Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal Software Engineering and Computer Science (IJSECS) Journal Innovations Computer Science
Fegie Yoanti Wattimena
Universitas Ottow Geissler
Computer Science & IT

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

Found 2 Documents
Search

 1 
Application of Machine Learning in Computer Networks: Techniques, Datasets, and Applications for Performance and Security Optimization Memed Saputra; Fegie Yoanti Wattimena; Davy Jonathan
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3989

Abstract

This study designs and tests a network security system based on a combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) framework. In this study, distributed processing and reinforcement learning methods in combination with differential privacy are introduced into the proposed system to enhance attack detection and network management. The evaluation results show significant improvements; 97.3% detection accuracy, 34% more efficient bandwidth utilization and 45% less latency than the previous system. The 16-node linear scalability of the distributed architecture has a throughput of 1.2 million packets per second. It is defended against adversarial attacks by maintaining accuracy above 92% and provides a total energy saving of 38% using dynamic batch processing. Three months of testing in an operational environment detected 99.2% of 1,247 threats, including 23 new attack types, with an average detection time of 1.8 seconds. Sensitivity analysis was performed to preserve the privacy of sensitive data while maintaining network performance. The results show that the hybrid solution is reliable, scalable and secure for today's network management.
Perbandingan Kinerja Algoritma Machine Learning untuk Prediksi Cuaca di Wilayah Tropis Indonesia: Studi Komparatif Random Forest, SVM, LSTM, XGBoost, dan LightGBM Nofirman Nofirman; Munawir Munawir; Fegie Yoanti Wattimena
Journal Innovations Computer Science Vol. 5 No. 1 (2026): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v5i1.420

Abstract

Weather prediction in tropical Indonesia faces complex challenges due to high climate variability, persistent El Niño–Southern Oscillation (ENSO) influence, and uneven observational coverage. This study compared five machine learning algorithms — Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), XGBoost, and LightGBM — using 187,320 daily records from BMKG stations, ERA5 reanalysis, and TRMM satellite data (2000–2023). Preprocessing included MMDIF-RF imputation, Z-score normalization, and SMOTE for class imbalance correction. Models were evaluated on RMSE, MAE, R², Accuracy, Precision, Recall, F1-Score, and AUC-ROC. LSTM achieved the best performance (RMSE = 3.94 mm; R² = 0.891; F1-Score = 0.887; AUC-ROC = 0.941), reflecting its capacity to capture long-range temporal dependencies. XGBoost and LightGBM delivered competitive accuracy at 8–18 times lower training cost, while SVM recorded the lowest accuracy with the highest computational demand. Regional analysis showed station density and data completeness were more consequential than algorithm choice — LSTM RMSE ranged from 3.61 mm in West Java to 5.43 mm in East Nusa Tenggara. A tiered hybrid approach is recommended: LightGBM or XGBoost for routine forecasting and LSTM for extreme event detection, alongside expanded BMKG coverage in eastern Indonesia.  
Co-Authors Davy Jonathan Memed Saputra Munawir Munawir Nofirman, Nofirman