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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electronics and Instrumentation Systems Jurnal Ilmu Dasar TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Journal of Engineering and Technological Sciences Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Mechanical Engineering Science and Technology Jurnal Elektronika dan Telekomunikasi Jurnal Rekayasa elektrika

Anam, Khairul

Department Of Electrical Engineering, Universitas Jember

Author-ID : 1465583

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

Published : 19 Documents Claim Missing Document

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Articles

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Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition Khairul Anam; Adel Al-Jumaily
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 1: February 2018
Publisher : Institute of Advanced Engineering and Science

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

Improved myoelectric pattern recognition of finger movement using rejection-based extreme learning machine Khairul Anam; Adel Al-Jumaily
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16566

Myoelectric control system (MCS) had been applied to hand exoskeleton to improve the human-machine interaction. The current MCS enables the exoskeleton to move all fingers concurrently for opening and closing hand and does not consider robustness issue caused by the condition not considered in the training stage. This study addressed a new MCS employing novel myoelectric pattern recognition (M-PR) to handle more movements. Furthermore, a rejection-based radial-basis function extreme learning machine (RBF-ELM) was proposed to tackle the movements that are not included in the training stage. The results of the offline experiments showed the RBF-ELM with rejection mechanism (RBF-ELM-R) outperformed RBF-ELM without rejection mechanism and other well-known classifiers. In the online experiments, using 10-trained classes, the M-PR achieved an accuracy of 89.73% and 89.22% using RBF-ELM-R and RBF-ELM, respectively. In the experiment with 5-trained classes and 5-untrained classes, the M-PR accuracy was 80.22% and 59.64% using RBF-ELM-R and RBF-ELM, respectively

Multilayer extreme learning machine for hand movement prediction based on electroencephalography Khairul Anam; Cries Avian; Muhammad Nuh
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2626

Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.

Analisis Hasil Elektroforesis DNA dengan Image Processing Menggunakan Metode Gaussian Filter Khairul Anam; Widya Cahyadi; Ihsanul Azmi; Kartika Senjarini; Rike Oktarianti
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 11, No 1 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.58268

DNA gel electrophoresis plays an important role in the development of science. However, the process of manually analyzing DNA size is still relatively difficult, time-consuming, and often results an error. This study proposed electrophoresis process using image processing with Gaussian Filter method. Gaussian Filter is used to improve the quality of the image which makes the image clearer. The method was applied using python programming and then embedded into Raspberry pi 3 module. This modul processed images taken by Raspberry pi V1 camera. To realize these taken images, tracking mouse was used. All the images which had been processed were displayed on LCD touchscreen 5 inch. The result shows that the study using Gaussian Filter indicates good performance. This is proved by the lowest error percentage of 0,20% . In addition, compared to previous studies, the largest error percentage is still relatively smaller at 12.41%.

Myoelectric Control Systems for Hand Rehabilitation Device: A Review Khairul Anam; Ahmad Adib Rosyadi; Bambang Sujanarko; Adel Al-Jumaily
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

One of the challenges of the hand rehabilitation device is to create a smooth interaction between the device and user. The smooth interaction can be achieved by considering myoelectric signal generated by human's muscle. Therefore, the so-called myoelectric control system (MCS) has been developed since the 1940s. Various MCS's has been proposed, developed, tested, and implemented in various hand rehabilitation devices for different purposes. This article presents a review of MCS in the existing hand rehabilitation devices. The MCS can be grouped into main groups, the non-pattern recognition and pattern recognition ones. In term of implementation, it can be classified as MCS for prosthetic and exoskeleton hand. Main challenges for MCS today is the robustness issue that hampers the implementation of MCS on the clinical application.

Steering System of Electric Vehicle using Extreme Learning Machine Sofyan Ahmadi; Khairul Anam; Azmi Saleh
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2096

The development of electric vehicle technology is currently increasing and growing very fast. Some efforts have been conducted, one of which is using BLDC (brushless direct current) motors to improve efficiency. This study utilized extreme learning machine (ELM) embedded on the microcontroller as well as the differential method for controlling the rotational speed of the BLDC motor. The experimental results on the acceleration testing by traveling a distance of 200 meters achieved the average current of 1.09 amperes. The average power efficiency test is 104 watts. Furthermore, the results of the efficiency experiment with a track length of 3.3 km (kilometers) in 10 minutes obtained the energy efficiency of 177.34 km/kWh (kilowatt for one hour)

Evaluation Of Inverse Kinematics For Quadruped Robot With Accelerometer Sensor Ahmad Iqbal Nasrudin; Khairul Anam; M. Agung Prawira N
Jurnal Rekayasa Elektrika Vol 15, No 3 (2019)
Publisher : Universitas Syiah Kuala

Quadruped robot is one of the types of robots that move using legs 4 compiled by some of the servo motor as a driving force on each foot ft the DOF is used. However, problems arise when this robot is confronted on the inclined plane, because the burden is borne out every servo motor on the feet will be different, so can make a fast servo motor damaged. This research was conducted on the design of the quadruped robot system for stability on the inclined plane using the accelerometer sensor and the application of the inverse kinematics method with PID control of Ziegler-Nichols. The results of tests obtained response robots in stabilizing the body when faced with the inclined plane with some degree of slope of the pitch and roll. In this research was conducted some test for quadruped robot: static Testing robot against the angel of the pitch in the standby retrieved response average robot in stabilizing the body is 245 ms, static Testing robot against the angle of roll in standby retrieved response average robot in stabilizing the body is 280 ms, dynamic Testing robot against the roll and pitch in standby retrieved response average robot in stabilizing the body is 8 seconds, Static Testing robot to stabilizing the body against the angel of roll in running the largest robot oscillations obtained 10 degrees, dynamic Testing robot to stabilizing the body against the angle of roll in run retrieved response average robot in stabilizing the body is 490 ms.

