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All Journal Jurnal Manajemen dan Agribisnis Indonesian Journal of Electrical Engineering and Computer Science
Mohd Ibrahim Shapiai
Universiti Teknologi Malaysia
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance

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The Growth Performance and Costs of Rearing Friesian Crossbreed Dairy Young Stock in Malaysian Commercial Farm Ang Xin Tong; Norhariani Mohd Nor; Shanmugavelu Sithambaram; Mark Wen Han Hiew; Uswah Khairuddin; Mohd Ibrahim Shapiai; Nurul Aisyah Mohd Suhaimi; Peter Lee Ah Kong; Muhammad Ali Hanapiah
Jurnal Manajemen & Agribisnis Vol. 18 No. 3 (2021): JMA Vol. 18 No. 3, November 2021
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jma.18.3.362

Abstract

The important elements in rearing dairy young stock are good farm management, proper growth and optimal costs of rearing. A survey on these important elements was conducted at two commercial farms in Johor and Sabah in 2019. The farm herd size is 214 heads and 2,221 heads with 163,682 litres and 4.2 mil. litres of milk production, in Johor and Sabah respectively. In addition, the body weight data of 188 dairy young stock was collected and analysed to determine the growth performance using polynomial growth function. The results showed the two farms have youngstock with different Friesian blood levels (60% and 70% in Johor, and 87.5% in Sabah) with different growth performance. The average weight of dairy young stock with 60%, 70% and 87.5% Friesian blood levels at birth were 21.31±3.70kg, 22.33±2.23kg and 26.55±2.68kg, respectively, while average weight at 3 months of age were 45.00±7.07kg, 55.57±8.36kg and 75.84±12.54kg, respectively. Heifers with 87.5% Friesian blood levels was bred at 15 months of age (444kg) while heifers with lower Friesian blood levels was bred 6 months later (250kg). The average rearing (feed) cost was RM4,932 (USD1,194)/heifer. The findings of this study can give awareness and insights in the performance and costs of rearing crossbreed dairy young stock in tropics. Keywords: tropical, dairy, young stock, management, rearing cost
Radial basis function neural network for head roll prediction modelling in a motion sickness study Sarah ‘Atifah Saruchi; Mohd Hatta Mohammed Ariff; Mohd Ibrahim Shapiai; Nurhaffizah Hassan; Nurbaiti Wahid; Noor Jannah Zakaria; Mohd Azizi Abdul Rahman; Hairi Zamzuri
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1637-1644

Abstract

Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses.
Vascular dementia classification based on hilbert huang transform as feature extractor Wan Siti Nur Shafiqa Wan Musa; Mohd Ibrahim Shapiai; Hilman Fauzi; Aznida Firzah Abdul Aziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i2.pp968-974

Abstract

Impairment of cognitive and working memory after stroke was common. Vascular dementia (VaD) was a prevalent type of dementia that was caused by an impaired blood supply to the brain because of a series of small strokes. Electroencephalogram (EEG) gives information about brain status and activity, so it had a lot of potential to be used in diagnosing people with dementia. Since the EEG signal is extremely non-linear and non-stationary data, traditional Fourier analysis such as Fast Fourier Transform (FFT) that broadens sinusoidal signals cannot describe the amplitude contribution of each frequency value in specific time. Meanwhile, Hilbert Huang Transform (HHT) was based on the characteristic local time scale of the signal, it can efficiently obtain instantaneous frequency and instantaneous amplitude for nonstationary and nonlinear data. In this paper, HHT was employed as feature extraction method to extract the energy features of frequency bands from post stroke patients and healthy subjects. The extracted features were fed into extreme learning machine (ELM) for classifying post stroke patient with VaD and healthy subjects. The results of classification accuracy using HHT as feature extractor and FFT as feature extractor were compared. The mean accuracy of classification using HHT was 59.14%, respectively, while mean accuracy of classification using FFT was 94.4%, respectively, in classifying post stroke patient with VaD and healthy subjects.
Co-Authors Ang Xin Tong Aznida Firzah Abdul Aziz Hairi Zamzuri Hilman Fauzi, Hilman Mark Wen Han Hiew Mohd Azizi Abdul Rahman Mohd Hatta Mohammed Ariff Muhammad Ali Hanapiah Noor Jannah Zakaria Norhariani Mohd Nor Nurbaiti Wahid Nurhaffizah Hassan Nurul Aisyah Mohd Suhaimi Peter Lee Ah Kong Sarah ‘Atifah Saruchi Shanmugavelu Sithambaram Uswah Khairuddin Wan Siti Nur Shafiqa Wan Musa