Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal MDP Student Conference
Firizki, Muh.
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Sistem Deteksi Masker Wajah Menggunakan CNN untuk Akses Pintu Otomatis Firizki, Muh.; Brilliant, Brian; Warohma, Ayu Mawadda
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14413

Abstract

This research discusses the development of a face mask detection system using a Convolutional Neural Network (CNN) for automatic door access in hospitals. Considering the high risk of infectious disease transmission in hospital environments, the implementation of strict health protocols, including mandatory mask usage, is essential. Manual supervision of mask compliance has limitations; therefore, an automated system is required to improve monitoring effectiveness. The dataset used in this study was collected using an ESP32 Cam, consisting of 1,186 images of masked and unmasked faces. The CNN model achieved an average training accuracy of 96.60%, with Precision and Recall values of 0.98. The automatic door system was evaluated through real-time testing involving six subjects, each undergoing 15 trials with masks and 15 trials without masks, resulting in a total of 180 trials. The system achieved a detection accuracy of 90.00% for masked faces and 74.44% for unmasked faces, with an overall system accuracy of 82.22%. These results indicate that the proposed system is capable of reliably supporting automatic door access control based on face mask compliance in hospital environments.
Implementasi Logika Fuzzy Mamdani pada Line Follower Robot dengan Fitur Obstacle Avoidance Pontoh, Abraham Lincoln; Firizki, Muh.; Rahman, Abdul
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.15396

Abstract

This study presents the design and implementation of a Line Follower Robot equipped with an Obstacle Avoidance feature based on Mamdani Fuzzy Logic. The robot is designed to autonomously follow a predefined line while adapting its movement to avoid obstacles encountered along the path. Infrared sensors are used to detect the line position, while an ultrasonic sensor is utilized to measure the distance to obstacles. The decision-making process for controlling the direction and speed of the motors is handled using the Mamdani Fuzzy Logic method. System testing was conducted through experimental trials on various track conditions, including straight paths, turns, and branching tracks. Each condition was tested with 50 trials to evaluate system performance. The experimental results show that the robot achieved an average line-following success rate of 77%, with the highest performance on straight tracks and reduced performance on branching tracks due to increased track complexity. The obstacle avoidance system demonstrated effective detection and response with a reaction time of less than 1 second. These results indicate that the proposed system is capable of performing stable line-following and obstacle avoidance, and can serve as a basis for further development of autonomous mobile robots.
Co-Authors Abdul Rahman Brilliant, Brian Pontoh, Abraham Lincoln Warohma, Ayu Mawadda