Robot Beroda Pendeteksi Gas Karbon Monoksida dan Metana Berbasis IoT Menggunakan Metode Finite State Machine dan Fuzzy Logic Wira Adi Winata; Khairul Anam; Ali Rizal Chaidir
Jurnal Rekayasa Elektrika Vol 18, No 1 (2022)
Publisher : Universitas Syiah Kuala

Occupational Safety and Health (K3) is an important requirement needed in mining. This is because activities in mining have great risks and are associated with unpredictable natural conditions. One of them is the leakage of hazardous gas at the mine site caused by mining activities. This article proposes a wheeled robot to detect carbon monoxide gas and methane gas based on the Internet of Things (IoT) using Finite State Machine (FSM) and Fuzzy Logic. The finite state machine (FSM) in this study is used as a control of the robot’s movement, while fuzzy logic is used as a safety classification of the readable state of dangerous gases. The results showed that the system was capable of detecting gas and the information is successfully sent to a web server. In addition, the use of lidar can detect obstacles around the robot.

Rancang Bangun Sistem Navigasi Robot Beroda Pemandu Disabilitas Netra Menggunakan Metode Waypoint Ahmad Rausan Fikri; Khairul Anam; Widya Cahyadi
Jurnal Rekayasa Elektrika Vol 16, No 3 (2020)
Publisher : Universitas Syiah Kuala

Robotics has become a popular field of research for developing medical and human aids, including visually impaired people. This paper presents problem-solving of creating a robot that can guide visually impaired people outdoor using a Global Positioning System (GPS)-based navigation system with a waypoint method. This study uses Linkit ONE, which is equipped with a GPS as a determinant of the earth’s ordinate position, added with a compass module to determine the robot’s direction and a rotary encoder sensor to minimize the error of the robot’s position. There are two tests with four waypoints. Firstly, it is a test with no obstacles and holes. Secondly, it is the test with obstacles and holes. The first test results obtained an average error of waypoint-1 0.54 m(meters), waypoint-2 1.2 m, waypoint-3 1,9 m, and waypoint-4 1.7 m. Meanwhile, the second test results yielded an average error of waypoint-1 1.26 m, waypoint-2 2.18 m, waypoint-3 2.52 m, and waypoint-4 2,44 m. Therefore, the visual disability guidance robot with this waypoint method has good accuracy because the average error value of the robot is under a radius of 2 m when there are no obstacles and holes and under a radius of 3 m when there are obstacles and holes.

Sistem Navigasi Kursi Roda Elektrik untuk Pasien Penyandang Cacat Fisik Menggunakan Metode Convolutional Neural Network Sutikno Sutikno; Khairul Anam; Azmi Shaleh
Jurnal Rekayasa Elektrika Vol 17, No 1 (2021)
Publisher : Universitas Syiah Kuala

Patients with physical disabilities, such as losing a leg or experiencing paralysis, will have difficulty moving from one place to another. As a result, they need someone or a device that can help them to move. One that is often used by patients is a wheelchair. This study proposes an electric wheelchair navigation system that can be controlled by voice commands using the Convolutional Neural Network (CNN) method. CNN is used as the main method for identifying commands embedded on the Raspberry Pi microcontroller. The recorded voice data is then converted to spectrogram images before being fed to CNN. This method is proven to be better in voice command recognition with an accuracy of above 90%. There are five different voice commands: forward, backward, left, right, and stop. Preliminary experimental results indicate that the electric wheelchair is able to move according to the commands given.

Co-Authors Adel Al-Jumaily Adel Al-Jumaily Ahmad Adib Rosyadi Ahmad Iqbal Nasrudin Ahmad Rausan Fikri AHMADI, SOFYAN Al-Jumaily, Adel Ali Rizal Chaidir Andrita Ceriana Eska Azmi Saleh Azmi Shaleh Azmy Akhyar Al Insani Bambang Sri Kaloko Bambang Sujanarko Cries Avian Cries Avian Efendi, Dicky Yusril Faruq Sandi Hanggara Harun Ismail Ihsanul Azmi Kartika Senjarini M. Agung Prawira N Mahros Darsin Mohamad Agung Prawira Negara, Mohamad Agung Muchamad Arif Hana Sasono Muhammad Nuh Mulyadi, Fikri Qoyima, Rosida Amalia Nurul Rike Oktarianti Rizal, Ahmad Ayyub Syaiful Safri Nahela Sasono, Muchamad Arif Hana Sutikno Sutikno Wibowo, Robertoes Koekoeh K. Widya Cahyadi Wira Adi Winata

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