Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Information Technology (BIT) SISFOTENIKA
Yusfi Syawali
Universitas Negeri Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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

Found 2 Documents
Search

 1 
Face Recognition Motorcycle Rider Registration System for Rider Data Management Kana Saputra S; Insan Taufik; Irham Ramadhani; Putri Sasalia S; Yusfi Syawali; Dede Yusuf; Rezkya Nadilla Putri; Najwa Latifah Hasibuan; Fauzan Hafiz Harahap
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2157

Abstract

This research aims to develop a motorcycle rider registration system using facial recognition technology that can improve the efficiency of rider data management. This system is designed to identify and authenticate riders with high accuracy, thereby simplifying the registration and monitoring process. The methods used in this research include collecting rider facial data through cameras, image processing for feature extraction, and implementing a facial recognition algorithm. Testing was conducted in several locations with varying lighting conditions and viewing angles to ensure the system's robustness. The results show that the developed system is capable of achieving facial recognition accuracy of up to 95%. In addition, this system provides an intuitive user interface to facilitate the registration and data management process. With the implementation of this system, it is expected to reduce the time and costs required in managing motorcycle rider data, as well as improve safety and comfort while riding.
Deteksi Emosi Suara Berbahasa Indonesia Berbasis Web Menggunakan MFCC dan BiLSTM: Web-Based Indonesian Speech Emotion Recognition Using MFCC and BiLSTM Yusfi Syawali; Arnita
SISFOTENIKA Vol. 16 No. 2 (2026): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v16i2.662

Abstract

Deteksi emosi suara berbahasa Indonesia masih menghadapi tantangan karena keterbatasan dataset lokal dan belum banyaknya implementasi dalam bentuk sistem yang dapat digunakan secara langsung. Penelitian ini bertujuan mengembangkan sistem deteksi emosi suara berbahasa Indonesia berbasis web menggunakan ekstraksi fitur Mel-Frequency Cepstral Coefficients (MFCC) dan klasifikasi Bidirectional Long Short-Term Memory (BiLSTM). Dataset yang digunakan merupakan dataset hybrid yang terdiri atas 1.200 berkas audio dari 40 aktor dengan lima kelas emosi, yaitu netral, senang, terkejut, jijik, dan kecewa. Fitur suara diekstraksi menggunakan 40 koefisien MFCC yang dikombinasikan dengan Delta dan Delta-Delta, kemudian diklasifikasikan menggunakan model BiLSTM dengan attention pooling. Hasil evaluasi pada data uji menunjukkan akurasi sebesar 80,56% dan Macro F1-score sebesar 0,8060. Model juga diimplementasikan ke dalam sistem web Serambi Emosi. Hasil pengujian Black-Box menunjukkan seluruh fungsi berjalan valid, sedangkan evaluasi usability menggunakan System Usability Scale memperoleh skor 71,25 dengan kategori Good. Hasil ini menunjukkan bahwa MFCC dan BiLSTM berpotensi digunakan sebagai prototipe pendukung analisis kondisi emosional berbasis suara.
Co-Authors Arnita Dede Yusuf Fauzan Hafiz Harahap Insan Taufik Irham Ramadhani Kana Saputra S Najwa Latifah Hasibuan Putri Sasalia S Rezkya Nadilla Putri