HEMS: Automated Online System for SEGAK Analysis and Reporting

International Journal of Software Engineering and Its Applications

Vol. 10, No. 10 (2016), pp. 89-104

http://dx.doi.org/10.14257/ijseia.2016.10.10.10

ISSN: 1738-9984 IJSEIA

Copyright ⓒ 2016 SERSC

HEMS: Automated Online System for SEGAK Analysis and

Reporting

Fadzli Syed Abdullah1, Nor Saidah Abd Manan2,*, Aryati Ahmad3,*1, Sharifah

Wajihah Wafa4, Mohd Razif Shahril5, Nurzaime Zulaily6,

Rahmah Mohd Amin7 and Amran Ahmed8

1,2Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin,

Terengganu, Malaysia 3Institute for Community (Health) Development, Universiti Sultan Zainal Abidin,

Terengganu, Malaysia 4,5,6Faculty of Health Sciences, Universiti Sultan Zainal Abidin,

Terengganu, Malaysia 7Faculty of Medicin, Universiti Sultan Zainal Abidin, Terengganu, Malaysia

8Institute of Engineering Mathematics, Universiti Malaysia Perlis,

Perlis, Malaysia [email protected], [email protected],

{3aryatiahmad, 4sharifahwajihah, 5razifshahril}@unisza.edu.my, [email protected], [email protected],

[email protected]

Abstract

The Ministry of Education Malaysia (MOE) implemented the National Physical Fitness

Standard (SEGAK) for Malaysian School Children Assessment Program. Ever since, the

SEGAK assessment data had been collected by the respective teachers in every school

twice a year, then its summary is being submitted to the State Department of Education

manually through email. This creates problems such as lack of a standardized report

format, complex formula in calculating SEGAK score and different data interpretation. In

this paper, an integrated and automated SEGAK submission and analysis system is

proposed. The system, which is known as Health Monitoring System (HEMS), is a web

based system developed with an automated pre-processing method and implemented three

tier architecture. HEMS have a centralized database that collects the assessment data

from seven districts in Terengganu. A total of 35,681 data was collected from 213

primary schools, and 27,201 data from 44 secondary schools, giving a big total of 67,519

data. During the pre-processing, 4,637 data or 6.9% of the collected data were excluded

due to wrong and incomplete information. Using HEMS template, the submitted data have

a consistent format of data types. HEMS generates an automated analysis and reporting

for the use of related authorities.

Keywords: Data Pre-processing, Health Monitoring System, Physical Fitness

Assessment, SEGAK

1. Introduction

Physical fitness can be defined as “the ability to carry out daily tasks with vigor and

alertness, without undue fatigue and with ample energy to enjoy leisure-time pursuits and

to meet unforeseen emergencies” [1]. It can be an indicator and later-predictor of the

health of children [2]. Physical fitness can be illustrated by three main components;

1 *Corresponding Author


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90 Copyright ⓒ 2016 SERSC

cardiorespiratory capacity, strength and agility [3]. The measurement of physical fitness

includes cardiorespiratory endurance, muscular strength, flexibility, muscular endurance

and body composition.

As a learning institution, schools should provide physical education to all students and

it must be taught by an experienced teachers [4]. Since the 21st century, children's health

trends were at an alarming rate. This pushes the schools to remodel and enlarge their roles

in promoting physical education [5], [6].

Physical fitness affects mental health by reducing symptoms of depression, anxiety,

mood swings and increases self-esteem as well as academic performance among youth

[7]. Due to this fact, Ministry of Education Malaysia (MOE) had implemented the

National Physical Fitness Standard for Malaysian School Children (SEGAK) Assessment

Program in 2008. Moreover, during school hours, majority of Malaysian children were

not meeting the minimum, moderate to vigorous physical activity (MVPA)

recommendations [8] which is 60 min/day [9].

