bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Bootstrap Simulation for comparison of Group Risk Plan and Multi-Peril Crop Insurance Policy
Authors: Sutomo Valantino Agus , Kusumaningrum Dian , Anisa Rahma , Paramita Aryana 
Published: November 2019

Abstract
Agricultural insurance is one of the solutions for farmers to avoid risks such as climate change, pest, disease and price fall which increase the risk of crop failure. Indonesian Ministry of Agriculture (MoA) and Jasindo have been using subsidized Multi-Perils Crop Insurance (MPCI) and faced some disadvantages that mainly due to high risk moral hazard, adverse selection, and high administrative costs. Our research provides an alternative policy area yield index insurance known as Group Risk Plan (GRP) to handle this problem. To find homogeneous basis risk in GRP, the data was classified into two categories: up to and above two hectares. Bootstrapping approach was used to find MPCI and GRP indemnity amount for each term of harvesting period. In addition, both parametric estimation and goodness of fit test indicated appropriate distribution used by MPCI and GRP in each term of harvesting period as well. To compare the aggregate loss distribution, the conditional value at risk CV aR(X) measures the mean of excess loss exceeding V aR(X). It reflects how risky and measures the amount of money insurance company should retain. Simulations indicated that MPCI has greater CV aR(X) than GRP, which implies the higher likelihood of loss. The insurance company should have enough reserve for MPCI to cover that loss. On the other hand, the GRP policy that minimizes moral hazard and adverse selection thereby reduces administrative costs and offers potential to market at lower costs, may encourage farmers to do good farming practices that will result in the lower likelihood of loss. Therefore, GRP could be considered as an alternative crop insurance policy in Indonesia.
Online learning environment to enhance HOTS in mathematics using Polya’s problem solving model
Authors: Mustapha Sudirman , Rosli Mohd Shafie , Saleh Nor Shela 
Published: November 2019

Abstract
Low Higher Order Thinking Skills (HOTS) among students is a persistent and pervasive challenge in modern classroom. The problem remains for years despite the intensive manoeuvres to solve it. Yet, the need to enhance HOTS is imperative in ensuring quality education. The purpose of this research is to enhance HOTS among students in Mathematics using an online learning environment. The online learning environment was developed based on Polya’s problem solving model. However, the system is not discussed in this paper. There are three research objectives which are to investigate the current HOTS achievement among students, to study the effect of the developed online learning environment in nurturing HOTS among samples and finally to study how learning process taken place to achieve HOTS among sample while engaging the system. This research implemented experimental research design with a set of pre and posttest and the developed online learning environment as intervention. A total of 76 samples were sampled for the preliminary study and another 32 samples for the four weeks treatment using cluster random sampling technique. Samples are from three schools from a district in Malaysia. Finding shows that the current HOTS achievement among students are still low at mean = 32.05, σ = 15.87. During the intervention, samples show improvement in their HOTS from mean = 39.52, σ = 11.65 in the pretest to higher level in the posttest at mean = 77.84, σ = 12.84. The Paired-Samples T Test conducted to identify the differences between the pre and posttest shows a statistically significant difference at t (31) = -11.58, p < 0.05. Indicating that an online learning environment using Polya’s problem solving for four weeks enhance HOTS among samples. The process of how samples achieve HOTS is also reported. This research shows the importance of integrating technology and inquiry in modern classroom to increase students’ thinking skill ability. Finding suggests that HOTS can be nurtured via technology enhanced learning environment.
Direct multistep method for solving delay differential equation with boundary conditions
Authors: Jaaffar N T , Majid Z A , Senu N 
Published: November 2019

