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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF)
Zuhdi, Shaifudin
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Journal : Jurnal Teknik Informatika (JUTIF)

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Geographically Weighted Random Forests for Human Development Index of Central Java Prediction Zuhdi, Shaifudin; Fatatik, Isna Nurul; Prihasno, Izlah Nur Fadlila Herawati; Rozaq, Hasri Akbar Awal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5204

Abstract

The geographically weighted regression (GWR) model has been widely used in various types of predictions, including human development index predictions. Similarly, the random forests (RF) model has also been widely used in various value predictions. The GWR model always assumes a local linear relationship between dependent and independent variables. The RF model only produces one global model that cannot represent conditions at each location. The GWR model is susceptible to multicollinearity in each independent variable, which can lead to overfitting if multicollinearity in the model is high. To address the vulnerability of the GWR model to multicollinearity, the RF model and the GWR model can be combined. Since the RF model is not vulnerable to multicollinearity in the independent variables, the modification becomes the geographically weighted random forests (GWRF) model to improve the shortcomings of the GWR and RF models. The GWR and GWRF models were constructed using data from districts and cities in Central Java Province, which was selected as the study area due to evident disparities in human development index achievements. These disparities highlight the presence of spatial heterogeneity that conventional models fail to adequately capture. To rigorously evaluate model performance, data from 2023 were employed as training data, while data from 2024 served as testing data. This research introduces a novel integration of spatial econometric and machine learning approaches, providing a more robust framework for addressing complex spatial variations in human development outcomes. The GWRF model is capable of producing a model that does not overfit when there is multicollinearity among independent variables. The GWRF model offers a novel integration of machine learning and spatial modelling, outperforming both GWR and RF by not only delivering high predictive accuracy under complex variable relationships but also capturing nuanced local spatial heterogeneity that conventional approaches fail to address.
Stroke Risk Prediction using Winsorizing Interquartile Range and Tree-Based Classification with Explainable Artificial Intelligence Rahmadani, Fitria; Wiharto, Wiharto; Zuhdi, Shaifudin
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4760

Abstract

According to the Global Burden of Disease (GBD) Study, stroke is the third leading cause of death globally. Recognizing its signs early is crucial for both prevention and effective treatment. Although machine learning has made significant progress in predicting strokes, many current models operate like "black boxes", making them hard to interpret and often resulting in high error rates. This study aims to enhance prediction accuracy and interpretability in stroke risk detection by integrating Winsorizing Interquartile Range (IQR) for outlier management, a tree-based classification method, and Explainable Artificial Intelligence (XAI) techniques. The proposed approach applies Winsorizing Interquartile Range to handle extreme values while employing tree-based methods for prediction due to their superior performance in processing tabular data. Additionally, Explainable Artificial Intelligence techniques are utilized to improve model transparency and interpretability. Testing was conducted using the Cerebral Stroke Prediction-Imbalanced Dataset, comparing results with various existing models. The suggested approach demonstrated the lowest prediction error rates, achieving a False Positive Rate (FPR) of 15.74% and a False Negative Rate (FNR) of 8.56%. Additionally, it attained an accuracy of 84.39%, sensitivity of 91.43%, specificity of 84.26%, Area Under the Receiver Operating Characteristic Curve (AUROC) of 94.74%, and G-Mean of 87.76%, outperforming previous studies in stroke risk prediction. The combination of Winsorizing Interquartile Range, Random Under-Sampling, tree-based classification, and Explainable Artificial Intelligence techniques effectively enhances prediction accuracy and transparency, supporting early stroke detection with improved interpretability. This study contributes to medical informatics by integrating transparent predictive models suitable for decision support systems.
Co-Authors Cianata, Bryant Fatatik, Isna Nurul Hasanah, Mutiara Ibadurrahman, Muh Taqiyudin Kurniawan, Ilham Wira Prihasno, Izlah Nur Fadlila Herawati Rahmadani, Fitria Ramdhani, Moh Ferdinand Rozaq, Hasri Akbar Awal Werizky, Muhammad Rafli Wiharto Wiharto