Safwati Ibrahim
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Preferred name
Safwati Ibrahim
Official Name
Safwati, Ibrahim
Alternative Name
Ibrahim, Safwati
Safwati, Ibrahim
Safwati, I.
Ibrahim, S.
Main Affiliation
Scopus Author ID
57191950629
Researcher ID
GDL-1857-2022

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60-year Research history of missing data: a bibliometric review on Scopus database (1960-2019)

2020-12 , Farah Adibah Adnan , Mohd Hafiz Zakaria , Safwati Ibrahim

esearch on missing data was initiatedin1960and the study on this topicgrew exponentially acrossvarioussubject areas since then.Therefore, this study aims to analyze those studies, specificallyjournal articles publishedin the context of missing data.Scopus databaseand analysis tools wereutilized to retrieve all available journal articles related tomissing dataand its data. Next, due to the large number of articles foundin the Scopus database, its informationcan only be efficiently extracted and combined using Mendeley software.To further obtained insights on the extractedinformation, VOSviewer was used to obtain network visualization and overlay visualization on authors’ keywordand citation metrics was obtained using HarzingPublish or Perish software. Additionally, the growth of publication, languages used, subject area, countriesinvolved,and publication activity were also presented using bibliometric analysis. In total, 6227journal articles werefound. The recordshows thata drastic increment of research in missing data happened in 2016, with 446publications compared to 361 in 2015. Most of the articleswere affiliated with researchers in the United States and werewritten mainly in English. Mathematics, decision sciences, medicine, and computer scienceare four subject areas that have high number of articles. It is expected that thepublications on this topic will increase significantly in 2020 due to its research trend that is currently blooming in the area of medicine and thereforeleadtopotential directions for future research

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Predictor selection for progression and development of diabetic nephropathy among diabetes mellitus type 2 patients

2023-04-24 , Syafawati Ab Saad , Maz Jamilah Masnan , Karniza K. , Safwati Ibrahim

The prevalence of diabetic nephropathy is thriving worldwide such as in the United States, Europe and Asia. Diabetic nephropathy or commonly known as "diabetic kidney disease"(DKD) is characterized by the present of albuminuria, hypertension and progressive renal failure. A variety of predictors are associated with the development, progression and severity of diabetic nephropathy. This study divides diabetes patients with diabetic nephropathy into three groups; Group 1 (diabetes patients who were diagnosed with diabetic nephropathy with the same stage for a certain period of time until the current follow-up), Group 2 (diabetes patients who were diagnosed with diabetic nephropathy without obvious clinical findings but have shown disease development) and Group 3 (diabetes patients who were diagnosed with diabetic nephropathy at a certain stage for a period time but progressively worsen over time during the current follow-up). The purpose of this study is to evaluate the predictors that are associated to patients in Groups 2 and 3. Retrospective data were collected from a healthcare center located in northern peninsular Malaysia. A total of 194 patients were included in this study. Characteristics of data include demographics information such as age, gender, race and clinical data such as glycosylated haemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), urea and haemoglobin concentration (Hb). Findings show that few predictors and risk factors are significant to the development and progression of diabetic nephropathy. This study is important to reveal the significant predictors and risk factors as healthcare professionals can identify patients with risk for diabetic nephropathy and may reduce the morbidity and mortality among patients.

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Development A Portable Solar Energy Measurement System

2021-07-26 , Atika Z. , Leow Wai Zhe , Iszaidy I. , Mohd Irwan Yusoff , Safwati Ibrahim , Muhammad Irwanto Misrun , Wafi N.M. , Saw S.X.

This project presents the design and development a portable measurement device for measure and monitor solar panel parameters by using Internet of Things (IoT) concept. Solar energy measurement plays a very important role in the measurement of parameter reading for the determination of output generated, but the challenge is only performed manually at the work site using a clamp meter or a multimeter. Furthermore, it was very difficult to get the value at that time, and the data recovery error occurred. There are three specific objectives have been used for the project. Firstly, the relevant circuits for this project are design and built the circuit by using software. The output of the measurement solar irradiance, ambient temperature, solar panel temperature, current and voltage value were displayed on LCD. Next, IoT concept is used for solar panel measurement and monitoring. The value of the measurement and monitoring is used ThingSpeak cloud and ThingView application on the smartphone. It can be collected the portable solar for the energy measurement system can monitor on site, anywhere and anytime using IoT platform.

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Comparative Study of Three Methods for Determining Weibull Parameters in Pauh Putra, Perlis

2021-06-11 , Thiraphorn B.L. , Leow Wai Zhe , Mohd Irwan Yusoff , Safwati Ibrahim , Muhammad Irwanto Misrun , Kumuthawathe Ananda-Rao , Amelia A.R.

