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Stationer: Journal of Statistical Innovations and Applications
Published by Kiswantara Academic Publishing
ISSN : -     EISSN : 3163964X     DOI : -
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Stationer: Journal of Statistical Innovations and Applications is an open-access electronic journal published by Kiswantara Academic Publishing. The journal provides a platform for high-quality scientific articles in statistics, statistical modeling, data analysis, computational statistics, and their applications across various fields. tatisioner applies a double-blind peer-review process and welcomes manuscript submissions from authors worldwide. All submitted manuscripts must be original, unpublished, and not under review in another journal. To maintain academic integrity, each manuscript is screened using Turnitin, and manuscripts with a similarity score exceeding 20% may be rejected before the review process. The journal is available in both print and electronic formats with E-ISSN: 3163-964X.
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Articles 5 Documents
 1 
Comparison of Apple Inc Stock Forecasting Accuracy Using Hybrid TSR Linear-ARIMA Model and ARIMA Model Aulia Padhila; Muhammad Rijal Alfian; Nur Asmita Purnamasari
Stationer: Journal of Statistical Innovations and Applications Vol. 1 No. 1 (2026): Stationer: Journal of Statistical Innovations and Applications
Publisher : Stationer: Journal of Statistical Innovations and Applications

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Abstract

This study aims to compare the accuracy of Apple Inc. stock price forcasting using two time series models, namely the hybrid TSR Linear-ARIMA model and the ARIMA model. The background of this research is the need for more accurate forcasting methods in a dynamic stock market, especially for technology stocks such as Apple which have high volatility. The research methodology uses the quantitative approach with daily Apple stock price time series data for the period 2023. The hybrid TSR Linear-ARIMA model incorporates trend and residual components, while the ARIMA model uses the Box-Jenkins approach. Both models were implemented using statistical software R Studio and Minitab. The results that the ARIMA model provided better forcasting accuracy compared to the hybrid TSR Linear-ARIMA model. Comparative analysis using the MAPE shows the ARIMA model has a lowwer error rate. Specifically, the ARIMA model produces a MAPE of 2.909%, while the hybrid TSR Linear-ARIMA model produces a MAPE of 3.780%. in conclusion, the ARIMA model proved to be more effective in forecasting the stock price of Apple Inc. compared to the hybrid TSR Linear-ARIMA model. This research contributes to the development of forecasting techniques in finance and investment, especially for technology stock.
A Comparison of Linear, Polynomial, and RBF Kernels in the Support Vector Machine Method for Diabetes Classification Aulia Zahra Panjaitan; Ella Debora Sri Karina Br. Tarigan; , Hilda Umayyah Anak Ampun; Peggy Mahara Dingga
Stationer: Journal of Statistical Innovations and Applications Vol. 1 No. 1 (2026): Stationer: Journal of Statistical Innovations and Applications
Publisher : Stationer: Journal of Statistical Innovations and Applications

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Abstract

Diabetes mellitus is a noncommunicable disease with a steadily increasing prevalence, necessitating accurate methods for early detection. This study aims to analyze the performance of the Support Vector Machine (SVM) method using three kernels—Linear, Polynomial, and Radial Basis Function (RBF)—in classifying diabetes risk. The data used is the Pima Indians Diabetes dataset, consisting of 768 observations with 8 predictor variables and one target variable. This study employs a quantitative approach involving data exploration, preprocessing, and model evaluation using 10-fold cross-validation. Model performance was evaluated based on the metrics Accuracy, Sensitivity, Specificity, Kappa, and ROC-AUC. The results showed that all three kernels demonstrated good classification capabilities; however, the Polynomial kernel outperformed both the Linear and RBF kernels. The Polynomial kernel achieved the highest Accuracy value of 77.60%, as well as superior Kappa and ROC-AUC values. Furthermore, the Polynomial kernel also demonstrated better balance in classifying diabetic and non-diabetic patients. These results indicate that kernel selection in the SVM method significantly affects classification performance, and the Polynomial kernel is the most optimal model for the dataset used in this study.
Drift-Adaptive Non-Crossing Quantile Regression With Local Conformal Calibration For Nonstationary Data Zainudin Matdoan; Abdul Wahid Matdoan
Stationer: Journal of Statistical Innovations and Applications Vol. 1 No. 1 (2026): Stationer: Journal of Statistical Innovations and Applications
Publisher : Stationer: Journal of Statistical Innovations and Applications

