Article Per Year (5 Year)
p-Index From 2020 - 2025
1.959
P-Index
This Author published in this journals
All Journal 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 Decision Sciences, Operations Research & Management Education Environmental Science Health Professions Immunology & microbiology Nursing Public Health Social Sciences

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Penerapan Sistem Informasi E-Document Akreditasi Program Studi Dengan Penggunakan Metode Rapid Application Development Di Bagian Unit Lembaga Penjamin Mutu Ikest Muhammadiyah Palembang Fadillah, Arif; Apriansyah, Apriansyah
Jurnal Digital: Telnologi Informasi Vol 8, No 1 (2025): Jurnal Digital Teknologi informasi
Publisher : Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/digital.v8i1.9500

Abstract

Penerapan sistem informasi e-document dalam proses akreditasi program studi di Unit Lembaga Penjamin Mutu (LPM) IKesT Muhammadiyah Palembang diharapkan dapat meningkatkan efisiensi, akurasi, dan transparansi dalam pengelolaan dokumen akreditasi yang selama ini dilakukan secara manual. Penelitian bertujuan untuk merancang Sistem Informasi E-Document berbasis web yang mengoptimalkan akreditasi dalam 9 standar dengan menerapkan 2 metode yaitu pengambilan record  melalui  database  sistem  yang  terintegrasi  dengan  unit  lain,  dan  input  upload dokumen prodi. Metode dalam penelitian ini adalah studi kasus, yang melibatkan pengumpulan data melalui observasi, dan wawancara. Kemudian rancangan metode menggunakan Metode RAD. Kesimpulan Penelitian adalah penerapan sistem e-document dengan metode RAD di LPM IKesT Muhammadiyah Palembang memberikan kontribusi signifikan terhadap perbaikan manajemen dokumen akreditasi program studi. Selain itu, diharapkan dapat menjadi referensi bagi institusi pendidikan lain dalam penerapan sistem serupa untuk mendukung proses akreditasi yang lebih efisien dan efektif. 
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.
Co-Authors . Apriansyah Ahmad Firdaus Dhia Ritaj Ramadhani Fadhil Ramadhan Fendri Martadinata First Wanita, First Ilham Ilham Joko Tri Wahyudi MUHAMMAD RIZAL Mursalim Mursalim Paray Theo Lonando Putri, Rosmitha Aizah RAMADHAN, FADHIL Ridwan Hakim Rizal H, Muhammad Rudiansyah Rudiansyah, Rudiansyah Saputra, Febri Hidayat Sukron, Sukron Ulfa, Wafiq Nanda Wisda, Wisda Yuniza Yuniza