Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics
Djoko Soetarno
Binus University
Computer Science & IT

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

Found 2 Documents
Search

 1 
Ensemble Learning for Pediatric Stunting Detection: A Comparative Study of XGBoost, Random Forest, and LightGBM with Oversampling Techniques Tri Sugihartono; Djoko Soetarno; Rahmat Sulaiman; Sarwindah; Marini; Fitriyani
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1568

Abstract

Stunting, driven by chronic childhood malnutrition, remains a critical global public health concern. Early detection is persistently challenged by class imbalance in pediatric health datasets and the absence of systematic comparisons between oversampling strategies and ensemble classifiers. This study develops and evaluates an ensemble learning pipeline for stunting detection, benchmarking XGBoost, Random Forest, and LightGBM across five oversampling configurations — Original, SMOTE, ADASYN, Borderline-SMOTE, and SMOTE-ENN — using 10,000 pediatric health records from posyandu activities in Bangka Belitung Province, Indonesia. Seven anthropometric and demographic features were utilized, with stratified 80:20 train-test splitting and five-fold cross-validation. XGBoost with original imbalanced data achieved the highest Recall (0.9573) and a competitive F1-Score (0.9158), while LightGBM with SMOTE delivered the strongest balanced performance (F1-Score: 0.9160, ROC-AUC: 0.8431). SMOTE-ENN consistently underperformed across all classifiers. To our knowledge, this is the first study to simultaneously compare five oversampling strategies across three ensemble models within a unified framework, offering a foundation for high-sensitivity stunting surveillance in resource-constrained healthcare settings.
Comparative Performance Analysis of Dual-Prime RSA and Eight-Prime RSA Rahmat Sulaiman; Agustina Mardeka Raya; Djoko Soetarno; Tri Sugihartono; Ellya Helmud
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1569

Abstract

This study presents a comparative performance analysis of Dual-Prime RSA and Eight-Prime RSA by evaluating computational efficiency in key generation, encryption, and decryption at 1024-bit and 2048-bit key lengths. Experiments were conducted in a controlled environment, using processing time as the primary performance metric. The results show a consistent computational advantage for Dual-Prime RSA across all operations. At the 2048-bit key length, Eight-Prime RSA requires substantially more time for key generation, performing approximately 643% slower than Dual-Prime RSA, which highlights the overhead associated with increasing the number of prime factors. Decryption results further reinforce this gap: Eight-Prime RSA at 2048-bit records about a 247% increase in processing time compared with its own 1024-bit baseline and remains markedly slower than Dual-Prime RSA at the same key length. Although differences in encryption time are less significant, Eight-Prime RSA offers no meaningful efficiency advantage. While earlier studies suggest that additional prime factors may provide theoretical security benefits, this work is limited to empirical performance benchmarking and does not include a full security analysis. Overall, the findings indicate that Dual-Prime RSA is the more practical and scalable choice for real-world 2048-bit applications and performance-sensitive deployments.
Co-Authors Agustina Mardeka Raya Ellya Helmud Fitriyani Marini Rahmat Sulaiman Sarwindah Tri Sugihartono