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All Journal G-Tech : Jurnal Teknologi Terapan Jurnal Pengembangan Sistem Informasi dan Informatika
Fadilla, Muhammad Andika
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Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering Social Sciences

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Implementasi Metode Convolutional Neural Network (CNN) Pada Sistem Deteksi Emosi dari Ekspresi Wajah Manusia dengan Aplikasi Android sebagai Antarmuka Pengguna Fadilla, Muhammad Andika
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 5 No. 4 (2024): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v5i4.1754

Abstract

Emotion detection through human facial expressions plays an important role in various fields, such as human-computer interaction, psychology, and artificial intelligence. This thesis describes the implementation of Convolutional Neural Network (CNN) for emotion detection system based on human facial expressions, with an Android application as the user interface. The dataset used to train the CNN model consists of fer2013 and muxspace, which includes thousands of human facial images with various expressions. The system development includes data preprocessing, CNN model training, model evaluation and optimization, and integration with the Android application. The results show that the generated model is capable of accurately identifying emotions from human facial expressions in realtime and can be used in various practical applications.
Penggunaan Algoritma Greedy dan Deep Reinforcement Learning untuk Optimasi Jadwal Operasi dalam Adaptive Scheduling Fadilla, Muhammad Andika; Sutabri, Tata
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6844

Abstract

Operating room scheduling faces persistent challenges in healthcare facilities worldwide, with inefficiencies leading to resource wastage, extended patient waiting times, and staff burnout. This study addresses these challenges through three methodologies: greedy algorithm, deep reinforcement learning (DRL), and a novel hybrid model. Analysis of 35,000 surgical procedures revealed significant inefficiencies in current practices, including OR overutilization (463.87%), substantial waiting times (170.07 minutes), and frequent delays (58.39% of procedures). The hybrid model demonstrated superior performance, achieving a 34.2% reduction in OR utilization, 55.9% reduction in waiting times, and 87.5% improvement in on-time procedures compared to baseline. These improvements translated into significant clinical benefits, including reduced staff overtime (57.1%) and enhanced emergency case accommodation (17.6%). The hybrid model's resilience to operational disruptions and balanced performance across multiple dimensions provides compelling evidence for implementing adaptive scheduling methodologies in clinical practice, offering a comprehensive solution that balances efficiency, adaptability, and patient-centered care.
Co-Authors Tata Sutabri