Abstract
This study will consider the implementation of direct multistep method for solving Delay Differential Equations (DDEs) with boundary conditions. The approach of this method is solving DDEs directly using the proposed method without reducing to the system of first order. To execute the direct multistep method to solve Boundary Value Problems (BVPs), we apply the shooting technique by using Newton’s method to compute the guessing value. Some numerical examples are presented to show that the proposed method are capable for solving DDEs with boundary conditions.
Speech recognition using Dynamic Time Warping (DTW)
Authors: Permanasari Yurika , Harahap Erwin H. , Ali Erwin Prayoga 
Published: November 2019

Abstract
Sound is one of the most common communication medias used by humans. Every human has different sound characteristics. To recognize the compatibility of a sound, a special algorithm is needed, which is Dynamic Time Warping (DTW). DTW is a method to measure the similarity of a pattern with different time zones. The smaller the distance produced, the more similar between the two sound patterns. Both sound patterns are similar, thus the two voices are said to be the same. The initial data on the speech recognition process is transformed into frequency waves. Pronounce volume, pronunciation time, and noise from the sound around the recording takes place affecting the distance generated. The smaller the effect, the smaller the distance that will be generated.
Quality Solution of Logic Programming in Hopfield Neural Network
Authors: Kasihmuddin M S M , Mansor M A , Alzaeemi S , Basir M F M , Sathasivam S 
Published: November 2019

Abstract
The dynamical behaviours of Artificial neural network (ANN) system are strongly dependent by its network structure. In that sense, the output of ANN has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of ANN in doing logic programming. The work presents an integrated representation of 2 Satisfiability (2SAT) in different Hopfield Neural Network (HNN) models. Neuron states of HNN always converge to minimum energy but the solution produced always confined in limited number of solution space. The main purpose of this study is to explore the quality of the solution obtained from HNN. It has been shown that HNN only retrieves limited neuron states with the lowest minimum energy. This finding will lead to a better understand of logic programming in HNN.
Rational bicubic Ball for image interpolation
Authors: Zulkifli N A , Karim S A A , Shafie A , Sarfraz M 
Published: November 2019

Abstract
In this study, a new rational bicubic Ball with six free parameters is proposed for image interpolation. These free parameters can be used to refine the shape of the surface. This rational bicubic Ball function is constructed by using tensor product approach. The proposed scheme is tested to grayscale image upscale. An efficient algorithm is employed to upscales the original image with factor 2 and 4. The effectiveness of proposed scheme is measured in terms of Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Feature SIMilarity (FSIM) index including comparison with some existing schemes. Numerical and graphical results for image interpolation are presented by using MATLAB. We found out the proposed scheme resulting with higher PSNR and FSIM and smaller in RMSE. Thus, the new rational bicubic Ball with six parameters is better than the existing schemes.
A comparison study between Doane’s and Freedman-Diaconis’ binning rule in characterizing potential water resources availability
Authors: Chuan Zun Liang , Wan Yusoff Wan Nur Syahidah , Mohd Aziz Mohd Khairul Bazli , Senawi Azlyna , Ken Tan Lit 
Published: November 2019

Abstract
One of the primary constraints for development and management of water resources is the spatial and temporal uncertainty of rainfall. This is due to the stability and reliability of water supply is dynamically associated with the spatial and temporal uncertainty of rainfall. However, this spatial and temporal uncertainty can be assessed using the intensity entropy (IE) and apportionment entropy (AE). The main objective of this study is to investigate the implications of the use of Doane’s and Freedman-Diaconis’ binning rule in characterizing potential water resource availability (PWRA), which the PWRA is assessed via the standardized intensity entropy (IE’) against the standardized apportionment entropy (AE’) scatter diagram. To pursue the objective of this study, the daily rainfall data recorded ranging from January 2008 to December 2016 at four rainfall monitoring stations located Coastal region of Kuantan District Pahang are analyzed. The analysis results illustrated that the use of Doane’s binning rule is more appropriate than Freedman-Diaconis’ binning rule. This is due to the resulted PWRA characteristics using Doane’s binning rule is relatively consistent with practical climate such that the study region is experiencing poor-in-water zone with less amount and high uncertainty of rainfall during the Southwest Monsoon, while abundant and perennial rainfall during the Northeast Monsoon. Furthermore, the use of Doane’s binning rule is more advantages compared to the Freedman-Diaconis’ binning rule with the abstraction of computational cost and time.
Imputation Analysis for Time Series Air Quality (PM10) Data Set: A Comparison of Several Methods
Authors: Shaadan N , Rahim N A M 
Published: November 2019

