Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal Science, Technology, and Communication Journal
Iis Afrianty
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Application of recursive feature elimination for sex classification of skull bones using random forest Zam Afryan; Iwan Iskandar; Iis Afrianty; Benny Sukma Negara; Fadhilah Syafria
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.390

Abstract

In forensic anthropology, sex estimation from the skull was a crucial initial step when visual identification of a body was not possible. Conventional methods relied on morphological assessment by experts, which was inherently subjective and dependent on the observer's experience. To address this limitation, this study implemented a computational approach using the random forest algorithm combined with the Recursive Feature Elimination feature-selection technique. The approach was evaluated using craniometric measurements from 2,524 individuals, comprising 1,368 males and 1,156 females, sourced from the Howell's craniometric dataset. The main challenge was the high dimensionality of the data, comprising 85 measurement features after non-biological attributes were removed. Using an excessive number of variables simultaneously introduced irrelevant information that lowered the model's ability to recognize true patterns, so the feature-selection technique was used to iteratively select the most informative measurements. The results showed that the model using all 82 features achieved an accuracy of 86.49 percent, while the optimized model using only 20 selected features achieved a higher accuracy of 86.85 percent. This indicated that by reducing the feature set by 75 percent, the model became lighter while remaining more accurate. The selection process further identified that cheekbone width and the height of the posterior ear protrusion were the most discriminative measurements between male and female crania, consistent with established biological evidence. In conclusion, the combination of random forest and recursive feature elimination produced an efficient and accurate sex-identification model, opening opportunities for its development as an objective forensic identification tool in the future.
Comparative analysis of feature selection methods for support vector machine-based sex classification using craniometric data Iis Afrianty; Fitri Wulandari; Nabilla Alya Firana
Science, Technology, and Communication Journal Vol. 7 No. 1 (2026): SINTECHCOM Journal (October 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v7i1.419

Abstract

Sex classification is a fundamental stage in forensic anthropology that can be supported by machine learning to achieve objective biological profiling. To address the high dimensionality and inter-trait multicollinearity of craniometric measurements, this study introduces a comparative feature-optimization framework that evaluates two distinct filtering paradigms entropy-based information gain (IG) and redundancy-aware correlation-based feature selection (CFS) integrated with a support vector machine (SVM) classifier against a full-feature baseline model. Experiments were conducted using the William W. Howells craniometric dataset, comprising 2,524 human crania (1,368 males and 1,156 females) characterized by 82 osteometric predictor variables after removing administrative identifiers. Model performance on a 70:30 training-testing split was evaluated using accuracy, precision, recall, F1-score, and the Matthews correlation coefficient (MCC). Experimental results show that the baseline SVM using all 82 features achieved an accuracy of 81.10% and an F1-score of 0.8110. Integrating feature selection substantially improved classification performance while compressing data dimensionality. The IG–SVM model achieved the highest accuracy of 86.73% (F1-score = 0.8674, MCC = 0.7348) using 20 selected features (a 75.60% feature reduction). Meanwhile, the CFS–SVM model achieved a virtually identical accuracy of 86.69% (F1-score = 0.8670, MCC = 0.7335) while requiring only 10 features (an 87.80% feature reduction). Crucially, these findings demonstrate that eliminating 75.60% to 87.80% of the original craniometric features not only enhances SVM classification accuracy by more than 5.5 percentage points by removing noisy and collinear variables, but also substantially reduces osteometric measurement complexity. Consequently, the parsimonious 10-feature CFS–SVM configuration provides an efficient and interpretable framework for computer-assisted sex estimation in forensic anthropology.
AI-based feature selection for craniometric sex classification: A nested cross-validation study on the Howells dataset Iis Afrianty; Liza Afriyanti
Science, Technology, and Communication Journal Vol. 7 No. 1 (2026): SINTECHCOM Journal (October 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v7i1.420

Abstract

Sex estimation is an important part of forensic biological-profile reconstruction. However, machine-learning results can be overly optimistic when feature selection and hyperparameter tuning are performed without separating them from model evaluation. This study compared five feature-selection methods, namely Information Gain, ReliefF, LASSO, SHAP-based ranking, and support vector machine recursive feature elimination (SVM-RFE), using five classifiers: SVM, random forest, logistic regression, decision tree, and multilayer perceptron. The study used 2,524 individuals from a Howells-derived craniometric dataset, consisting of 1,156 females and 1,368 males. A stratified hold-out set containing 20% of the data was reserved before model development. After missingness filtering based on the development data, 71 predictors remained. Model development used 5-fold outer and 5-fold inner cross-validation, with 3, 5, 7, 10, and 15 candidate features. SVM-RFE combined with SVM obtained a nested-cross-validation accuracy of 0.8767 ± 0.0098, with an F1-score of 0.8852 and ROC-AUC of 0.9481. After retuning using the full development set, the final 15-feature model achieved 0.8970 accuracy, 0.9055 F1-score, 0.9511 ROC-AUC, 0.9088 sensitivity, and 0.8831 specificity on 505 internal hold-out observations. The AI-oriented selectors had a mean accuracy only 0.0074 higher than the filter selectors, which was below the predefined 0.05 improvement threshold. Classifier choice had a significant effect on accuracy, while the feature-selection method and its interaction with the classifier were not significant. SHAP analysis identified ZYB, AUB, and MDH as the three highest-ranked features. The results show that a relatively small number of craniometric features can provide good classification performance under the evaluation procedure used in this study. However, the differences between feature-selection approaches were relatively small, and further validation on independent populations and data collected at different time points is needed before the model can be considered for forensic use.
The impact of ADASYN on Alzheimer's disease classification performance using backpropagation neural networks Alghozi Rafif Ali; Iis Afrianty; Pizaini Pizaini; Yusra Yusra; Nazruddin Safaat Harahap
Science, Technology, and Communication Journal Vol. 7 No. 1 (2026): SINTECHCOM Journal (October 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v7i1.438

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder that causes a decline in cognitive ability and the capacity to perform daily activities. Class imbalance in Alzheimer's data may bias machine learning models toward the majority class and reduce their ability to recognize the minority class. This study applied adaptive synthetic sampling (ADASYN) to a backpropagation neural network (BPNN) to balance the class distribution and improve the classification of Alzheimer’s disease. The Kaggle dataset contained 2,149 records (760 Alzheimer and 1,389 non-Alzheimer) with 34 input variables, which were reduced to 32 after selection, and one binary output variable. After preprocessing, the data were split using 10-fold cross-validation. Z-score normalization and ADASYN were applied to the training data of each fold, whereas the test data received only Z-score normalization. The 32-33-1, 32-48-1, and 32-63-1 architectures were evaluated with learning rates of 0.01, 0.05, and 0.09, each with and without ADASYN. The 10-fold average results showed that ADASYN consistently improve neither accuracy (80.47% with ADASYN versus 80.41% without) nor recall, which was higher in only 2 of 9 configurations (70.83% versus 72.28%). The best configuration was the 32-63-1 architecture with a learning rate of 0.01 and ADASYN, which achieved 81.57% accuracy, 75.05% precision, 71.84% recall, and 73.31% F1-score. ADASYN tended to reduce false positives but did not reduce undetected Alzheimer cases (false negatives) providing no better results for Alzheimer’s disease diagnosis.
Co-Authors Alghozi Rafif Ali Benny Sukma Negara Fadhilah Syafria Fitri Wulandari Iwan Iskandar Liza Afriyanti Nabilla Alya Firana Nazruddin Safaat Harahap Pizaini Pizaini Yusra Yusra Zam Afryan