This paper studied about analysis characteristics of wind speed at Pauh Putra, Perlis, where nearest to Chuping station, Perlis, Malaysia. The wind speed characteristics consist of monthly and annual wind speed in Perlis, Malaysia. By using Weibull distribution, three different methods to calculate the potential of wind power generation and analysis the characteristics of wind speed at Pauh Putra, Perlis. The results present the means wind speed is 1.0790 m/s and 1.1321 m/s for 2018 and 2019, respectively. The highest monthly mean wind speed occurred in February for both years, 2018 and 2019. Besides, the lowest monthly wind speed for 2018 in May and for 2019 in October. The Weibull distribution summarized the highest probability density is 120% in the wind speed, 1.1 m/s using the Maximum Likelihood Method (MLM) method for these two years. Furthermore, this research found that the Energy Pattern Factor (EPF) Method is stretched to the right, and its height decreased from other methods for both years based on the graph of the wind speed of probability density function. The Maximum Likelihood Method (MLM) for these two years is higher because its shape parameters are relatively higher based on the graph of the wind speed of probability density function.

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A Case Study of Coffee Sachets Production Defect Analysis Using Pareto Analysis, P-Control Chart and Ishikawa Diagram

2021-01-01 , Idris N.I. , Sin T.C. , Safwati Ibrahim , FadzliRamli M. , Rosmaini Ahmad

Nowadays, food and beverages companies in Malaysia are struggle to survive with their rival, hence improving quality and increase productivity are vital. This paper propose the method of analysis a coffee sachet production defectives by using statistical process control (SPC) tools and also to identify each types of defects with their root cause. This paper are using methodology of physical observation through examination of automated production flow line, then Ishikawa cause-and-effect diagram are created. The company valid information are obtained from the professional such as production managers, quality control executives and line supervisors, also staffs and operators that direct or indirectly involves the production line activities through interview and distributed feedback form. After that, a Pareto diagram analysis is done hence creating a control chart (p-chart) to illustrate the result analysis. The result shows there are high number of product defectives according to each type and waste production occur. The problem found was underweight, leaking, overweight, empty, unsealed and height out of the standard. The major causes of defectives coffee sachet and root causes of each defect types are specified which are human, machine, work methods, and materials. While the main root cause of underweight and leaking defect are caused by unskilled worker and improper adjustment at the machine each time before running the production.

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Defect Factor Analysis Using Statistical Process Control Analysis: A Case Study in Spices Defected Packaging Production

2021-01-01 , Idris N.I. , Tan Chan Sin , Safwati Ibrahim , Mohammad Fadzli Ramli , Rosmaini Ahmad

The problems faced by food and beverages in Malaysia are still the emergence of defect products in every production and it escaped from quality control checked thus it reaches the consumer. This study aims to analyze the variability of defects in the production line in XX Food Industries Sdn. Bhd., using p-chart control and fishbone diagram. This company produce a food spices products whether it is well within the control or not and looking for the factors that causing to the defect products. Data collection and information obtained from interviews and documentation for the number of products and production defects for three months in XX Food Industries. The methods used to analyze products quality control by using p-chart, one of the statistical process control and fishbone diagram to determine the cause of defects products. The defects that usually occur in the production line are types of packaging defects such as leaking sachets, eyemark on the sachet sealing, and less product fill in the sachet that cause lightweight sachet. Results showed that products quality control in XX Food Industries Sdn. Bhd. is still controlled by the largest type of defects that are leaking with defects percentage 46.3 and with 36.28% percentage of defects caused by eyemark. Then, followed by defect caused by lightweight is 17.5%. Factors causing defects products are human, machine, work methods, and materials. In this case unskilled worker and the material of the plastic wrapping for the spices packet are primary cause of defects products. This study is useful in providing useful information in identifying causes for rejection or defect analysis. This research also helpful in proposing optimal solution to be implemented for productivity and quality improvement.