Abstract
Good quality data is important to guarantee for the best quality results of research analysis. However, the quality of the data often being impacted by the existence of missing values that bring bad implication on the accuracy of analysis and subsequently lead to biased results. In air quality data set, missing values problem often caused by various reasons, for example machine malfunction and errors, computer system crashes, human error and insufficient sampling used. In the case for time series modelling, complete series of data is very important to enable for the model construction. This paper aims to highlight a systematic statistical procedure and analysis on how to investigate the performance of several missing values imputation methods to solve for the problem of missing value existence when data are time series. The knowledge could help researchers to implement a comprehensive procedure in deciding a type of imputation method that suits with their data. A case study was conducted using real data set from Shah Alam air quality monitoring station. The results have shown that the missing data at the monitoring station is completely at random (MCAR). Among six imputation methods compared and based on the performance of indicators such as RMSE, MAE, AI and R2 it is shown that imputation using Kalman Filter using ARIMA model is the best appropriate method for the data set.
An empirical study on the determinants of retirement savings attitude in Malaysia!
Authors: Shariff N S M , Isah N A M 
Published: November 2019

Abstract
The awareness among working individuals in Malaysia to do extra savings for older life is still minimal. This situation involved the younger generation. They think that retirement planning is a burden for them because it involves long-term planning. Nevertheless, the uncertainty and unaware about future risks (inflation, critical illness, etc) may lead to this behavior. Due to such problems, this study aims in discussing the behavior among working individuals on retirement savings at different age of group, education levels, income levels, factors affecting this behavior a and the relationship of these factors towards retirement savings attitude. This study will use survey strategy through questionnaire to collect the primary data (qualitative study). More than 200 individuals participate in this study and each of them is asked to response the questionnaire form to represent population in Malaysia. As such, this study employs several statistical procedures such as factor analysis, t-test, analysis of variance and regression models. The findings of this study reveal that income levels, attitudes towards retirement and future expectation provide significant impact on the retirement savings behavior among adults in Malaysia.
Pricing Critical Illness Insurance Premiums Using Multiple State Continous Markov Chain Model
Authors: Pasaribu U. S. , Husniah H. , Sari RR. K. N. , Yanti A R. 
Published: November 2019

Abstract
Critical Illness Insurance is an insurance product with a lump sum benefit or cash payment if the policyholder is diagnosed with critical illness in an insurance contract. The health state change process can be observed and modeled by a multi-state Markov Chain with a time-continuous parameter. In this article, we will illustrate how the mathematics of Markov Chain can be used to develop a model of state change in critical illness in the case of a cancer patient. Health state changes process in critical illness modeled by four states Markov chain. Using transition probability which is based on transition intensity of continuous Markov chain. We will estimate the transition intensity and used it in a differential equation of transition probability. The application of Markov chain model will be used to estimate the value of the premiums in some of critical illness insurance benefit model. The premiums value will be shown in the table in section five.
Construct Validation in Secondary Data: A Guideline for Medical Data Mining
Authors: Sajid Mirza Rizwan , Muhammad Noryanti , Zakaria Roslinazairimah 
Published: November 2019

