What’s What FSKM edisi 1_2021

Fakulti Sains Komputer dan Matematik UiTM Cawangan Negeri Sembilan Kampus Kuala Pilah What’s What FSKM eISSN: 2756-7729 VOLUME 1/2021 EDITORIAL BOARD PATRON Prof. Dr. Yamin Yasin HEAD Siti Noor Dina Ahmad CHIEF EDITOR Intan Syaherra Ramli EDITORIAL COMMITTEE Norlis Osman Ts. Dr. Ratna Zuarni Ramli Jamaliah Mohd Taib Nor Ashikin Sahrom Mahfuzah Mahayadin GRAPHIC AND LAYOUT Siti Noor Dina Ahmad Siti Zaharah Mohd Ruslan Dr. Nur Ida Aniza Rusli [email protected] FSKM UITM Kuala Pilah FSKM UITM Kuala Pilah

What’s What FSKM Volume 1, 2021 Published by: Universiti Teknologi MARA (UiTM) Cawangan Negeri Sembilan Pekan Parit Tinggi, 72000 Kuala Pilah Negeri Sembilan, MALAYSIA Tel: 606-4832100 Fax: 606-4842449 No part of this ebulletin may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without the written consent of the publisher. DISCLAIMER: This ebulletin is made for educational purpose and to provide general information only. The materials on this bulletin comprise the author’s views; they do not constitute legal or other professional advice. Readers are advised to consult appropriate professional resources for legal or other advice. FSKM accepts no responsibility for the consequences of error or for any loss or damage suffered by readers who use any information and/or materials contained in this bulletin. eISSN: 2756-7729 National Library of Malaysia This ebulletin is published three times a year Copyright 2021 FSKM_UiTMKP

CONTENT READER DIGEST 4 8 3 Best Google Forms Add-Ons for Educators 10 Regression Model: Choose the Right One Mortality Studies: Little Did We Know? 14 16 FSKM ACTIVITIES 18 20 ODL Webinar Series Program Bicara Santai 22 Tabung COVID-19: Kita Jaga Kita Bicara Wasiat & Pengurusan Harta Pusaka Menurut Islam ACHIEVEMENTS

READER Best Google Forms DIGEST 3 Add-Ons for Educators Prepared by Siti Noor Dina Ahmad Recently, Google Forms has You've come to the right gained popularity among place if you're a teacher or educators. This is because administrator who is looking today's education is more for Google Forms add-ons. focused on online raw The following add-ons let materials since the you do plenty of things like pandemic of COVID-19 hit analyzing your student's all countries of the globe. responses, creating time- The easiness and user- bound quizzes or shut/lock friendly features of Google the form's responses after a Forms make it one of the particular time. platforms of choice for both Let's get started. teachers and lecturers. One of the tools of Google Forms is students are able to see their marks once they submit the form or in this case is the test. However, there are some features that Google Forms is lacking such as unable to set the time limit to answer and incapable to lock automatically the form after a certain number of responses. 4 Copyright 2021 FSKM_UiTMKP

1. formLimiter formLimiter is the best to levy a limit on the responses for the forms. This nifty add-on gives you two options; you can either set the number of responses, or you can specify the final date and time to response. The time feature can be widely used in assignments and quizzes, where you do not want students to spend their own time submitting their assignments. Copyright 2021 FSKM_UiTMKP 5

2. Advanced Summary by Awesome Table Your Google Forms Response tab automatically creates beautiful graphics and charts. However, if you want to add a little customization, you can switch to Advanced Summary by Awesome Table add-on. It comes with a useful filter that allows you to filter the response as well. Additionally, there are useful Timestamp graphics that illustrate the response times and answers. 6 Copyright 2021 FSKM_UiTMKP

3. Certify‘em Certify’em allows you to create certifications online through Google Forms. This add-on provides several templates that you can utilize to create professional designed certificates. Of course, you can also make your own. You have the option to email your custom certificate in PDF file format as well. Another welcome feature of Certify'em is that it automatically saves the latest logs of exam if you need to refer later. Copyright 2021 FSKM_UiTMKP 7

