Syahrul Affandi Saidi
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Preferred name
Syahrul Affandi Saidi
Official Name
Syahrul Affandi, Saidi
Alternative Name
Afandi Saidi, Syahrul
Saidi, S. A.
Affandi Saidi, Syahrul
Main Affiliation
Scopus Author ID
56239489400
Researcher ID
FYT-4472-2022

Research Output

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  • Publication
    Zero-Biasing Split Ring Resonator using Metamaterial Element for High Gain Superstrates Ultra-Wideband Antenna
    ( 2023-03-01)
    Othman N.A.
    ;
    Mohd Aminudin Jamlos
    ;
    Wan Azani Wan Mustafa
    ;
    Jamlos M.F.
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Syahrul Affandi Saidi
    ;
    Mirza H.
    Complex materials with artificial structures known as metamaterials (MTM) have unique properties that draw several scientists to use them in a variety of research fields. In addition, MTM can go beyond some of the restrictions placed on tools used in technical practise while improving the characteristics of microwaves. The Internet of Things (IoT) application calls for the construction of zero-index Split Ring Resonator (SRR) MTM element superstrates with an ultra-wideband antenna. Keep in mind that the MTM simulates behaviour that is not found in nature, namely the zero-reflection phase (dB) on the resonance frequency. For this project, an antenna with an SRR MTM unit cell operating at 2.70 GHz is built. The SRR has four inductance-related loops (r1, r2, r3, and r4), and gaps (slots) are added to the ring to produce the capacitance effect. Parametric research has been done for the SSR in the interim to identify the best design with zero indexes, permittivity and permeability at the desired frequency. The MTM unit cells array design's 7 x 4 and 10 x 5 dimensions achieved a dB of 0° at the 2.70 GHz frequency range. A 7 x 4 MTM unit cell makes up the first design, MTM Antenna Design 1, which at 2.70 GHz recorded a gain of 5.70 dB and a return loss (S11) of-20.007 dB. The return loss (S11) at a frequency of 2.70 GHz was-19.734 dB in the second design, an MTM antenna consisting of 10 x 5 MTM unit cells, which recorded a gain of 5.66 dB.
  • Publication
    Arduino IOT Based Inventory Management System Using Load Cell and NodeMCU
    ( 2023-11-01)
    Muhammad Naufal Mansor
    ;
    Noor Anida Abu Talib
    ;
    Syahrul Affandi Saidi
    ;
    Wan Azani Wan Mustafa
    ;
    Zamri N.F.
    Nowadays, everything is made simpler with information and communication technological advancements. It is preferable to track and monitor using devices rather than perform it manually. This resulted in the rapid growth of Internet of Things (IoT) technology and relevant markets. Low cost IoT products has made access to IoT much easier and desirable. These low cost IoT devices and related technologies are widely used in areas such as educational, transportation, tracking, inventory management and many more. The use of Arduino and RFID in the inventory management system lacks in some areas including hardware limitations. In conjunction to the limitation of using an Arduino and RFID technology, this project aims to develop an IoT based inventory management system that incorporates the uses of a NodeMCU and a load cell. In comparison of the NodeMCU to an Arduino, the NodeMCU stands out with the built in Wi-Fi module with much higher processor and additional properties of it being much smaller. While the use of a load cell is much more convenient as to suit all kinds of inventory management needs compared to the use of RFID that suits better to larger scale businesses with larger inventory and massive stocks. Towards the end, this project is expected to ease inventory management by the implementation of IoT with IoT Based Inventory Management System using Load Cell and NodeMCU. The project will generate the inventory count and automatically stores data in the cloud platform. These data can be accessed with internet connection. The project also alerts users when the inventory is low or high in balance. The output of the project is that the project’s working prototype was successfully developed. Overall, the project is a success as all the objectives of the project was successfully achieved.
  • Publication
    Mechanical behaviour on concrete of coconut coir fiber as additive
    ( 2020-12-18)
    Naamandadin N.A.
    ;
    Rosdi M.S.
    ;
    Wan Azani Wan Mustafa
    ;
    Shahrol Aman M.N.S.