There are 500 schools in Terengganu which consists of 352 primary schools and 148

secondary schools [10]. The traditional way of collecting SEGAK data from all schools

was a big challenge. Teachers who teaches physical and health education (PJPK) are

being asked to assess all the students twice a year and submit the summary report of the

assessment to the State Department of Education at the end of each year. Lack of a

standardized report format, complex formula in calculating SEGAK score and different

data interpretation had led into reporting error. This paper proposes an integrated and

automated Health Monitoring System (HEMS) that acts as an online platform to collect,

pre-process and analyzes the SEGAK data for all schools in Terengganu. The system will

allow the teachers to upload the collected data in a single repository, where the data will

be automatically pre-processed to eliminate data errors that could affect data analysis and

reporting process.

Three tiers architecture is used as the integrated design of the HEMS. Figure 1

illustrates the three tiers architecture consisting of data tier, application tier and

presentation tier.

Figure 1. Three Tiers Architecture for HEMS System

2. SEGAK Assessment

SEGAK assessment consists of four tests which includes step-up test, push-up test,

partial curl-up test and sit and reach test. Each student is examined using this assessment

during the PJPK class [11]. The followings were descriptions for each test:

Step-up (NTB). A test to assess the cardiorespiratory. A student must step up and

down from a bench for three minutes. The teacher must be ready with a metronome with a

standardized rhythm, 96 beats per minute. The heart rate needs to be taken five seconds


Vol. 10, No. 10 (2016)

Copyright ⓒ 2016 SERSC 91

after the student finishes the test. The rate should be taken within one minute. Figure 2(a)

shows the position of the student during step-up assessment.

Push-ups (TT/S). A test to exercise the pectorals and biceps. This test is different

between male and female respondents. A male student will perform this test in prone

position (lies with chest down and face up) as shown in Figure 2(b). While a female

student will perform push-ups with bent knees as shown in Figure 2(c). A student must

raise and lower the body using the arms for one minute.

Partial curl-ups (RTS). A test to assess the abdominal muscles. A student must lie

down with knees bent at a 900 angle as shown in Figure 2(d). The student needs to curl-up

within one minute, with 50 beats per minute metronome rhythm. The measurement is

based on the maximum number of curl-ups achieved in one minute.

Sit and reach (JM). A test to examine flexibility. Using a standard sit and reach

tools, a student must sit on the floor without wearing shoes. The teacher will take the

maximum distance reached by the tip of the student’s fingers. Students have a trial of

three times. Figure 2(e) shows the position of the student during sit and reach test

assessment.

Figure 2. The Set of SEGAK Tests

The scores for each item of the tests were standardized based on students’ gender and

age. The scores for each item were summed up to acquire the total score as follows:

(1)

Based on the total score, the SEGAK grades for the students were obtained. Table 1

shows the grade and score allocation for SEGAK.

Table 1. SEGAK Grade and Score Allocation

Total Score SEGAK Grade Achievement Fitness Level

18 – 20 A 4 stars Best

15 – 17 B 3 stars Good

12 – 14 C 2 stars Normal

8 – 11 D 1 star Below Normal

4 – 7 E No star Not Active


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3. Proposed Framework

Figure 3 shows the framework of the HEMS. The framework is divided into three

phases; data submission, data pre-processing and data analysis. Figure 4 shows the

screenshot of the HEMS.

Figure 3. Framework of HEMS Data Collection

Figure 4. HEMS Screenshot

3.1. Data Submission Phase

Data submission phase is conducted by the PJPK teacher in their respective schools.

The process starts with the SEGAK physical assessment among the students. The

assessment data is documented using HEMS template that is available on the HEMS

online portal. The HEMS data template requires the teacher to provide 13 data types

which are important to SEGAK data analysis for each student that are being assessed.

Table 2 lists the data dictionary for the HEMS data template. Completed data are then

uploaded into the HEMS centralized database through HEMS. Figure 5 shows the

screenshot of uploading form of the HEMS.