Regression Choose the Model: Right One!!! Prepared by Zuraida Jaafar Regression analysis is a set of There are numerous types of regression models that can be statistical methods used for the used. This choice often depends estimation of relationships on the kind of data for the between a dependent variable and one or more independent dependent variable and the type variables. It can be utilized to of model that provides the best fit. assess the strength of the relationship between variables and Researchers may choose for modelling the future relationship between them. appropriate regression models Regression analysis can be used to based on the type of dependent answer those questions such as: Do socio-economic status and race variables such as continuous, affect educational achievement? ●Do education and IQ affect categorical or count data. earnings? ●Do exercise habits and diet affect weight? ●Are drinking coffee and smoking cigarettes related to mortality risk? References: 1. David A. Freedman (2009). Statistical Models: Theory and Practice. Cambridge University Press. p. 26. 2. Bates, D. M., & Watts, D. G. (1988). Nonlinear regression analysis and its applications. New York: John Wiley & Sons. 3. Darroch, J.N. & Ratcliff, D. (1972). \"Generalized iterative scaling for log-linear models\". The Annals of Mathematical Statistics. 43 (5): 1470–1480. 4. Menard, Scott (2002). Applied Logistic Regression Analysis. SAGE. p. 91 5. Long, J. S. and Freese, J. (2006). Regression Models for Categorical Dependent Variables Using Stata, Second Edition. College Station, TX: Stata Press. 8 Copyright 2021 FSKM_UiTMKP

Table below is the list of several common regression model according to different types of dependent variables. Type of Dependent Regression Model Description Variable Linear Linear approach to model the relationship between Continuous Example of dependent variable: dependent variable and one or more independent academic performance variables. ▪Numerical data (CGPA=3.85) which arises from ������������ = ������0+ ������1������������1 + ⋯ + ������������������������������ + ������������ measuring process Partial Least Square (PLS) ▪Example: weight, Example of dependent variable: Used to find the relationships when there are very time, and length. Customer satisfaction score few observations compared to the number of independent variables or when independent Non-Linear variables are highly correlated. Example of dependent variable: Population growth ������ Categorical Binary Logistic ������������������ = ෍ ������������������������������ + ������������������ Example of dependent variable: ▪ Values that can Decision to vaccinate (yes, no) ������=0 be put into a countable number Ordinal Logistic Model in which observational data are modelled by of distinct groups Example of dependent variable: a function which is a nonlinear combination of the based on a Internet addiction level (none, model parameters. characteristic. mild, severe) ������~������(������, ������) ▪Categorical data Nominal Logistic might not have a Example of dependent variable: Used to predict the probability that an observation logical order. Profession (surgeon, doctor, falls into one of two categories of a dichotomous nurse, dentist, therapist) dependent variable based on one or more independent variables that can be either continuous or categorical. 1 ������ ������ = ������ ������ = 1 + ������−(������0+������1������) Used to predict an ordinal dependent variable given one or more independent variables. 1 ������ ������������ − w ∙ x = 1 + ������−(������������−w∙x) Used when the dependent variable in question is nominal (categorical) and for which there are more than two categories. Pr ������������ = ������ = ������������������ ∙ X������ σ������������=1 ������������������������∙X������ Count Poisson Used to predict a dependent variable that consists Example of dependent variable: ▪Count of items, Number of children in a family of count data given one or more independent events, results, or activities. Negative Binomial variables. Example of dependent variable: log E ������ x = ������ + ������′x) ▪Numerical data Number of accidents in Negeri which arises from Sembilan. Used to predict a dependent variable that consists counting process. of count data. It is known as NB2, which can be more appropriate when the variance of count data is greater than the mean count (overdispersion). Copyright 2021 FSKM_UiTMKP 9

MORTALITY STUDIES: LITTLE DID WE KNOW? Prepared by Nurul Aityqah Yaacob Why do we study mortality? As an example, actuaries applied mortality forecasts Mortality registration is for cash flow projections and assessment of premium and mandatory in almost all reserves in life insurance and pension. Mortality countries and hence studies contain modeling mortality plus mortality mortality studies offer a forecasts. valuable measure for assessing our community health status. Improving mortality in population will lead to improvements in When studying mortality, a public health, medical useful framework is the advances, lifestyle changes, difference in mortality rates and government regulation. between clusters. A No doubt, mortality studies differential between two play a very significant role in groups is simply measured if numerous areas such as the the groups vary by a fixed pension systems, insurance characteristic such as sex or sectors, actuarial, age. According to demographics and Tuljapurkar & Shripad epidemiological research. (1998), there have five characteristics to consider in the mortality studies, which are marital status, sex, racial and ethnic, education and social-economic variables and also genetic variation. 10 Copyright 2021 FSKM_UiTMKP