    ;
    Syahrul Affandi Saidi
    Fiber is one of the famous waste material in this country and fibre also can be used in order to increase the mechanical properties of concrete. Mechanical properties of concrete will be testing such compression strength and splitting tensile strength. Concrete has low tensile strength due to the brittleness properties. The coconut coir fibre processed by using the fabricated. Then the coconut coir fibre will be a sink in sodium hydroxide for 1 week and in pure water for 2 weeks at room temperature. The authorities that are already going through under treatment gave coconut coir fibre and it had been cut into a size of 25mm to 30mm. Three different ratios used in this research, which is 3%, 4% and 5% of coconut coir fibre as an additive to the concrete. There will be two types of specimens, which are cube size of 100mm x 100mm and cylinder 100mm diameter with 200mm length. All the samples cured in a water tank for 7 and 28days. The rate that had been using for this compression strength and splitting tensile strength follow the British Standard (BS 1881-116:1983). This research outcome is the addition of coconut coir fibre with concrete to increase the compression strength of the cube sample but it has lower strength than normal concrete. Meanwhile, for the splitting tensile strength of this reinforced concrete with coconut coir fibre as an additive has higher strength than normal concrete. The result proved that the addition of fibre will increase the mechanical properties of concrete but at the same time, it will decrease the workability of concrete.
  • Publication
    Entropy virus microscopy images recognition via neural network classifiers
    ( 2017-07-02)
    Afiq Ahmad Shakri
    ;
    Syahrul Affandi Saidi
    ;
    Haryati Jaafar
    ;
    Muhammad Naufal Mansor
    ;
    Wan Azani Wan Mustafa
    ;
    Ahmad Kadri Junoh
    One of the topics that are commonly in focus of object detection and image recognition is virus detection. It is well known that to learn and detecting virus proven to be a challenging and quite complex task for computer systems under different noise level. This research work investigates the performances of preprocessing stages with Entropy feature extraction with Feed Forward Neural Network (FFNN) classifier under various levels of noise. The real time experiment conducted proved that the method proposed are efficient, robust, and excellent of which it has produced a results accuracy of up to 88% for biological viruses images classification.
  • Publication
    Parametric Study on The Rice Bran Protein Extraction Process Using Water as a Solvent
    ( 2022-01-01)
    Kanapathy M.
    ;
    Mohd Sharizan Md Sarip
    ;
    Syahrul Affandi Saidi
    ;
    Mohd Aminudin Jamlos
    ;
    Aris N.I.A.
    Rice bran is a by-product resulting from the milling process that is frequently underutilized as cattle food or disposed through open-burning despite of its high nutritional and nutraceutical properties. Thus, this research aims in recognizing and exploring rice bran and its extraction methods that could further cultivate in the industry. This study focuses on the rice bran extraction process using water assisted with ultrasonication. The relationship between the operational parameters such as the temperature, extraction time and sample-to-solvent ratio to the protein yield were studied. The rice bran protein was subjected to the surface functional group analysis using Fourier-transform infrared spectroscopy (FTIR). As a conclusion, the extraction temperature of 60°C, sample to solvent ratio of 10 % and extraction time of 25 mins were chosen as the best conditions for the protein extraction. The extraction of the protein from rice bran is highly profitable due to its nutritional and nutraceutical properties as well as it is readily available at low cost.
  • Publication
    SFTA and GLCM via LDA Classifier for Skin Cancer Detection
    ( 2020-12-18)
    Mohd Firdaus Omar
    ;
    Mansor M.N.
    ;
    Syahrul Affandi Saidi
    Skin cancer may be a serious tumor. This can be clearly seen through the mature, uncommon appearance of fur pathology, which has abnormal properties in complex situations, wrinkled or uncertain perimeters, and dual colors. A small number of tulle melanomas of uncertain diameter can imitate benign moles and cannot be perceived by optical inspection. The only assumption for analyzing them is through dermoscopy as an option. Original identification and medical surgery can alternative for the patients. Within this research a detection method through image processing with various feature extraction such as Gabor filter and Hu Moment were employed and substantially improves the diagnosis performance with 97% via LDA Classifier.
  • Publication
    Reduced Graphene Oxide UWB Array Sensor: High Performance for Brain Tumor Imaging and Detection
    ( 2023-01-01)
    Mohd Aminudin Jamlos
    ;
    Jamlos M.F.
    ;
    Wan Azani Wan Mustafa
    ;
    Othman N.A.