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Abstract

This article proposes Drift-Adaptive Non-Crossing Quantile Regression (DAN-CQR), a methodological framework for modeling conditional distributions under nonstationary and tail-sensitive data. Conventional quantile regression can describe heterogeneous effects across the response distribution, but it often treats all observations as equally relevant, may produce crossing quantile curves, and usually relies on global conformal corrections that are less efficient under distributional drift and local heteroscedasticity. DAN-CQR integrates four components: memory-weighted composite quantile loss, residual-adaptive robustness, non-crossing rearrangement, and local conformal calibration. A simulation study with nonlinear structure, heteroscedasticity, heavy-tailed asymmetric errors, outliers, and regime changes was conducted to assess its behavior. The preliminary results show that DAN-CQR achieves calibrated coverage of 0.950 with a narrower average interval width of 11.460 and lower median absolute error than the global conformalized linear and polynomial quantile regression baselines. These findings suggest that the proposed framework can provide coherent quantile estimates and adaptive prediction bands for dynamic data. The method offers a promising direction for robust and interpretable distributional regression in economics, finance, environmental risk, health, education, and public policy.
Development of a Bootstrap-Stability Adaptive Ridge Regression Method for Multicollinear Data Eka Sasmita; Yuni N. Qomariah; Dyah H. Keliwida
Stationer: Journal of Statistical Innovations and Applications Vol. 1 No. 1 (2026): Stationer: Journal of Statistical Innovations and Applications
Publisher : Stationer: Journal of Statistical Innovations and Applications

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Abstract

This study develops Bootstrap-Stability Adaptive Ridge Regression (BSA-Ridge), a methodological extension of classical ridge regression for multicollinear regression data. Classical ridge regression controls coefficient variance by adding a uniform quadratic penalty, but it does not distinguish predictors whose coefficients are empirically unstable from predictors whose coefficients are relatively stable. The proposed method estimates coefficient instability through bootstrap resampling and converts the bootstrap variance into predictor-specific penalty weights. The empirical illustration uses the Longley benchmark dataset, a public dataset widely used to examine numerical instability and multicollinearity in least-squares regression. The results show that BSA-Ridge produces interpretable adaptive shrinkage and competitive predictive performance relative to ordinary least squares and classical ridge regression. The contribution of this article is a transparent, equation-based, and reproducible extension of ridge regression that can be further evaluated through simulation and high-dimensional applications.
Development of a Robust Shrinkage Empirical-Bayes P-Chart for Heterogeneous Proportion Data Muhammad Solihin Sahal
Stationer: Journal of Statistical Innovations and Applications Vol. 1 No. 1 (2026): Stationer: Journal of Statistical Innovations and Applications
Publisher : Stationer: Journal of Statistical Innovations and Applications

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Abstract

This study develops a Robust Shrinkage Empirical-Bayes P-Chart (RSEB p-chart) for monitoring proportions when subgroup sizes differ and observed proportions are heterogeneous. The classical p-chart assumes binomial variation around a stable process proportion, but real educational, health, and service data often show extra-binomial variation and unstable small-sample proportions. The proposed method combines a robust center line based on the median proportion with empirical-Bayes shrinkage of subgroup proportions. The empirical illustration uses the public STAR98 educational assessment dataset available through statsmodels. Results show that the proposed chart stabilizes proportions across subgroup sizes and provides interpretable control limits. The article contributes a transparent p-chart development for proportion monitoring in heterogeneous public-sector data.

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