Expectation Explanation Extrapolation Based on expert Restricted to certain Use of statistical opinion incorporation causes of death methods assume that future trends will be a of demographic, feedback mechanisms epidemiological and and limiting factors continuation of the can be taken into pass implausible age other relevant account. Difficult to knowledge subjective obtain the data. patterns. potential for bias. Many models have been However, the model does not give sufficiently good proposed since Gompertz prediction for every country (Baran et.al, 2007). Besides published his law of that, the LC approach also can produce the violation of mortality in 1825. There the homoscedasticity in the error term (Zhao, 2012). For have been three methods in these reasons, many models have been suggested in demographic forecasting forecasting mortality such as Lee-Miller (LM) model, such as extrapolation, Booth-Maindonald-Smith (BMS) model and Hyndman- explanation, and Ullah (HU) model. expectation (Booth & Tickle, 2008). Time-series methods are frequently used in extrapolative predicting. The Lee-Carter (LC) model is one of the most popular extrapolation models amongst researchers. The model was proposed by Lee and Carter since 1992 and was fitted to the United States population (Lee & Carter, 1992). . Copyright 2021 FSKM_UiTMKP 11

Did you know, not one mortality model performs better than the other in every aspect. Therefore, Cairns et al. (2006) defined standards against which a model can be evaluated. The key standard that can be highlighted is that the model must be selected carefully based on available data and is applicable to a full age range in order to achieve a good future demographic forecast for each country (Plat, 2009). In conclusion, mortality studies are very important as it is a single indicator which can show the general health problems of a population and beyond that for the social system. 12 Copyright 2021 FSKM_UiTMKP

References: 1. Baran, S., Gáll, J., Ispány, M., & Pap, G. (2007). Forecasting Hungarian mortality rates using the Lee- Carter method. Acta Oeconomica, 57(1), 21-34. 2. Booth, H., & Tickle, L. (2008). Mortality Modelling and Forecasting: a Review of Methods. Annals of Actuarial Science, 3(1–2), 3–43. 3. Cairns, A. J. G., Blake, D., & Dowd, K. (2006). A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty. Journal of Risk and Insurance, 73(4), 687–718. 4. Lee, R. D., & Carter, L. R. (1992). Modeling and Forecasting U.S. Mortality. American Statistical Association, 87(419), 659–671. 5. Plat, R. (2009). On stochastic mortality modeling. Insurance: Mathematics and Economics, 45(3), 393– 404. 6. Tuljapurkar, Shripad, and C. B. (1998). Mortality Change and Forecasting : How Much and How Little Do We Know ? North American Actuarial Journal, 2(4), 13–47. 7. Zhao, B. B. (2012). A modified Lee–Carter model for analysing short-base-period data. Population Studies, 66(1), 39-52. Copyright 2021 FSKM_UiTMKP 13

FSKM ACTIVITIES ODL WEBINAR SERIES Prepared by Norul Fadhilah Ismail & Mahfuzah Mahayadin FSKM KP has organised an Kuala Pilah Facebook Live ODL Webinar Series on 2-4 that takes two hours for September 2020 during each sessions. FSKM Digital Week. There are five series of ODL The objectives of this Webinar that successfully run during the week namely program are to share the 1) Google Form: Creating informations and knowledge and Grading Online Assessment among staff at UiTM Negeri 2) Microsoft Teams: The Sembilan and others in Introduction using Google Forms and 3) Jamboard: Collaborative Digital Whiteboard with Google Slides interactively Google Meet for the purpose of building 4) Interactive Teaching and Learning using EdPuzzle online questions and to 5) How to Connect your produce an effective Device to Smart TV using WiFi Dongle? teaching aids using The webinars have been Microsoft Teams and recorded using Google Meet application and FSKM UiTM EdPuzzle. 14 Copyright 2021 FSKM_UiTMKP

Besides, there are demonstration on the use of WiFi Dongle to connect the devices like smartphone and laptop to Smart TV. This program has received a very encouraging response among UiTM staff and most of them are satisfied with the programs and it is very useful in teaching and learning process especially through Open and Distance Learning (ODL) during this pandemic COVID-19. Copyright 2021 FSKM_UiTMKP 15