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Table 2. Data Dictionary for HEMS Template

Parameter Data Type Description

MyKid Varchar Students’ identification number

Student Name Varchar Students’ full name

Age Integer Students’ age

Gender Varchar Students’ gender

Physical Impairment Varchar Yes/No

Classroom Varchar Students’ classroom name

Assessment Date Date Assessment Date

NTB Integer Step up measure

TT/S Integer Push up measure

RTS Integer Partial curl-ups measure

JM Integer Sit and reach measure

Weight Float Students’ weight

Height Float Students’ height

Figure 5. SEGAK Data Upload Form

3.2. Data Pre-processing Phase

Since the data in the real world may be incomplete, noisy and inconsistent, the data

pre-processing become a crucial issue in data warehouse and data mining [12]. During

this process, some rules were applied in order to validate the uploaded data. Every file

uploaded by the teachers is being automatically validated. Valid data are added into

HEMS database while data with errors are cleaned using defined rules. If the data cannot

be cleaned, it will be ignored and eliminated. During the cleaning process, four (4)

parameters are being looked at, namely, MyKid, age, gender and assessment date.

3.2.1. MyKid: MyKid is the most important data since it will assign as primary key in the

database. The data with empty MyKid will not be accepted. The MyKid data is cleaned

first by removing the symbols such as (-) and (.) that may cause the data inconsistencies.

There are two main patterns of MyKid; birth certificate (aa00000) and MyKid number

(000000000000). In order to standardize the form of MyKid, a digit (0) will be

concatenated to the MyKid in order to get the correct length of MyKid. This data error

commonly happened due to the incorrect data input in excel file. Figure 6 shows the

pseudocode for MyKid cleaning and validation.


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Figure 6. Pseudocode for MyKid Cleaning and Validation

3.2.2. Age: Age is another data type that is also crucial for the data analysis. Hence, this

data should be cleaned from any noises. The data were cleaned by first removing not-digit

characters. The input age should be in a range between 10 to 17 years old. If the age was

less than 10 or greater than 17 years old, the data will be rejected since it become an

outlier. Figure 7 shows the pseudocode for age cleaning and validation.

Figure 7. Pseudocode for Age Cleaning and Validation

3.2.3. Gender: Gender is another data that commonly have problems during the data

entry. Firstly, the spaces in the data were trimmed. Then the length of the data was

checked. If the length is 1, the value of the data then checked. The inconsistencies of data

may occur here such as the value of data is entered as ‘l’ instead of ‘L’. The value then

will be repaired. If the length is 1 but the value is not equal to ‘P’ or ‘L’, the data will be

rejected. Other than that, if the length is more than 1, the first character of the string will

be checked. If the first character is equal to ‘P’ or ‘L’, the data will be accepted, or else it

will be rejected. Figure 8 shows the pseudocode for gender cleaning and validation.

Figure 8. Pseudocode for Gender Cleaning and Validation


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3.2.4. Assessment Date: Assessment Date is a sensitive data input and commonly prone

to data error. The data need to be cleaned in order to maintain the consistency for the

purpose of database entry. The common date data errors when the month was type using

words (e.g., ’01 January 2015’), usage of single digit on day and/or month (e.g.,

‘1/1/2015’), usage of two digits for year (e.g., ‘1/1/15’) and inconsistent use of date

separator (e.g., ‘1/1/2015’, ‘1-1-2015’ or ‘1.1.2015’). Data cleaning was applied to the

data to make the data format consistent. The standard pattern of a date was declared,

yyyy-mm-dd or yy-mm-dd. The input data with another pattern will be rewritten. Figure 9

shows the pseudocode for assessment date cleaning and validation.

Figure 9. Pseudocode for Assessment Date Cleaning and Validation

3.2.5. Final Validation: Final validation is the final process that will determine whether

to use or to eliminate the data. During the process, a rule was created where MyKid, BMI

category and SEGAK grade must not be empty or missing. If these three crucial data are

missing, the whole set of data for a student will be automatically rejected. Figure 10

shows the pseudocode for the final validation.

Figure 10. Pseudocode for Final Validation

4. Data Collection

This study involves students from primary schools (aged between 10 and 12 years) in

standard 4 to 6 and secondary schools (aged between 13 and 17 years) in form 1 to 5, in

all the seven districts of Terengganu. A total of 366 primary schools and 146 secondary

schools were involved. SEGAK was examined on all students during PJPK class. A total

of 35,681 data was collected from primary schools, and 27,201 from secondary school,

giving a big total of 67,519 data. However, 4,637 data or 6.9% of the collected data was

excluded during the pre-processing process due to incomplete information. Table 3 lists

the submission statistic according to district. Figure 11 shows the screenshot of the

submission statistic on the HEMS.