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Syahrul Affandi Saidi
    ;
    Mohd Sharizan Md Sarip
    ;
    Mohd Al-Hafiz Mohd Nawi
    A low cost, with high performance, reduced graphene oxide (RGO) Ultra-wide Band (UWB) array sensor is presented to be applied with a technique of confocal radar-based microwave imaging to recognize a tumor in a human brain. RGO is used to form its patches on a Taconic substrate. The sensor functioned in a range of 1.2 to 10.8 GHz under UWB frequency. The sensor demonstrates high gain of 5.2 to 14.5 dB, with the small size of 90 mm × 45 mm2, which can be easily integrated into microwave imaging systems and allow the best functionality. Moreover, the novel UWB RGO array sensor is established as a detector with a phantom of the human head. The layers’ structure represents liquid-imitating tissues that consist of skin, fat, skull, and brain. The sensor will scan nine different points to cover the whole one-sided head phantom to obtain equally distributed reflected signals under two different situations, namely the existence and absence of the tumor. In order to accurately detect the tumor by producing sharper and clearer microwave image, the Matrix Laboratory software is used to improve the microwave imaging algorithm (delay and sum) including summing the imaging algorithm and recording the scattering parameters. The existence of a tumor will produce images with an error that is lower than 2 cm.
  • Publication
    Optimization of the rice bran protein powder yield using spray drying technique in response surface methodology
    ( 2024-03-21)
    Mansor M.R.
    ;
    Mohd Sharizan Md Sarip
    ;
    Ibrahim L.H.
    ;
    Syahrul Affandi Saidi
    In this study rice bran protein were extracted and powdered using spray dryer. The optimization of the process were done using Box-Behkenn response surface design. Process parameter such as temperature (120°C,165°C,210°C), aspirator (52%,66%,80%) feed flow rate (5%,30%,55%) and air flow rate (20%,30%55%) were investigated. The result show that the extraction condition have significant effects on extraction yield of protein the obtained experimental data were fitted to a quadratic equation using multiple regression analysis with high coefficient of determination value of 0.8137.an optimization study using Derringer's desired function methodology was performed and the optimal conditions based on both individual and combinations of all independent variable for yield (temperature is 120˚C, Aspirator is at 80% feed flowrate set to 5% and air flowrate of 42mmhg) and protein (temperature is 120˚C, aspirator were set at 79.9%, feed flowrate set to 31.8% and air flowrate of 51mmhg) were determined with maximum protein yield of 17.29% per 50 gram of raw rice bran (RRB)
  • Publication
    Quantitative analysis method for zingiber officinale water extract using high-performance liquid chromatography
    ( 2024-01-01)
    Mohd Sharizan Md Sarip
    ;
    Nik Daud N.M.A.
    ;
    Mohd Zainudin M.A.
    ;
    Ibrahim L.H.
    ;
    Syahrul Affandi Saidi
    ;
    Idham Z.
    ;
    Anuar A.
    Quantitative analysis of the Zingiber Officinale sample using subcritical water extraction (SWE) was developed employing High-Performance Liquid Chromatography (HPLC) to bolster the advancement of this innovative green extraction process. This research focuses on three principal ginger bioactive compounds: 6-gingerol, 6-shagoal, and 10-gingerol. Various stages were undertaken to establish the quantitative analysis method, encompassing the optimisation of HPLC operating conditions and the formulation of standard calibration curves, yielding individual compound equations. A robust correlation within the calibration curve was achieved, exhibiting an r2 value of 0.9814 and an RSD of 5.00%. A simultaneous, swift, and dependable method was established with an injection time of 20 minutes and an 8-minute delay between injections, in contrast to the previous HPLC analysis requiring a 45-minute injection time for detecting and quantifying all components. Notably, no post-treatment was applied after the SWE process. This advancement allows for potential future online measurement of Zingiber Officinale bioactive compounds extracted using subcritical water extraction through this technology.
  • Publication
    Contrast virus microscopy images recognition via k-NN classifiers
    ( 2017-07-02)
    Afiq Ahmad Shakri
    ;
    Syahrul Affandi Saidi
    ;
    Muhammad Naufal Mansor
    ;
    Haryati Jaafar
    ;
    Ahmad Kadri Junoh
    ;
    Wan Azani Wan Mustafa
    One of the topics that are commonly in focus of object detection and image recognition is virus detection. It is well known that to learn and detecting virus proven to be a challenging and quite complex task for computer systems under different noise level. This research work investigates the performances of preprocessing stages with Contrast feature extraction with K-Nearest Neighbor (KNN) classifier under various levels of noise. The real time experiment conducted proved that the proposed method are efficient, robust, and excellent of which it has produced a results accuracy of up to 88% for biological viruses images classification.