Article Per Year (5 Year)
p-Index From 2021 - 2026
2.501
P-Index
This Author published in this journals
All Journal JUTIM (Jurnal Teknik Informatika Musirawas) JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Digital Teknologi Informasi Journal of System and Computer Engineering Masker Medika Journal of Community Development Journal Health Applied Science And Technology (JHAST) Jurnal Inspirasi Kesehatan (JIKA)
Arif Fadillah
IKesT Muhammadiyah Palembang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Health Professions Immunology & microbiology Nursing Public Health Social Sciences Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : journal of system and computer engineering

 1 
Enhancing Intrusion Detection Using Random Forest and SMOTE on the NSL‑KDD Dataset Saputra, Febri Hidayat; Ilham, Ilham; Rizal, Muhammad; Wisda, Wisda; Wanita, First; Mursalim, Mursalim; Fadillah, Arif
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2056

Abstract

Intrusion Detection Systems (IDS) play a crucial role in identifying suspicious activities on computer networks. However, a major challenge in developing machine learning-based IDS is the issue of class imbalance, where attacks—being minority classes—are often overlooked by classification models. This study aims to construct an intrusion detection system based on the Random Forest algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this problem. The NSL-KDD dataset is used for evaluation, with the data split into 80% for training and 30% for testing. Experiments include Random Forest-based feature selection and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Random Forest–SMOTE combination achieves an accuracy of 99.78%, precision of 99.70%, recall of 99.88%, and an F1-score of 99.79%. The confusion matrix indicates a very low rate of false positives and false negatives. Additionally, selecting the most influential features such as src_bytes and dst_bytes improves model efficiency. Thus, the integration of Random Forest and SMOTE proves to be effective in enhancing detection sensitivity toward attacks without compromising model precision. This approach offers a significant contribution to the development of adaptive, accurate, and deployable IDS in real-world network environments.
Enhancing Flood Prediction Using Hybrid LSTM-Transformer Deep Learning Approach Fadillah, Arif; Rizal H, Muhammad; Mursalim, Mursalim
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2083

Abstract

Flood prediction is crucial for effective disaster management, yet it remains a complex challenge due to the nonlinear nature of meteorological processes. This study develops and evaluates a novel hybrid model that integrates Long Short-Term Memory (LSTM) networks and Transformer attention mechanisms to enhance predictive accuracy for rainfall-based flood forecasting. Using extensive Australian weather data collected from 49 stations over a decade (2007-2017), the model incorporates comprehensive feature engineering, including derived meteorological indicators, rolling statistical measures, and temporal lag features. The hybrid LSTM-Transformer architecture achieved superior precision (77.69%) and high accuracy (84.57%) compared to a Random Forest baseline model. Confusion matrix analysis illustrated the hybrid model’s strength in reducing false alarms, indicating a conservative yet highly reliable predictive performance. Feature correlation analysis revealed important relationships among temperature, humidity, pressure, and rainfall, highlighting the complexity of meteorological interactions. The findings demonstrate the effectiveness of integrating sequential and global temporal modeling for flood prediction, providing valuable guidance for operational forecasting systems and disaster preparedness strategies. This research contributes significantly to existing flood forecasting methodologies and suggests promising directions for future enhancements.
Explainable Machine Learning for Long-Term Monthly Hydroclimatic Forecasting and Extreme-Event Detection Arif Fadillah; Markani Pato; Nuraida Latif; Benny Leornard Encrico Panggabean; Muhammad Rizal; Mursalim Mursalim; Muhajirin Muhajirin
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2741

Abstract

Long-term hydroclimatic prediction in arid urban environments remains methodologically demanding because monthly records are often intermittent, highly seasonal, zero-inflated, and dominated by rare but consequential extreme events. Using a 121-year monthly hydroclimatic record for Makkah, Saudi Arabia, spanning January 1901 to December 2021, this study develops an explainable hybrid machine-learning framework for monthly forecasting, seasonal diagnostics, and extreme-event detection. The dataset contains 1,452 monthly observations with a mean value of 6.19, median of 3.00, standard deviation of 8.05, and maximum of 52.00, indicating a strongly skewed distribution. Exploratory analysis reveals pronounced seasonality: November, December, and January exhibit the highest hydroclimatic values, whereas June is consistently dry across the full record. A temporal feature set was constructed using lag variables, rolling statistics, annual seasonal memory, cyclical month encodings, and trend indicators. Several predictive models were evaluated, including Random Forest, Extra Trees, Histogram Gradient Boosting, XGBoost, and a hybrid SARIMA–Random Forest residual-correction model. Extra Trees achieved the best forecasting performance on the holdout period, with MAE = 2.997, RMSE = 5.603, sMAPE = 57.669%, and R² = 0.518. Extreme-event detection was performed using a 90th-percentile threshold of 17.68, identifying 146 extreme months over the full record. The best classification trade-off was obtained by Histogram Gradient Boosting, while Random Forest produced the highest ROC-AUC. SHAP-based interpretation demonstrates that seasonal phase variables and annual memory features dominate model behaviour, especially month_cos, month_sin, same_month_last_year, and lag_12. The findings show that interpretable ensemble learning can provide a more transparent and operationally relevant framework than accuracy-only forecasting for arid-region hydroclimatic risk assessment.
Co-Authors . Apriansyah Ahmad Firdaus Arif Fadillah Benny Leornard Encrico Panggabean Dendra Dhia Ritaj Ramadhani Egga Asoka Fadhil Ramadhan Fendri Martadinata First Wanita, First Ilham Ilham Joko Tri Wahyudi Kartina, Riza Latif, Nuraida Markani, Markani Muhajirin, Muhajirin MUHAMMAD RIZAL Mursalim Mursalim Mursalim Mursalim Paray Theo Lonando RAMADHAN, FADHIL Ridwan Hakim Rizal H, Muhammad Rosmitha Aizah Putri Rudiansyah rudiansyah Saputra, Febri Hidayat Sukron Sukron Ulfa, Wafiq Nanda Wisda, Wisda Yuniza Yuniza