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Table 3. Statistic of Data Submission

District Primary School High School

No. of

School

No. of

Submission

% of

Submission

No. of

School

No. of

Submission

% of

Submission

Besut 54 28 52% 19 5 26%

Dungun 48 21 44% 23 6 26%

Hulu

Terengganu

48 28 58% 15 6 40%

Kemaman 47 27 57% 24 6 25%

Kuala

Terengganu

98 66 67% 39 13 33%

Marang 28 15 54% 13 4 31%

Setiu 43 28 65% 13 4 31%

TOTAL 366 213 58.2% 146 44 30.1%

Figure 11. The Screenshot of Submission Statistic

Figure 12 shows the total number of students’ data collected by HEMS based on

districts, age and gender. The highest data collected were from Kuala Terengganu with

41.3%, while the lowest data collected were from Setiu with 12.0%. The total number of

female students is 30,960 while the total number of male students is 31,922. Almost

56.7% of the collected data were from the primary schools (age 10-12) and 43.3% of the

collected data were from the secondary schools (age 13-17).


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Figure 12. Number of Students Based on District, Age and Gender

Figure 13 shows the total number of schools submitted SEGAK data using HEMS

based on districts and school level. The highest number of school who has submitted the

data is from primary schools in Kuala Terengganu with 66 schools, while the lowest

number of school who has submitted the data is from secondary schools in Marang and

Setiu with only four schools each. The total number of primary schools is 366 while the

total number of secondary schools is 146. From the total number of schools (512), almost

58.2% of the primary schools and 30.1% of the secondary schools have submitted the

SEGAK data via HEMS. Since the primary schools in two districts; Kuala Terengganu

and Besut were involved directly during our study, the number of schools submitted the

data was significantly different compared to other districts.

Figure 13. Number of Primary and Secondary Schools Based on District


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5. Data Analysis Phase

During the data analysis phase, the cleaned data from data pre-processing phase were

analyzed. The HEMS generates two types of report i.e., submission report and SEGAK

report. While SEGAK report can be further categorized into three types of sub-report;

BMI category report, SEGAK grade report and BMI category vs. SEGAK grade report.

The submission report was reported based on district, gender and school level (primary

or secondary school). This report depicts the number and percentage of data submitted via

the HEMS (Figure 14). While the BMI category and SEGAK grade report were reported

based on age, BMI category / SEGAK grade and gender (Figure 15). Besides that, BMI

category vs. SEGAK grade report also will be reported (Figure 16). The data in this report

were presented based on district, school and student.

Figure 14. Screenshot of Submission Report

Figure 15. Screenshot of BMI Category Report


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Figure 16. Screenshot of BMI Category vs. SEGAK Grade Report

6. Results and Discussions

Figure 17 shows the percentage of SEGAK grade among school children in

Terengganu. The majority of students managed to score B and C in the SEGAK

assessment, with 31% and 51% respectively. This means the majority of the students are

either Good or Normal in terms of SEGAK fitness.

Figure 17. Percentage of SEGAK Grade among School Children in Terengganu

Figure 18 shows the proportion of students based on SEGAK grade and age. The graph

shows that majority of students' scores C in SEGAK grades across all ages. From the

collected data, 1,753 of male students and 1,756 of the female students have scored A,

10,131 of male students and 9,271 of female students have scored B. Almost a total of

51% of students have scored C of which 16,271 and 15,936, are male and female

respectively. Whereas 3,204 of male students and 3,401 of female students have scored D.

The number of students scored in E was the lowest. They were 563 of male students and

596 of female students. The graph also shows that a gender difference does not give any

significant impact on the SEGAK grade scores.


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Figure 18. Number of Students on SEGAK Grade by Age and Gender

Figure 19 shows the percentage of students based on SEGAK grade and district. From

the collected data, the percentage of students in each SEGAK grade based on seven

districts in Terengganu was not significantly different. The percentage of students from

Kuala Terengganu was highest among each SEGAK grade. Meanwhile, the percentage of

students from Marang was lowest among each SEGAK grade. The distributions of

SEGAK grade by the district shows that most students from Kuala Terengganu have

scored the highest.