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Product defect prediction model in food manufacturing production line using multiple regression analysis (MLR)

2021-07-21 , Illa I.N. , Tan Chan Sin , Fadzli R. , Safwati Ibrahim , Rosmaini Ahmad , Mohd Fathullah Ghazli@Ghazali

This paper aims to develop an improved general mathematical model by focusing on human factors variables that related to the product defect in the manufacturing production line. This is because many studies found that almost 40% of total defects resulted from the operator error and the defects are usually not obvious and neglected. The objective to have defect prediction mathematical model to satisfy as early quality indicator of the manufacturing flow production line and assist the quality control team in manufacturing industries. Thus, the human factor variables will be investigate thoroughly and final model can be used to predict product defect on the line to improve product quality. Product defects quantity are identified and analyzed to determine the potential predictors for developing the mathematical model. A case study is offered that illustrates in a spice packaging semi-automated production line the effect that complexity variables have on assembly quality. By using Minitab, Multiple Regression analysis is conducted to model the relationship between the input variables towards response variables. From the analysis, the predicted data showed reasonable correlation with the observed data improved with adjusted R-Sq from 2.6% to 7.9%. Hence, the regression equation obtain is selected to be the prediction mathematical model for defects based on human factor input variables.

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Evaluation of predictors for the development and progression of Diabetic Retinopathy among diabetes Mellitus Type 2 patients

2022-12 , Syafawati Ab Saad , Maz Jamilah Masnan , Karniza Khalid , Safwati Ibrahim

Diabetic retinopathy is one of the microvascular complications caused by prolonged uncontrolled diabetes. It is believed that diabetic retinopathy correlates with certain predictors and risk factors that might worsen the disease, eventually causing visual loss and blindness among diabetes patients. There are some predictors and risk factors that attribute to the development and progression of diabetic retinopathy, such as the duration of diabetes and HbA1c trends. This study aims to evaluate the predictors and risk factors associated with the development and/or progression of diabetic retinopathy. Retrospective data were collected from a single healthcare facility in the northwest of Peninsular Malaysia. Patients included in this study were those with type 2diabetes mellitus diagnosed with diabetic retinopathy. The total number of patients involved in this study were 197, where 161 of them were newly diagnosed or with progressive diabetic retinopathy. The characteristics of diabetes patients with complication of diabetic retinopathy were described through descriptive statistics. Characteristics include demographics data such as age, gender, race and clinical data such as HbA1c readings HbA1c, estimated glomerular filtration rate (eGFR), urea and haemoglobin concentration (Hb). The results show that 7 predictors and risk factors are significant to the development and progression of diabetic retinopathy among diabetes patients. By using multinomial logistic regression, this study offers better understanding of the significant predictors and risk factors related to diabetic retinopathy.

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Assessment of wind power potential in the North region of Malaysia, Chuping Perlis

2023-01-01 , Thiraphorn Bun Leew , Leow Wai Zhe , Mohd Irwan Yusoff , Safwati Ibrahim , Tan Xiao Jian , Muhammad Irwanto Misrun , Kumuthawathe Ananda-Rao

The wind turbines is a main device that convert the kinetic energy from blades to electrical energy. Before installing wind turbines, the Weibull probability distribution must be calculated to determine the certain wind speed probability. Many problems will come if there no analysis the characteristics of wind in selected location, such as wind speed that not suitable for building wind farm to supply the population in that area. Shape and scale factors, which be controlled in a variety of ways, influence the Weibull distribution. Many studies have looked into which of the various Weibull parameter estimation methods is the most dependable. However, because the results of these investigations were inconsistent, research into more trustworthy Weibull parameter estimation methods is still ongoing. An analysis of data collected Chuping, Perlis for two years was conducted in this study (from 2018 to 2019). By using statistical analysis to evaluate the Weibull distribution method, this study used three methods to compared the Weibull parameters and identified the most reliable and effective method to obtain the Weibull probability distribution by using a three approach that compares the variances of RMSE, MSE and R2, which provides comprehensive insight into level error and volatility. Modified maximum likelihood method, graphical method, and power density method are the three methods used in this study. Therefore, the graphical method has the best accuracy in the wind speed distribution prediction, several methods such as the modified maximum likelihood method, and the power density method have the worst prediction of the wind speed distribution based on all the statistical method variances for this region.

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A review of detecting outlier in a circular regression model

2020-03-20 , Ramlee, Intan Mastura , Safwati Ibrahim , Leow Wai Zhe , Mohd Irwan Yusoff

Circular data is very relevant and important application technique in many fields such as physical science, medical science and others. During the last few years, writers have shown a deep interest of outlier detection in the circular regression model. In this case, authors have a tendency to study and explore in detail about the outlier detection in circular regression model. This paper aims to review the outlier detection methods in circular regression model. Here, we concentrate the attention on the methods of identifying outlier in this model. These survey of circular regression models in which many interesting properties and is good enough to detect the occurrence of outlier. Through the survey may highlight the significant of methods to detect outliers in circular regressions model and provide guideline for future work to look into the research gap.

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