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Shamsul Kamal Ahmad Khalid
Universiti Tun Hussein Onn Malaysia

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Laptop Cooling Pad Temperature Monitoring System Shamsul Kamal Ahmad Khalid; Noor Azah Samsudin; Nor Amirul Amri Nordin; Muhammad Syariff Aripin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp420-427

Abstract

Cooling pads are commonly used to reduce temperature of laptop to avoid overheating problem. However, existing cooling pads are prone to various limitations: fixed voltage in the hardware component, inaccurate temperature readings and lacked of computer-based temperature monitoring functions. In this paper, a laptop cooling system with multivoltage fan speed controller using real-time processor temperature readings is proposed. A graphical user interface (GUI) and color coded LEDs are also implemented to provide visual inspection of the temperature values captured from the laptop. The temperature values are displayed in graph and tabular form.  The performance of the proposed cooling pad with computer-based monitoring application is evaluated against two other types of existing cooling pad systems.  The experiments have shown that the temperature values can be monitored clearly with the proposed GUI. More importantly, the proposed cooling pad system has the potential to achieve lower temperature faster than the rest of the existing cooling pad systems.
Autonomous coop cooling system using renewable energy and water recycling Shamsul Kamal Ahmad Khalid; Nurul Shafiqah Che Dan; Noor Azah Samsudin; Muhammad Syariff Aripin; Nor Amirul Amri Nordin
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1303-1310

Abstract

Extreme temperature in a chicken coop can significantly affect the growth and productivity of poultry. Therefore, the temperature inside the chicken coop need to be controlled to protect it from extreme temperatures. Most of the technology use electrical energy supplied to an evaporative cooling system to control the temperature of a coop. This paper presents an autonomous chicken coop cooling system using renewable energy and water recycling (REMACT). In this study, a monitoring system with necessary hardware, control application, powered with solar power source and water recycling, has been developed. The proposed cooling system consists of hardware part such as an Internet of Things (IOT) controller platform, temperature sensor, solar panel, water pump, water storage, water drain and pipe. When the temperature sensor detects extreme temperature more than 28℃ in a chicken coop, the water in storage tank will flow throughout the pipe and pass into water pump before it irrigates the chicken coop roof. When the temperature is below 22℃, the bulb will light up to transfer heat to the chicken coop and cause the temperature drop back to a healthy range. The water drain that is attached to the roof will collect the water and return the water back to the water storage again. The software components required by the project are Arduino IDE, Thinger.io, and Android Studio Framework. Several experiments have been conducted with hot and cold scenarios. The system was able to stabilise the temperature back to a healthy range. A usability testing result demonstrates 80% satisfactory rate. The findings from the experiments show that IoT, renewable energy and water recycling have the potential for temperature control of a chicken coop.
Augmented reality application for location finder guidance Anatun Nadrah Rosman; Noor Azah Samsudin; Azizan Ismail; Muhammad Syariff Aripin; Shamsul Kamal Ahmad Khalid
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1237-1242

Abstract

Finding directions to a specific location can be troublesome especially when we are not familiar with a new area. Conventionally, we may want to ask people around or possibly we use Global Positioning System (GPS) navigator. However, using GPS navigator may not be the best solution if the address is not entered accurately.  Therefore, this paper presents an augmented reality (AR) application for location finder guidance. Instead, a user is only required to scan the address indicated on a surface such as card or flyer using smart phone camera.The proposed application has utilized various components of AR technology including multiple image target, virtual button and markerless features. The development of the AR application follows phases of activities in Multimedia Mobile Content Development (MMCD) model. The proposed application is found to be very interactive and convenient in finding directions to specific location.
Towards IR4.0 implementation in e-manufacturing: artificial intelligence application in steel plate fault detection Adeleke Abdullahi; Noor Azah Samsudin; Mohd Rasidi Ibrahim; Muhammad Syariff Aripin; Shamsul Kamal Ahmad Khalid; Zulaiha Ali Othman
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp430-436

Abstract

Fault detection is the task of discovering patterns of a certain fault in industrial manufacturing. Early detection of fault is an essential task in industrial manufacturing. Traditionally, faults are detected by human experts. However, this method suffers from cost and time. In this era of Industrial revolution IR 4.0, machine learning (ML) methods and techniques are developed to solve fault detection problem. In this study, three standard ML models: LR, NB, and SVM are developed for the classification problem. The experimental dataset used in this study consists of steel plates faults. The dataset is retrieved from UCI machine learning repository. Three standard evaluation methods: accuracy, precision, and recall are validated on the classification models. Logistic regression (LR) model achieved the highest accuracy and precision scores of 94.5% and 0.756 respectively. In addition, the SVM model had the highest recall score of 0.317. The results showed the significant impact of AI/ML approach in steel plates fault diagnosis problem. 
A Simulation of Energy Recycling Concept in Automotive Application Using Hybrid Approach Noor Azah Samsudin; Muhammad Syariff Aripin; Shamsul Kamal Ahmad Khalid; Nor Amirul Amri Nordin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp412-419

Abstract

This paper presents development of a simulation to demonstrate a relatively new hybrid approach in improving energy resources that is applicable in automotive industry. The existing hybrid approach in automotive industry is considerably efficient in terms of energy saving by switching between fuel and electricity for energy resources. However, both energy resources confront various challenges. While the electricity resources require recharging, the fuel resources are scarce and expensive. Therefore, in this paper we aim to propose a relatively new hybrid approach, referred to as energy recycling concept equipped with coordination algorithm. To simulate the proposed energy recycling concept, a prototype of Electrical Control Unit (ECU) car is built. Then, an algorithm that coordinates battery charging is developed and integrated with the ECU. Finally, the simulation of the proposed energy recycling concept equipped with the coordination algorithm is evaluated on the prototype of the ECU car. The results show that the proposed energy recycling concept that allows switching between two sources of energy is applicable to operate the ECU car prototype. 
Multi-label classification approach for quranic verses labeling Abdullahi Adeleke; Noor Azah Samsudin; Mohd Hisyam Abdul Rahim; Shamsul Kamal Ahmad Khalid; Riswan Efendi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp484-490

Abstract

Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard MLC methods: binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.
Co-Authors Abdullahi Adeleke Adeleke Abdullahi Anatun Nadrah Rosman Azizan Ismail Mohd Hisyam Abdul Rahim Mohd Rasidi Ibrahim Muhammad Syariff Aripin Noor Azah Samsudin Nor Amirul Amri Nordin Nurul Shafiqah Che Dan Riswan Efendi Zulaiha Ali Othman