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Services
Translation, Editing/proofreading, Subtitling, Desktop publishing, Transcription, Language instruction, Interpreting
Expertise
Specializes in:
IT (Information Technology)
Computers: Systems, Networks
Computers (general)
Automation & Robotics
Internet, e-Commerce
Computers: Software
Computers: Hardware
Media / Multimedia
Marketing / Market Research
Photography/Imaging (& Graphic Arts)
Also works in:
Advertising / Public Relations
Education / Pedagogy
Cinema, Film, TV, Drama
Cooking / Culinary
Cosmetics, Beauty
Music
Names (personal, company)
Environment & Ecology
Printing & Publishing
Retail
Surveying
Human Resources
Management
Textiles / Clothing / Fashion
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Portfolio
Sample translations submitted: 1
English to Malay: WATER QUALITY CLASSIFICATION FOR LANGAT RIVER BASIN USING MULTI-CLASS CLASSIFICATION MODEL General field: Tech/Engineering Detailed field: Computers (general)
Source text - English Water Quality Index (WQI) is a method of water quality assessment by Department of Environment (DOE), under the River Water Quality Monitoring Program used to measure the level of pollution and suitability of water uses in accordance with the National Water Quality Standards. Rivers are the main source of water, and it is important to keep track of changes in water quality to ensure that the water supply is clean and safe. Currently, river water quality in Malaysia is classified into five classes based on the conventional method of WQI-DOE mathematical model. WQI-DOE takes into consideration six water quality parameters in its formula and calculations to produce one score value. The value of this score will then be compared to the WQI-DOE water quality index range to determine the water quality class. This study aims to employ data mining method and machine learning techniques to overcome water quality classification problems using WQI-DOE conventional method. The multi-class classification model was developed using supervised machine learning algorithms, namely Artificial Neural Networks (NN), Decision Tree (DT) and Support Vector Machines (SVM). It is aimed at finding the best classifier model to classify water quality data from different classes according to the established standards. Experimental results show that the SVM model is the best classifier model for classifying data from different classes with an average accuracy performance of 96.35%, supported by precision value of 91.97% and recall value of 84.89%. Whereas the DT Model showed a low average accuracy performance of 94.71%, supported by precision value of 89.22% and recall value of 76.35%. The success of SVM model strongly depends on the use of kernel function, the penalize parameter and the multi-class classification technique implemented in this study.
Translation - Malay Indeks Kualiti Air (IKA) ialah kaedah penilaian kualiti air di bawah Program Pengawasan Kualiti Air Sungai, Jabatan Alam Sekitar (JAS) yang digunakan untuk mengukur tahap pencemaran dan kesesuaian jenis guna air mengikut Standard Kualiti Air Negara. Mengesan perubahan ke atas kualiti air dimana sungai ialah sumber utama air adalah penting untuk memastikan bekalan air adalah bersih dan selamat. Pada masa ini, kualiti air sungai di Malaysia dikategorikan kepada lima kelas berdasarkan kaedah konvensional iaitu model matematik IKA-JAS. IKA-JAS menggunakan enam parameter kualiti air dalam formula dan pengiraannya untuk menghasilkan satu nilai skor. Nilai skor ini kemudiannya akan dibandingkan dengan julat indeks kualiti air IKA-JAS bagi menentukan kelas kualiti air. Kajian ini bertujuan mengkaji pendekatan perlombongan data dan teknik pembelajaran mesin bagi mengatasi masalah pengelasan kualiti air apabila menggunakan kaedah konvensional IKA-JAS. Model pengelasan pelbagai kelas yang dibangun menggunakan algoritma-algoritma pembelajaran mesin berselia iaitu Rangkaian Neural Buatan (NN), Pokok Keputusan (DT) dan Mesin Sokongan Vektor (SVM), bertujuan mencari model pengelas terbaik bagi mengelaskan data kualiti air daripada kelas yang berbeza mengikut standard yang telah ditetapkan. Keputusan eksperimen menunjukkan model SVM adalah model pengelas terbaik dalam mengelaskan data daripada kelas yang berbeza dengan prestasi ketepatan purata sebanyak 96.35%, disokong dengan nilai kepersisan pada 91.97% dan perolehan semula pada 84.89%. Manakala model DT menunjukkan prestasi ketepatan purata yang rendah pada 94.71%, yang disokong oleh nilai kepersisan pada 89.22% dan perolehan semula pada 76.35%. Kejayaan model SVM sangat bergantung kepada penggunaan fungsi kernel, parameter penalti dan teknik pengelasan pelbagai kelas yang dilaksanakan dalam kajian ini.
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Experience
Years of experience: 19. Registered at ProZ.com: Jun 2020.
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Bio
A certified teacher by profession. My professional background is in Computer Science, mainly problem solving and computer programming. I grew up speaking Malay as my native language and English proficiently. I have a particular interest in, and flair for, data analytics cum visualizations, and translations. My translation experience are gained through out my years of teaching in the academic field.
Keywords: computers, data analysis, visualization, translation
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