Article Per Year (5 Year)
p-Index From 2020 - 2025
0.947
P-Index
This Author published in this journals
All Journal Journal of System and Computer Engineering Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Advances in Computer System Innovation Journal (ACSI Journal)
Batau, Radus
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Implementasi Algoritma Advanced Encryption Standard Untuk Keamanan Data Customer Pegadaian UPC Pacongkang Batau, Radus
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 1 (2024): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v7i1.221

Abstract

Permasalahan yang ada pada Pegadaian UPC Pacongkang adalah sering terjadi kebocoran data customer yang membuat perubahan data secara tiba-tiba, seperti jangka waktu peminjaman customer berubah dan bahkan jumlah peminjaman customer berubah. Hal ini tentunya merugikan pihak Pegadaian UPC Pacongkang dan customer. Melihat permasalahan tersebut pihak Pegadaian UPC Pacongkang membutuhkan metode pengamana data yang dapat membantu menjaga kerahasiaan data customer. Melindungi data perusahaan adalah dengan menggunakan teknik kriptografi. metode matematis yang berkaitan dengan aspek keamanan informasi seperti kerahasiaan, integritas data, dan autentikasi. penelitian yang akan dilakukan yaitu mengamankan file dokumen. Untuk menerapkan kriptografi pada sistem keamanan data dibutuhkan suatu algoritma mesin autentukasi data. Salah satu metode yang dapat digunakan adalah Advanced Encryption Standard (AES). Keuntungan menggunakan algoritma Advanced Encryption Standard pada kriptografi Sistem Keamanan data customer pada Pegadaian UPC Pacongkang dapat menyembunyikan informasi asli dari data sehingga tidak mudah diketahui oleh orang yang tidak berkepentingan. Dengan diimplementasikan Implementasi Kriptografi Superenkripsi Menggunakan Metode Advanced Encrytion Standard Pada Pengamatan Data Customer Pegadaian UPC Pacongkang. File data nasabah Pegadaian UPC Paconggkang menjadi aman dan tidak mudah dimanipulasi oleh orang lain karena pesan asli sudah diubah menjadi file acak yang tidak bisa dimengerti
Recognition of Human Activities via SSAE Algorithm: Implementing Stacked Sparse Autoencoder Batau, Radus; Kurniyan Sari, Sri; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1470

Abstract

This study evaluates the performance of Stacked Sparse Autoencoder (SSAE) combined with Support Vector Machine (SVM) against a standard SVM for classification tasks. We assessed both models using accuracy, precision, sensitivity, and F1 score. The SSAE Support Vector Machine significantly outperformed the standard SVM, achieving an accuracy of 89% compared to 37%. SSAE also achieved higher precision (87% vs. 75%) and sensitivity (89% vs. 37%), with an F1 score of 88% versus 36% for the standard SVM. These results indicate that SSAE enhances the model’s ability to capture complex patterns and provide reliable predictions. This study highlights the effectiveness of SSAE in improving classification performance, suggesting further research with larger datasets and additional optimization techniques to maximize model efficiency
A Deep Learning Approach to Respiratory Disease Classification Using Lung Sound Visualization for Telemedicine Applications Wahyudi, Andi Enal; Batau, Radus; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2144

Abstract

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.
Enhancing Human Activity Recognition with Attention-Based Stacked Sparse Autoencoders Batau, Radus; Sari, Sri Kurniyan; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2148

Abstract

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI PENJUALAN MENGGUNAKAN BARCODE BERBASIS CLIENT SERVER PADA UD. CAHAYA MANDIRI Batau, Radus; Syamsuddin, Syadli; Alim, Andi Chandra
Advances in Computer System Innovation Journal Vol. 1 No. 1: Desember 2023, ACSI Journal
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/acsijournal.v1i1.448

Abstract

Seiring dengan laju perkembangan dunia informasi terutama dalam bidang komputerisasi, baik hardware maupun software, maka pengolahan data dengan menggunakan alat bantu komputer juga cenderung meningkat. Sistem pengolahan data pada UD. Cahaya Mandiri bagian Penjualan barang yang meliputi alat-alat perlengkapan rumah tangga, alat tulis, mainan anak-anak dan kebutuhan hari-hari masih menggunakan nota sebagai dokumentasi penjualan sehingga membutuhkan waktu yang cukup lama dan kadang-kadang hasilnya pun kurang memuaskan. Hasil menunjukkan bahwa aplikasi yang dirancang dapat diimplementasikan. Perancangan Sistem Informasi Penjualan Menggunakan Barcode Berbasis Client Server Pada UD. Cahaya Mandiri dibuat agar memberikan kemudahan dalam pelayanan pembeli untuk proses penjualan barang.
Co-Authors Alim, Andi Chandra Firman Aziz Jeffry Jeffry Kurniyan Sari, Sri Sari, Sri Kurniyan Syamsuddin, Syadli Wahyudi, Andi Enal