Figure 19. Number of Students on SEGAK Grade by District

The percentage students based on SEGAK grade stratified according to age and gender

is shown in Table 4. It shows that 51.2% or 32,207 of students recorded a normal SEGAK

grade and 3,186 of male students recorded the highest normal SEGAK grade represented


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by 51.5%. From the total number of students, 62,882 (1.8%) were not active. Most of the

male students had recorded a low percentage in grade E. The distribution of SEGAK

grades by ages and genders indicates that most of the students scored C.

Table 4. Percentage of Students on SEGAK Grade Based on Age and Gender

Age

(years

old)

Gender Total

Student

Scale of SEGAK Grade

A B C D E

Total % Total % Total % Total % Total %

10 Male 5845 297 5.1 1819 31.1 2847 48.7 739 12.6 143 2.4

Female 5800 312 5.4 1723 29.7 2922 50.4 685 11.8 158 2.7

11 Male 6247 376 6.0 1905 30.5 3082 49.3 744 11.9 140 2.2

Female 5795 360 6.2 1857 32.0 2845 49.1 610 10.5 123 2.1

12 Male 6189 334 5.4 1816 29.3 3186 51.5 719 11.6 134 2.2

Female 5805 360 6.2 1746 30.1 2966 51.1 615 10.6 118 2.0

13 Male 2809 88 3.1 837 29.8 1603 57.1 248 8.8 33 1.2

Female 2521 164 6.5 909 36.1 1188 47.1 240 9.5 20 0.8

14 Male 2769 145 5.2 798 28.8 1588 57.3 212 7.7 26 0.9

Female 2724 147 5.4 828 30.4 1392 51.1 302 11.1 55 2.0

15 Male 3083 176 5.7 1125 36.5 1570 50.9 175 5.7 37 1.2

Female 2678 113 4.2 718 26.8 1459 54.5 338 12.6 50 1.9

16 Male 2482 191 7.7 883 35.6 1207 48.6 178 7.2 23 0.9

Female 2843 162 5.7 780 27.4 1570 55.2 294 10.3 37 1.3

17 Male 2498 146 5.8 948 38.0 1188 47.6 189 7.6 27 1.1

Female 2794 138 4.9 710 25.4 1594 57.1 317 11.3 35 1.3

Total 62882 3509 5.6 19402 30.9 32207 51.2 6605 10.5 1159 1.8

The percentage of students based on SEGAK grade stratified according to the districts

and gender is shown in Table 5. The highest number of male and female students is from

Kuala Terengganu who have scored C, 6,875 (21.3%) and 6,515 (20.2%), respectively.

While the lowest number of male and female students is from Kemaman who have scored

E, 89 (7.7%) and 138 (11.9%), respectively. The distribution of SEGAK grades according

to districts and genders shows that most of the students scored C.

Table 5. Percentage of Students on SEGAK Grade Based on District and Gender

Districts Gender Total

Student

Scale of SEGAK Grade

A B C D E

Total % Total % Total % Total % Total %

KTG Male 13289 639 4.8 4088 30.8 6875 51.7 1393 10.5 291 2.2

Female 12712 784 6.2 4011 31.6 6515 51.3 1164 9.2 238 1.9

MRG Male 2361 139 5.9 830 35.2 1202 50.9 168 7.1 22 0.9

Female 2279 121 5.3 835 36.6 1178 51.7 132 5.8 13 0.6

HTG Male 2438 184 7.5 877 36.0 1157 47.5 182 7.5 38 1.6

Female 2173 151 6.9 756 34.8 1052 48.4 183 8.4 31 1.4

DGN Male 3509 198 5.6 1224 34.9 1626 46.3 408 11.6 53 1.5

Female 3434 209 6.1 1036 30.2 1616 47.1 474 13.8 99 2.9

KMM Male 4333 196 4.5 1190 27.5 2290 52.9 568 13.1 89 2.1

Female 4380 153 3.5 907 20.7 2414 55.1 768 17.5 138 3.2

STU Male 2172 177 8.1 790 36.4 1037 47.4 146 6.7 22 1.0

Female 2253 139 6.2 705 31.3 1189 52.8 193 8.6 27 1.2

BST Male 3820 220 5.8 1131 29.6 2082 54.5 339 8.9 48 1.3

Female 3729 199 5.3 1021 27.4 1972 52.9 487 13.1 50 1.3

Total 62882 3509 5.6 19401 30.9 32205 51.2 6605 10.5 1159 1.8


Vol. 10, No. 10 (2016)


