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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Wahju Sediono
Universitas Mercu Buana
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Biometric identification using augmented database Regina Lionnie; Ellisa Agustina; Wahju Sediono; Mudrik Alaydrus
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.11713

Abstract

Androgenic hair pattern is one of the newest soft biometric trait that can be used to identify criminals when their faces are covered in the evidences of criminal investigation. In real-life situation, sometimes the available evidence is limited thus creating problems for authorities to identify criminal based on the limited data. This research developed the recognition system to identify individuals based on their androgenic hair pattern in a limited data situation in such a way that the limited images were expanded by the augmentation process. There were 50 images studied and expanded into 2.000 images from the augmentation process of rotating, reflecting, adjusting color and intensity. Furthermore, the effect of human skin color extraction was investigated by employing HSV and YCbCr color spaces. The scale-space hierarchy was built among the images with Gaussian function and produced 70% recognition precision that was around more than 2 times higher compared to system of recognition with only limited data.
A NOVEL APPROACH TO STUTTERED SPEECH CORRECTION Alim Sabur Ajibola; Nahrul Khair bin Alang Md. Rashid; Wahju Sediono; Nik Nur Wahidah Nik Hashim
Jurnal Ilmu Komputer dan Informasi Vol 9, No 2 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.148 KB) | DOI: 10.21609/jiki.v9i2.382

Abstract

Stuttered speech is a dysfluency rich speech, more prevalent in males than females. It has been associated with insufficient air pressure or poor articulation, even though the root causes are more complex. The primary features include prolonged speech and repetitive speech, while some of its secondary features include, anxiety, fear, and shame. This study used LPC analysis and synthesis algorithms to reconstruct the stuttered speech. The results were evaluated using cepstral distance, Itakura-Saito distance, mean square error, and likelihood ratio. These measures implied perfect speech reconstruction quality. ASR was used for further testing, and the results showed that all the reconstructed speech samples were perfectly recognized while only three samples of the original speech were perfectly recognized.
Visual-Based Fingertip Detection for Hand Rehabilitation Dayang Qurratu’aini; Ali Sophian; Wahju Sediono; Hazlina Md Yusof; Sud Sudirman
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 2: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i2.pp474-480

Abstract

This paper presents a visual detection of fingertips by using a classification technique based on the bag-of-words method. In this work, the fingertips are specifically of people who are are holding a therapy ball, as it is intended to be used in a hand rehabilitation project. Speeded Up Robust Features (SURF) descriptors are used to generate feature vectors and then the bag-of-feature model is constructed by K-mean clustering which reduces the number of features. Finally, a Support Vector Machine (SVM) is trained to produce a classifier that distinguishes whether the feature vector belongs to a fingertip or not. A total of 4200 images, 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our results show that the success rates for the fingertip detection are higher than 94% which demonstrates that the proposed method produces a promising result for fingertip detection for therapy-ball-holding hands.
Characteristics with opposite of quranic letters mispronunciation detection: a classifier-based approach Tareq AlTalmas; Salmiah Ahmad; Nik Nur Wahidah Nik Hashim; Surul Shahbudin Hassan; Wahju Sediono
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3715

Abstract

Reading Quran for non-Arab is a challenge due to different mother tongues. learning Quran face-to-face is considered time-consuming. The correct pronunciation of Makhraj and Sifaat are the two things that are considered difficult. In this paper, Sifaat evaluation system was developed, focusing on Sifaat with opposites for teaching the pronunciation of the Quranic letters. A classifier-based approach has been designed for evaluating the Sifaat with opposites, using machine learning technique; the k-nearest neighbour (KNN), the ensemble random undersampling (RUSBoosted), and the support vector machine (SVM). Five separated classifiers were designed to classify the Quranic letters according to group of Sifaat with opposites, where letters that are classified to the wrong groups are considered mispronounced. The paper started with identifying the acoustic features to represent each group of Sifaat. Then, the classification method was identified to be used with each group of Sifaat, where best models were selected relying on various metrics; accuracy, recall, precision, and F-score. Cross-validation scheme was then used to protect against overfitting and estimate an unbiased generalization performance. Various acoustic features and classification models were investigated, however, only the outperformed models are reported in this paper. The results showed a good performance for the five classification models.
Design of a road marking violation detection system at railway level crossings Susilawati, Helfy; Nurpadillah, Sifa; Sediono, Wahju; Nurdin, Agung Ihwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp883-893

Abstract

When a train passed through a railway-level crossing, a common phenomenon was that many vehicles attempted to overtake others by crossing into lanes designated for oncoming traffic, resulting in both roads becoming congested with motorized vehicles. At that time, no system was in place to enforce penalties for violating road markings at level crossings. Therefore, a system capable of detecting such violations when trains pass through was needed. The designed system utilized a Raspberry Pi 4, a webcam, and an ultrasonic sensor. The single shot detector (SSD) method was employed for vehicle classification. The optical character recognition (OCR) method was used for character recognition on license plates. The research involved object detection at level crossings using varied objects (cars and motorcycles) with license plates categorized into two types: white background plates with black numbers and black background plates with white numbers. Based on the research results, turning on the webcam when the bar opened and closed using an ultrasonic sensor got an average error of 0.573% and 0.582%. The system could distinguish objects with an average recognition delay of 0.554 seconds and 0.702 seconds for car and motorbike objects. Regarding number plate detection, the success rate of character recognition stood at 64.45%.
Co-Authors Ali Sophian Alim Sabur Ajibola Dayang Qurratu’aini Ellisa Agustina Hazlina Md Yusof Helfy Susilawati Mudrik Alaydrus Nahrul Khair bin Alang Md. Rashid Nik Nur Wahidah Nik Hashim Nik Nur Wahidah Nik Hashim Nurdin, Agung Ihwan Regina Lionnie Salmiah Ahmad Sifa Nurpadillah Sud Sudirman Surul Shahbudin Hassan Tareq AlTalmas