Analisis tahap pencapaian pelajar di Fakulti Sains dan Teknologi, UKM dengan menggunakan model linear berhierarki

Penggunaan Model Linear Berhierarki dengan tiga aras telah digunakan di dalam kajian mengenai tahap pencapaian pelajar semester 1 sesi 2003/2004 dengan mengambilkira variasi antara program dan pusat pengajian yang berlainan di dalam Fakulti Sains dan Teknologi (FST). Sebanyak 1088 data mengenai PNGK pelajar telah digunakan di dalam kajian ini. Aras pertama adalah aras pelajar, aras kedua pula adalah program yang ditawarkan oleh pusat pengajian bagi pelajar sarjana muda dan pusat pengajian diwakilkan oleh aras ketiga. Secara keseluruhannya, apabila variasi di kalangan pelajar mengikut program dan pusat pengajian diambilkira, hasil analisis menunjukkan bahawa pelajar perempuan mempunyai min keputusan PNGK yang lebih baik berbanding dengan min keputusan PNGK bagi pelajar lelaki. Kaum bukan bumiputera juga menunjukkan min PNGK yang lebih baik berbanding dengan min PNGK bagi kaum bumiputera. Analisis terbahagi kepada dua jenis data iaitu data yang mempunyai kesemua 22 buah program dan data di mana program Sains Aktuari tidak dimasukkan ke dalam model. Ini adalah kerana min PNGK bagi program Sains Aktuari adalah sangat tinggi berbanding dengan min PNGK bagi program-program yang lain. Pemboleh ubah jantina dan kaum adalah beerti pada setiap model yang dibina. Manakala pemboleh ubah jumlah pelajar mengikut program dan peratus pelajar perempuan mengikut program adalah beerti apabila dimasukkan ke dalam model. Bagi analisis di mana data program Sains Aktuari tidak dimasukkan ke dalam model menunjukkan bahawa pemboleh ubah jantina dan kaum adalah beerti bagi setiap model yang dibina. Tetapi pemboleh ubah bagi peratus jumlah pelajar perempuan bagi setiap program sahaja yang beerti apabila pemboleh ubah penerang pada aras program dimasukkan ke dalam model. Bagi model penuh iaitu di mana kesemua pemboleh ubah penerang dimasukkan ke dalam model, menunjukkan pemboleh ubah bagi peratus jumlah pelajar bumiputera dan peratus jumlah pelajar perempuan bagi setiap pusat serta nisbah bagi seorang pengajar kepada pelajar mengikut pusat pengajian adalah beerti

Monitoring process variability and root cause analysis in paper box production

In this paper, monitoring procedure for process variability in multivariate setting based on individual observations which is a combination of (i) Hotelling’s T 2 control chart in detecting out of control signal and (ii) implementation of Mason, Young and Tracy (MYT) decomposition and structure analysis technique for root cause analysis is introduced. The advantages of this procedure will be shown by using the case of a paper box production process in one of the Malaysian manufacturing companies. The successful application of this multivariate approach could act as a stimulant for most industries to imitate in process monitoring. Moreover, the computation efficiency in root cause analysis enables quality’s multiple characteristics to be monitored simultaneously. Based on the findings, the core issue that needs to be a matter of concern by the management team is the closure tap of the box. This process variation should be solved immediately to avoid the products’ quality from further deteriorating

The Performance of K-Means and K-Modes Clustering to Identify Cluster in Numerical Data

Cluster analysis is a formal study of methods and algorithms for natural grouping of objects according to the perceived intrinsic characteristics and the measure similarities in each group of the objects. The pattern of each cluster and the relationship for each cluster are identified, then they are related to the frequency of occurrence in the data set. Meanwhile, the mean and the mode are known as the measures of central tendency in a distribution. In clustering, the mean and the mode are applied as a technique to discover the existing of the cluster in the data set. Therefore, this study aims to compare the performance of K-means and K-modes clustering techniques in finding the group of cluster that exists in the numerical data. The difference between these methods is that the K-modes method is usually applied to categorical data, while K-means method is applied to numerical data. However, both methods would be used to cluster the numerical data in this study. Moreover, performance of these two clustering methods are demonstrated using the output from R software. The results obtained are compared such that the method giving the best output could be determined. In conclusion, the efficiency of the methods is highly presented

Malaysia tourism demand forecasting using box-jenkins approach

Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry

A new genetic algorithm based clustering for binary and imbalanced class data sets

This research was initially driven by the lack of clustering algorithms that specifically focus on binary data. To overcome this gap in knowledge, a promising technique for analysing this type of data became the main subject in this research, namely Genetic Algorithm (GA). This type of algorithm has an intrinsic search parallelism that avoids getting stuck at the local optima and poor initialization. For the purpose of this research, GA was combined with the Incremental K-means (IKM) algorithm to cluster the binary data streams. However, prior to this proposed method, a well-known GA based clustering method, GCUK was applied to gauge the performance of this algorithm to cluster the binary data, with new application for binary data set. Subsequently, this led to a proposed new method known as Genetic Algorithm-Incremental K-means (GAIKM) with the objective function based on a few suff- cient statistics that may be easily and quickly calculated on binary numbers. Different from the other clustering algorithms for binary data, this proposed method has an advantage in terms of fast convergence by implementing the IKM. Additionally, the utilization of GA provides a continuous process of searching for the best solutions, that can escape from being trapped at the local optima like the other clustering methods. The results show that GAIKM is an effcient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. The other main contribution in this research is the ability of the proposed GAIKM to cluster imbalanced data sets, where standard clustering algorithms cannot simply be applied to this data as they could cause misclassification results. In conclusion, the GAIKM outperformed other clustering algorithms, and paves the way for future research in missing data and outliers and also by implementing the GA multi-objective optimization

Forecasting of unemployment rate in Malaysia using exponential smoothing methods

One of the issues that triggers worlds lately is the increasing rate of the unemployment rate. Consequently, this research objective is to compare the most accurate forecast method and to find the most suitable period to predict the future of Malaysia’s unemployment rate in 2016. There are five sets of Malaysia’s unemployment rate and three forecasting methods being used which are Naïve, Simple Exponential Smoothing (SES) and Holt’s method. The forecasting model was then selected based on the smallest accuracy measures. The results indicated that Holt’s is the optimal model in forecasting the overall yearly unemployment rate, male yearly unemployment rate and overall quarterly unemployment rate. Furthermore, for female yearly unemployment rate and overall monthly unemployment rate, the best forecasting method was SES. Meanwhile, the overall unemployment rate of Malaysia in year 2016 was predicted to be 2.9% while 3.4% was estimated to be the value of unemployment rate for second half year of 2016 by using quarterly and monthly data. The forecast value was remained the same as previous year for overall yearly male data and female data which were 2.9% and 3.3% respectively. Lastly, the best period in forecasting Malaysia’s overall unemployment rate was found to be month with the value of 3.4%