Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Journal of System and Computer Engineering Jurnal Teknologi dan Sains Modern
Sari, Sri Kurniyan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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

Found 2 Documents
Search

 1 
Potensi Transformasi Deep Learning dalam Berbagai Domain: Studi Literatur pada Manufaktur dan Layanan Kesehatan Mutia, Yesi Nurul; Mustamin, Syaiful Bachri; Saktilawati, Wa Ode; Sahriani; Fajar , Nurhikmah; Sari, Sri Kurniyan
Jurnal Teknologi dan Sains Modern Vol. 2 No. 2 (2025): Maret-April
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/jtsm.v2i2.349

Abstract

Penerapan deep learning di sektor manufaktur dan layanan kesehatan untuk meningkatkan efisiensi, akurasi, dan otomatisasi. Di sektor manufaktur, kolaborasi manusia-robot menghadapi tantangan dalam merencanakan tugas dan mengendalikan gerakan robot di lingkungan tak terstruktur, sementara deteksi cacat geometris pada produk menggunakan data awan titik 3D membutuhkan teknologi yang mampu menangani data kompleks. Di sektor layanan kesehatan, deep learning memainkan peran penting dalam segmentasi citra medis dan triage departemen gawat darurat untuk meningkatkan kecepatan dan akurasi pengambilan keputusan medis. Penelitian ini menggunakan studi literatur untuk mengeksplorasi berbagai aplikasi deep learning dalam kedua sektor tersebut, seperti penggunaan mixed reality head-mounted display (MR-HMD), deep reinforcement learning (DRL), dan model PointNet++ dalam manufaktur, serta aplikasi deep learning dalam segmentasi citra medis dan analisis data rekam medis elektronik di layanan kesehatan. Hasil penelitian menunjukkan bahwa deep learning meningkatkan efisiensi kolaborasi manusia-robot, memperbaiki deteksi cacat geometris, serta meningkatkan akurasi dan kecepatan proses triage di layanan kesehatan. Kesimpulannya, deep learning memiliki potensi transformasi besar di berbagai sektor dan membuka peluang untuk otomatisasi serta pengambilan keputusan berbasis data yang lebih canggih.
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.
Co-Authors Batau, Radus Fajar , Nurhikmah Firman Aziz Jeffry Jeffry Mustamin, Syaiful Bachri Mutia, Yesi Nurul Sahriani Saktilawati, Wa Ode