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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Mustafa Mamat
Universiti Sultan Zainal Abidin
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method Mujiarto Mujiarto; Asari Djohar; Mumu Komaro; Mohamad Afendee Mohamed; Darmawan Setia Rahayu; W. S. Mada Sanjaya; Mustafa Mamat; Aceng Sambas; Subiyanto Subiyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp293-299

Abstract

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.
Radial basis function neural network for 2 satisfiability programming Shehab Alzaeemi; Mohd. Asyraf Mansor; Mohd Shareduwan Mohd Kasihmuddin; Saratha Sathasivam; Mustafa Mamat
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp459-469

Abstract

Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.
Global Convergence of a New Coefficient Nonlinear Conjugate Gradient Method Nur Syarafina Mohamed; Mustafa Mamat; Mohd Rivaie; Shazlyn Milleana Shaharuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i3.pp1188-1193

Abstract

Nonlinear conjugate gradient (CG) methods are widely used in optimization field due to its efficiency for solving a large scale unconstrained optimization problems. Many studies and modifications have been developed in order to improve the method. The method is known to possess sufficient descend condition and its global convergence properties under strong Wolfe-Powell search direction. In this paper, the new coefficient of CG method is presented. The global convergence and sufficient descend properties of the new coefficient are established by using strong Wolfe-Powell line search direction. Results show that the new coefficient is able to globally converge under certain assumptions and theories.
An Efficient Schema of a Special Permutation Inside of Each Pixel of an Image for its Encryption Hana Ali-Pacha; Naima Hadj-Said; Adda Ali-Pacha; Mustafa Mamat; Mohamad Afendee Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp496-503

Abstract

The developments of communications and digital transmissions have pushed the data encryption to grow quickly to protect the information, against any hacking or digital plagiarisms. Many encryption algorithms are available on the Internet, but it's still illegal to use a number of them. Therefore, the search for new the encryption algorithms is still current. In this work, we will provide a preprocessing of the securisation of the data, which will significantly enhance the crypto-systems. Firstly, we divide the pixel into two blocks of 4 bits, a left block that contains the most significant bit and another a right block which contains the least significant bits and to permute them mutually. Then make another permutation for each of group. This pretreatment is very effective, it is fast and is easy to implement and, only consumes little resource.
Co-Authors Aceng Sambas Adda Ali-Pacha Adda Ali-Pacha Asari Djohar Ayman Ali Arafa Babatunde Idowu Darmawan Setia Rahayu Gamal M Mahmoud Hana Ali-Pacha Hana Ali-Pacha Irene M. Moroz Mada Sanjaya WS Mohamad Afendee Mohamed Mohd Rivaie Mohd Shareduwan Mohd Kasihmuddin Mohd. Asyraf Mansor Muhammad Afendee Mohamed Muhammad Zaini Ahmad Mujiarto Mumu Komaro Naima Hadj-Said Naima Hadj-Said Nur Syarafina Mohamed Nurain Zulaikha Husin Saleh Atiewi Saratha Sathasivam Sen Zhang Shazlyn Milleana Shaharuddin Shehab Alzaeemi Subiyanto Subiyanto Sundarapandian Vaidyanathan Sundarapandian Vaidyanathan Yahia Alemami Yicheng Zeng