Abstract
Construct validation is an important step in the formulation of theory and its testing. Any information infers from the construct and its related hypothesis, without the validation, might be misleading the decision. In the literature, the construct validation guidelines for primary data are available but the exponential usage of secondary data mining approaches in recent times has created its need for secondary data users. However, it is a less addressed domain, specifically for medical sciences due to underdeveloped theoretical foundations of the field. Usually, an assessment of the validity of the secondary data is not performed because primary users already use validation process especially in the tool development phase. Further, secondary data users are more concerned with data mining that is a data-driven approach rather than theoretical knowledge of the field. Therefore, if researchers want to explore the hidden structures of data and try some different combinations of items which were not tested by primary users, then they should validate this newly explored group of items for the creation of knowledge of the particular field. In this paper, a guideline of the construct validation process for secondary data users with its practical issues especially validity and reliability coefficients is discussed. This paper concludes that the construct validation based on secondary data needs special attention of data mining experts and medical researchers while designing the studies to maximize the benefits of the secondary data.
Fuzzy number conjoint method to analyse students’ perceptions on the learning of calculus
Authors: Osman R , Ramli N , Badarudin Z , Ujang S , Ayub H , Asri S N F 
Published: November 2019

Abstract
The fuzzy conjoint method (FCM) based on fuzzy set has been applied in many areas such as education, management and business. However, the FCM uses the fuzzy set to define the membership function of the linguistic values, which the score of weight and overall attribute for different degrees of confidence that cannot be obtained. Besides, the fuzzy set which is in discrete form does not clearly represent human judgement. Thus, in this study, a fuzzy number conjoint method (FNCM) based on triangular fuzzy number is developed. The fuzzy similarity measure based on distance, height and area was used in the procedure. The FNCM was implemented to analyse students’ perception in the learning of calculus at one selected government institution in Selangor, Malaysia. The study involved a set of 5-options Likert scale questionnaire which is in linguistic values of strongly disagree, disagree, indifferent, agree and strongly agree. All the questions were on various items related to the learning of calculus. The findings showed that overall, students agreed at above 0.85 degree of similarity that they perceived positively in the learning of Calculus. The developed FNCM with a degree of agreement for each linguistic value can successfully evaluate the students’ perceptions in the learning of calculus. This study will be as assistance and guidance to academicians and mathematics educators to enhance students’ achievement in the learning of calculus.
A comparative Study on Graduates’ Employment in Malaysia by using Data Mining
Authors: Binti A’rifian Nur Iman Natasha , Binti Mohd Daud Nur Sakinah Amirah , Muhamad Romzi Athirah Faiz Binti , Binti Md Shahri Nur Huda Nabihan 
Published: November 2019

Abstract
This study implements data mining to extract knowledge by analysing the graduates’ employment dataset from year 2017 obtained from Ministry of Higher Education (MoHE). The objective of this study is to compare three predictive models which are Decision Tree (DT), Logistic Regression (LR) and Artificial Neural Network (ANN). Besides, this study is also done to determine the best predictive model for predicting graduates’ employment sectors whether in public sector or private sectors. Every graduate student wishes to choose the right path in determining which sectors they are going to be entered, either to the public or private sectors. Usually, most graduates in Malaysia prefer the employment in the public sector rather than the private sector. Using data mining to discover the relationship and patterns can help in making a better decision. Prediction model is a must to determine the best performance when dealing with the large data set which helps the graduates to choose a sector based on the type of data or information that he/she furnishes. Based on the analysis, Artificial Neural Network (ANN 5) is the best model in predicting placement of employed graduates whether in public sector or private sector compared to the other models. ANN 5 is the highest accuracy at 81.52% and sensitivity at 65.67% while for the specificity of ANN 5 is 91.44%. The misclassification rate of ANN 5 is 18.48% which is the lowest compared to the other models. Overall, ANN 5 is the best model to predict negative target which is graduates employed in private sector since the value of specificity is higher than sensitivity. The result of this study can be used by government, universities and other responsible agencies in order to predict whether graduates will be employed in public or private sectors.
Modeling Dengue Fever Cases by Using GSTAR(1;1) Model with Outlier Factor
Authors: Mukhaiyar Utriweni , Huda Nur’ainul Miftahul , Novita Sari RR Kurnia , Pasaribu Udjianna Sekteria 
Published: November 2019

