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All Journal Journal Zetroem Journal of Embedded Systems, Security and Intelligent Systems
Rachmat
Universitas Pejuang Republik Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Machine Learning for Predicting Poverty and Educational Outcomes: A Comparative Simulation Study for Evidence Based Social Policy Syamsul Bhahri; Renny; Rachmat
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2630

Abstract

Purpose – This study aims to develop and test a comparative machine learning evaluation framework for predicting poverty status and educational risk as a methodological basis for evidence-based social policy. Design/methods/approach – A comparative simulation study was conducted using a controlled simulated dataset of 10,000 observations, sixteen input features, and two binary targets: poverty status and educational risk. Five supervised classification models were evaluated: Logistic Regression, Decision Tree, Random Forest, XGBoost, and LightGBM. The models were assessed using accuracy, F1-score, AUC, Brier Score, per-class performance, cross-validation stability, explainability, and a proposed Policy Readiness Index. The dataset included predefined prevalence assumptions, missing values, outliers, and simulated nonlinear and interaction effects. Findings - Within the controlled simulation setting, XGBoost achieved the strongest technical performance across both prediction tasks, with the highest accuracy, F1-score, AUC, and calibration quality. However, Random Forest obtained the highest Policy Readiness Index because it provided the best balance between predictive performance, cross-validation stability, and interpretable feature attribution. The findings show that the technically best model is not automatically the most policy-ready model. Research implications/limitations – The study offers a structured decision-support approach for comparing machine learning models in poverty and education policy contexts. However, all results are derived from simulated data and should be interpreted as a methodological proof of concept rather than empirical evidence for a specific real-world population. Originality/value – This study contributes a policy-oriented machine learning evaluation framework that integrates predictive quality, calibration, stability, explainability, and policy usability into a transparent Policy Readiness Index.
Comparison of CNN, ResNet50, and Xception for Deepfake Image Detection Rachmat; Mohammad Zainuddin; Handini Arga Damar Rani
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.7524

Abstract

This study compares the performance of three deep learning architectures—Convolutional Neural Network , ResNet50, and Xception—for frame-based deepfake image detection and identifies the most effective model in terms of accuracy, precision, recall, F1-score, and generalization. The study followed the Knowledge Discovery in Databases (KDD) framework using the Deepfake Detection Dataset (DFD Entire Original) from Kaggle, which consists of 3,432 videos, including 3,068 fake and 364 real videos. Videos were converted into frames using OpenCV, followed by face detection and cropping using MTCNN. The resulting face images were resized to 224×224 pixels, normalized, augmented, and labeled. To reduce classification bias caused by class imbalance, the training data were balanced using random undersampling, resulting in real frames and  fake frames. The dataset was then split into training, validation, and testing sets using a stratified 60:20:20 ratio. The results show that Xception achieved the best performance among the three models, with an accuracy of 95.21%, precision of 0.95, recall of 0.95, and F1-score of 0.95, followed by ResNet50 with an accuracy of 93.42% and CNN with an accuracy of 87.65%. These findings indicate that transfer learning-based architectures, particularly Xception, are more effective than conventional CNNs for deepfake image detection under a consistent experimental setting. This study is limited to a single dataset and frame-based evaluation, thus future work will explore the potential of hybrid models, such as Vision Transformer (ViT) combined with Capsule Networks , to improve detection performance and address challenges like temporal analysis and cross-dataset validation.
Co-Authors Handini Arga Damar Rani Mohammad Zainuddin, Mohammad Renny Syamsul Bhahri