Program Bicara Santai: Cabaran, Etika dan Terokai Dunia Maya Dengan Al-Quran Prepared by Nadiah Mohamed and Norlis Osman On 23rd September 2020, The first panel, Elina FSKM KP organized a program called “Program Mubin, an Executive of Bicara Santai: Cabaran, Etika Dan Terokai Dunia Outreach and Corporate Maya Dengan Al-Quran”. Three panels were invited to Communications at the program to share with the audience the importance Cybersecurity Malaysia, of being safe in the cyberworld and role of discussed issues of digital science in our everyday life and the relevancy to Quran. literacy in “Always Wise, The topics were chosen to stimulate interests of STEMS Always Safe: Are You Digital to students and also as public awareness. Fluent. Mrs Elina stressed on how parents should be aware of ways to make sure the Internet is safe for their children and tips on monitoring their children’s surfing activity. 16 Copyright 2021 FSKM_UiTMKP

Studies, Center for General Studies. He discussed the relationship between Science and Quran in “Explore the Science of Engineering Technology and Mathematics in the Quran. The panel showed several existing scientific events The second panel, from the happening and Malaysian Communications and Multimedia Commission mathematical formulas that (MCMC), Saidatul Ashikin Abu Hassan talked about can be traced its origin to computer ethics with her topic “Click Wisely! What Is the Quran. The interesting Wrong Outside Is Still Wrong Online ”. Mrs parallels between science Saidatul brings forth the issues of computer ethics and Quran captures the faced by users when online and relates to the laws and audience's attention and Act in Malaysia. hopefully can bring forth The third for this program is from the University of renewed interest in STEM. Selangor (UNISEL), Ts. Muhammad Nazir bin Mohammed Khalid, who is a Senior Lecturer in Electrical and Electronic Engineering at the Department of Basic Copyright 2021 FSKM_UiTMKP 17

TABUNG COVID-19: KITA JAGA KITA Prepared by Dr. Nur Ida Aniza Rusli FSKM KP, UiTM Negeri to 15th September 2020. Throughout this event, Sembilan Kampus Kuala FSKM managed to collect RM520 and received Pilah has organized a essential items such as thermometers, face masks, donation program named and face mask extenders from individual donors . “Tabung COVID-19: Kita Jaga Kita” on 25th September 2020. The main objective of the program is to lessen the burden and to help rural communities living in Kampung Beting who were affected by the pandemic COVID-19. Additionally, this program also aimed to create a spirit of good deeds among UiTM Kuala Pilah staff and also the public community. The fundraising program has been conducted for a week, starting from 9th September 18 Copyright 2021 FSKM_UiTMKP

The collected fund was used to Pusat Islam UiTM buy COVID-19 supplies such as Cawangan Negeri Sembilan face masks, hand sanitizer and also hand wash soap. All items (Kampus Kuala Pilah) were then distributed by FSKM’s staff to five locations namely Pusat Islam UiTM Cawangan Negeri Sembilan, Masjid Kariah Kampung Beting, Sekolah Kebangsaan Dato’ Inas, Sekolah Kebangsaan Tunku Munawir, and also Sekolah Kebangsaan Pelangai. Masjid Kariah Kampung Beting Sekolah Kebangsaan Dato’ Inas Sekolah Kebangsaan Tunku Munawir Sekolah Kebangsaan Pelangai Copyright 2021 FSKM_UiTMKP 19

BICARA WASIAT & PENGURUSAN HARTA PUSAKA MENURUT ISLAM Prepared by Norlida Othman This program has been successfully conducted by FSKM KP and collaborated with MyANGKASA Amanah Berhad and Agrobank Bahau on Wednesday 7th October 2020 through Google Meet platform. More than 900 participants have filled up the survey of Keberkesanan Program & more than 1700 viewers on FB Live in FSKM official page. This program has been hosted by Puan Norul Fadhilah binti Ismail and the talk was delivered by Encik Ahmad Sahrul bin Mohamad from MyANGKASA Amanah Berhad. 20 Copyright 2021 FSKM_UiTMKP

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ACHIVEMENTS PUBLICATION 22 Copyright 2021 FSKM_UiTMKP

INNOVATION Copyright 2021 FSKM_UiTMKP 23

Fakulti Sains Komputer dan Matematik UiTM Cawangan Negeri Sembilan Kampus Kuala Pilah [email protected] FSKM UITM Kuala Pilah FSKM UITM Kuala Pilah