, h3=0.6, and h4=0.2. The Robust Uniform Kernel model delivered the best performance, with an MSE of 2.0556, RMSE of 1.4337, MAE of 0.6581, and of 0.9510. This study indicates that robust MM-estimator kernel nonparametric regression can produce stunting prevalence estimates that are more accurate, flexible, and stable in the presence of outliers.
Application of the Cox Proportional Hazards Model in Survival Analysis of Hypertensive Patients Ineu Sulistiana; Zamziri; Ummi Maktum
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.10018

Abstract

Hypertension is a global health problem and one of the leading causes of mortality in Indonesia, including the Bangka Belitung Islands Province. This study aims to analyze the factors affecting the time required for blood pressure to return to normal in hospitalized hypertension patients at Bakti Timah Hospital Pangkalpinang. The methods used were survival analysis with Kaplan–Meier estimation, the Log-rank test, and the Cox Proportional Hazard Model. The results of the Log-rank test showed that the survival functions of hypertensive patients aged less than 50 years and those aged more than 50 years were statistically significantly different, indicating a different effect on the time to blood pressure normalization. Similarly, the comparison of the survival functions between patients with comorbidities and those without comorbidities showed a statistically significant difference, indicating a different effect on the time to blood pressure normalization among hypertensive patients. The best model obtained was h_i (t│X)=h_o (t)exp⁡(〖-2,82047〗_(hypertensive patients with comorbidities)). Based on the Hazard Ratio (HR), hypertensive patients with comorbidities have a hazard of achieving normal blood pressure that is 0.06 times that of hypertensive patients without comorbidities.
Modeling Industrial and Maritime Sector Effects on Economic Growth in Riau Island Using GWNR-P Spline Cinta Rizki Oktarina; Andro Kurniawan; Sandy Salomo Saruan; Putri Suci Aria; Winalia Agwil
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.10050

Abstract

Economic growth is an important indicator of regional development and is influenced by various socioeconomic factors. In the Riau Islands Province, the effects of the Labor Force Participation Rate (LFPR) and Capture Fishery Production Value on the Gross Regional Domestic Product (GRDP) percentage distribution may vary across districts and cities due to spatial heterogeneity and nonlinear relationships. Therefore, this study applies the GWNR-PSpline model to analyze these relationships. The optimal Penalized Spline model was obtained using knot points of 67.83 and 15.67 with a smoothing parameter of 50, as indicated by the minimum Generalized Cross Validation (GCV) value of 1245.917. Subsequently, the Manhattan distance and Bisquare kernel weighting scheme produced the minimum Cross Validation (CV) value of 64.51, indicating the lowest prediction error among the evaluated weighting combinations. The estimated local parameters varied across districts and cities, indicating spatial heterogeneity in the relationships between the explanatory variables and economic growth. Spatial analysis revealed that Natuna and Anambas Island exhibited stronger local effects than other regions. Furthermore, the local coefficients of determination ranged from 0.3944 to 0.8603, substantially exceeding the global P-Spline model (R2=0.158). These findings demonstrate that the GWNR-PSpline model effectively captures both nonlinear and spatially varying relationships.

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