Abstract
Dengue fever is an endemic disease transmitted through the Aedes Aegypti mosquitos. Dengue virus can be transmitted from human hosts who have been infected by the virus to the mosquitoes to be transmitted back to other humans. So that, it is possible for the virus to be transmitted to several surrounding locations. Aedes Aegypti is one of the dengue mosquitoes that likes a warm climate and not too wet or dry. In addition, many un-expected factors can cause a significant increase in the number of dengue fever cases. So that the number of dengue fever cases can increase significantly far different from other data. An observation data that has different characteristics from others is called outlier. The existence of outliers can indicate individuals or groups that have very different behavior from the most of the individuals of the dataset. Outlier data in a data set are often encountered in various kinds of data analysis. Frequently, outliers are removed to improve accuracy of the estimators. But sometimes the presence of an outlier has a certain meaning, which explanation can be lost if the outlier is removed. In this paper, modeling dengue fever cases using GSTAR(1;1) with outlier factors was firstly proposed.
Comparison between EM Algorithm and Multiple Imputation on Predicting Children’s Weight at School Entry
Authors: Avtar S S , Khuneswari G P , Abdullah A A , McColl J H , Wright C , Team GMS 
Published: November 2019

AbstractEM Algorithm and Multiple Imputation are widely used methods in dealing with missing data. Although Multiple Imputation always be the favourite choice of researcher due to its accuracy and simple application, but the issue arises whether EM algorithm perform better with several times of imputation. Both methods will be tested using different number of imputations with the help of Amelia and Mice package in R software. The imputed data sets are compared using model averaging with Corrected Akaike Information Criteria (AICC) as model selection Criterion. External validation and mean squared error of prediction (MSE(P)) are used to determine the best imputation method. Gateshead Millennium Study (GMS) data on children weight will illustrate the comparison between EM Algorithm and Multiple imputation. The results show that Multiple imputation performs slightly better compared to EM Algorithm.
ARIMA and Symmetric GARCH-type Models in Forecasting Malaysia Gold Price
Authors: Yaziz Siti Roslindar , Zakaria Roslinazairimah , Suhartono  
Published: November 2019

Abstract
Gold price modelling is crucial in gold price pattern determination since the information can be used for investors to enter and exit the market. The model selection is important and corresponds to the gold price movement characteristics. This study examines the forecasting performance of autoregressive integrated moving average (ARIMA) with symmetric generalised autoregressive conditional heteroscedastic (GARCH)-type models (standard GARCH, IGARCH and GARCH-M) under three types of innovations that are Gaussian, t and generalized error distributions to model gold price. The proposed models are employed to daily Malaysia gold price from year 2003 to 2014. The empirical results indicate that ARIMA(0,1,0) - standard GARCH(1,1) using t innovations is the most preferred ARIMA with symmetric GARCH-type model.
Isolated truncated chain deferred sampling plan for Weibull product life distribution
Authors: Braimah J O , Osanaiye P A 
Published: November 2019

Abstract
In this article, an Isolated Truncated Chain deferred Sampling Plan for Weibull product life distribution is proposed when the testing is truncated at a specified time. This type of sampling plan is used to save the testing time. The optimal sample sizes, required for testing product quality to ascertain a true mean life are obtained under a given Maximum Allowable Percent Defective, test termination ratios and acceptance numbers. The operating characteristics formula of the proposed plan was developed. The operating characteristics and mean-ratio were used to assess the performance of the plan. The study revealed that: Weibull distribution have low failure rate; as mean life ratio increase, the failure rate reduces and the minimum sample size increase as the acceptance number, maximum allowable percent defective and experiment time ratio increases; The study concluded that the modified required minimum sample sizes were smaller compared to those in the literature making it a more economical plan to be adopted when time and cost of production is expensive and the testing is destructive.