7. Conclusion

The SEGAK assessment may help the related authorities in monitoring students’

fitness level regularly. However, poor data management and analysis had created

drawbacks for the monitoring exercise. This study proposes an automated system called

HEMS to help the ministry and the schools in managing and analyzing SEGAK data

effectively. All submitted data are being pre-processed in order to ensure the reliability of

the data submitted. During pre-processing, almost 21% of the submitted data were

removed due to the incorrectness and incompleteness of data. This incorrect and

incomplete data should be filtered out in order to ensure the accuracy and reliability of the

analysis results.

Acknowledgments

This study was funded by Ministry of Higher Education via Fundamental Research

Grant Scheme, Grant no. [FRGS/2/2013/SKK/UNISZA/01/1].

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Vol. 10, No. 10 (2016)


Authors

Fadzli Syed Abdullah, received his PhD in Engineering

(Computational Engineering) from Cardiff University in 2012, M.Sc

in Computer Science from Universiti Teknologi Malaysia (UTM) in

2005 and B.Sc in Management Information System from Universiti

Islam Antarabangsa Malaysia (UIAM) in 2001. Currently he is a

senior lecturer at Faculty of Informatics and Computing, UniSZA.

His research interests are in the area of Knowledge Engineering

including text-based information retrieval, natural language

processing, semantic based systems and ontology based systems also

Software Engineering.

Nor Saidah Abd Manan, received B.Sc. in Computer Science

(Software Development) from Universiti Sultan Zainal Abidin

(UniSZA), Terengganu, Malaysia in 2014. Currently she is doing her

masters at UniSZA, Her research interests are in Software

Development, Data Management and Data Mining.

Aryati Ahmad, received her PhD in Dietetics from University of

Surrey in 2013 and B.Sc in Dietetics from Universiti Kebangsaan

Malaysia (UKM) in 2007. Currently, she is a senior lecturer at

Faculty of Health Science, UniSZA. Her research interests are in the

area of dietetics and nutrition including nutrition and metabolism,

clinical dietetics, Atherogenic Lipoprotein Phenotype, Carbohydrate

and CVD.

Syarifah Wajihah Wafa, received her PhD from University of

Glasgow in 2013, M.Sc from Glasgow Caledonian University in 2007

and B.Sc in Nutririon from Universiti Putra Malaysia (UPM) in 2006.

Now she is a senior lecturer at Faculty of Health Science, UniSZA.

Her research interests are in the area of childhood obesity and

community nutrition.

Mohd Razif Shahril, received his PhD in Nutrition from UniSZA

in 2014 and B.Sc in Nutrition from UKM in 2005. Currently, he is a

senior lecturer at Faculty of Health Science, UniSZA. His research

interests are in the area of Nutritional Epidemiology, Obesity, Dietary

Assessment, Cancer Prevention, Oncology Nutrition and Nutrition

Education.


Vol. 10, No. 10 (2016)


Nurzaime Zulaily, received her BSc in Dietetics from UniSZA in

2014. Currently, she is doing her masters at UniSZA. Her research

interests are in the area of childhood obesity and community dietetics.

Rahmah Mohd Amin, received her PhD in Gerantology from

Keele University in 2005, M.Sc in Public Health from UKM in 2005

and MD from Universiti Sains Malaysia (USM) in 1987. Currently,

she is an associate professor at Faculty of Medicine, UniSZA. Her

research interests are in the area of Public Health and Gerantology.

Amran Ahmed, received his PhD in Medical Statistics from

Hiroshima University, M.Sc in Statistics from UKM and B.Sc in

Mathematics from USM. Currently, he is a dean and professor at

Institute of Engineering Mathematics, Universiti Malaysia Perlis

(UniMaP). His research interests are in the area of Statistics.

Documents

HEMS: Automated Online System for SEGAK Analysis and Reporting