Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Technology Research in Information System and Engineering Journal of Embedded Systems, Security and Intelligent Systems
Syamsul Bhahri
STMIK Kharisma Makassar
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Pengujian Black Box pada Aplikasi AUBOOK berbasis WEB dengan Teknik Equivalence Partitioning Fernandi Julian Winardi; Syamsul Bhahri; Hendra Surasa
JTRISTE Vol 13 No 2 (2026): JTRISTE
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini membahas pengujian kualitas pada aplikasi berbasis web AUBOOK yang dirancang sebagai media penyedia layanan buku audio. Permasalahan yang diangkat adalah bagaimana memastikan bahwa fitur-fitur utama aplikasi berjalan sesuai harapan pengguna serta bagaimana performa aplikasi ketika diakses oleh sejumlah pengguna secara bersamaan. Penelitian ini bertujuan untuk mengevaluasi fungsionalitas dan performa aplikasi agar dapat memenuhi standar kualitas perangkat lunak. Metode pengujian yang digunakan adalah Black Box Testing dengan teknik Equivalence Partitioning untuk menguji fungsionalitas, sedangkan pengujian performa dilakukan menggunakan Google Lighthouse dan Apache JMeter. Hasil pengujian menunjukkan bahwa dari empat belas fitur yang diuji, sebelas fitur berjalan sesuai dengan fungsinya, sedangkan tiga fitur tidak berfungsi sebagaimana mestinya. Pengujian performa menunjukkan bahwa aplikasi memiliki waktu respons yang baik sesuai dengan standar Web Core Vitals, baik pada kondisi sibuk maupun tidak sibuk, dengan hasil uji pada sepuluh, lima puluh, hingga seratus pengguna masih berada dalam kategori yang dapat diterima. Simpulan dari penelitian ini adalah aplikasi AUBOOK secara umum telah memenuhi aspek fungsionalitas dan performa, meskipun beberapa fitur masih perlu dilakukan perbaikan agar kualitas aplikasi dapat ditingkatkan.
Co-Authors Fernandi Julian Winardi Hendra Surasa Rachmat Renny