The influence of workload, recognition, emotional management and self-esteem on job performances among primary school teacher in Alor Setar, Kedah

Ministry of Education has made some changes to the syllabus in school, the new system and syllabus that has been implemented this year more or less changed and affected the way teachers conducting their job. Hence, it increases teacher’s workload because they have to adapt and learn the new system and syllabus. Teachers also claimed that they have a high workload due to because of the new policy that being implemented by the government. The purpose of this research is to examine the relationship between workload, recognition, emotional management and self-esteem towards job performance among primary school teachers in Alor Setar, Kedah. There are four variables that will be focused on which are, workload, recognition, emotional management and self-esteem. The questionnaire has 39 items that have used five-point Likert scale. Researcher used Statistics Package for Social Science (SPSS) version 22 for analyses the data from the questionnaires. The obtained data were analyzed using descriptive analysis and correlation coefficient analysis. The results showed that emotional management have a positive and significant relationship towards job performance. Meanwhile, workload was found have significant relationship towards job performance. In order to achieve high job performance, emotional management and self-esteem skills need to be developed and improved by teachers through consistent and systematic approach. Lastly, limitations of the study and suggestions for future research were also highlighted in the study

Time Efficiency on Computational Performance of PCA, FA and TSVD on Ransomware Detection

Ransomware is able to attack and take over access of the targeted user'scomputer. Then the hackers demand a ransom to restore the user's accessrights. Ransomware detection process especially in big data has problems interm of computational processing time or detection speed. Thus, it requires adimensionality reduction method for computational process efficiency. Thisresearch work investigates the efficiency of three dimensionality reductionmethods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) andTruncated Singular Value Decomposition (TSVD). Experimental results onCICAndMal2017 dataset show that PCA is the fastest and most significantmethod in the computational process with average detection time of 34.33s.Furthermore, result of accuracy, precision and recall also show that the PCAis superior compared to FA and TSVD

Network anomaly detection research: a survey

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

Time efficiency on computational performance of PCA, FA and TSVD on ransomware detection

Ransomware is able to attack and take over access of the targeted user's computer. Then the hackers demand a ransom to restore the user's access rights. Ransomware detection process especially in big data has problems in term of computational processing time or detection speed. Thus, it requires a dimensionality reduction method for computational process efficiency. This research work investigates the efficiency of three dimensionality reduction methods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) and Truncated Singular Value Decomposition (TSVD). Experimental results on CICAndMal2017 dataset show that PCA is the fastest and most significant method in the computational process with average detection time of 34.33s. Furthermore, result of accuracy, precision and recall also show that the PCA is superior compared to FA and TSVD

Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%

Techniques and motifs in Kelingkan shawl : the Malay traditional hand-made embroidery

It is initially a royal handicraft which become popular in 19th century in Peninsular Malaysia

User Behavior in Using Mobile Commerce (Scale Development: Perspective of Trust and Risk)

The aim of ours research is to explore the relation between the elements of trust and risk and their impact on consumer behavior in the intention of use of mobile commerce services based on sales. The final goal of this research is to understand consumer behavior in the use of mobile commerce application services in Indonesia by considering the elements of trust and risk in consumer behavior. Finding from previous research have revealed that trust and risk is one of the critical aspects in the use of e-commerce services. This article focus is to discuss the development stages of research instruments development to be used in next survey agenda. By applying Technology Acceptance Model (TAM) as a theoretical basis of a conceptual model and the research instrument was developed. Models and instruments then validated through a pilot study involving 75 students as respondents. Survey data were analyzed via Smart-PLS software version 2 to ensure reliability level and validity of the instrument. This study resulted in a validated instrument that will be used to collect data from actual survey

Tingkat Kesuksesan E-Learning Edmodo Sebagai Sistem Pembelajaran Online Selama Pandemi Covid 19 Adopsi Model DeLone&Mclean

Edmodo merupakan platform pembelajaran online yang saat ini termasuk banyak digunakan di perguruan tinggi di Indonesia selama pandemic Covid19 salah satunya di Univeristas Dinamika Bangsa (UNAMA) Jambi. Dalam penelitian ini dilakukan pengevaluasian terhadapa kualitas kesuksesan LMS Edmodo pada platform-platform edmodo tersebut dengan mengadopsi model Delone And Mclean dengan 6 variabel yaitu Information Quality, System Quality, Service Quality, Use, User Statisfaction dan Net Benefit. Untuk data analisis menggunak Structural Equation Mode (SEM). Responden di penelitian ini adalah para dosen dan mahasiswa di UNAMA Jambi yang sebagai pengguna edmodo. Adapun tujuan penelitian ini untuk membuktikan sejauh mana kesuksesan yang penerapan Edmodo di universitas dinamika bangsa jambi. Responden pada penelitian ini sebanyak 166 responden. Data dikumpulkan dengan cara metode survey. Hasil dari penelitian ini menunjukkan nilai R2 dengan variabel information quality dan system quality memiliki nilai 0.339 dikategorikan tingkat moderat/sedang. Artinya kedua variabel dependen memberikan pengaruh dan tinkat moderat/sedang terhadap variabel dependen. Untuk R2 variabel independent use dan user satisfaction memiliki substansial/kuat dengan nilai 0.707, artinya kedua variabel independen memberikan pengaruh dan tingkat Substansial/kuat terhadap variabel depende

The Malaysian Islamic authorities’ approach to sufism : an analysis of their institutional fatwas = Malezya İslam kurumlarının tasavvufi konulara yaklaşımı : kurumsal fetvaların analizi

Bu çalışma Malezya’daki İslamî otoritelerin Tasavvufi konulara yaklaşımını, ulusal fetva komitesi ve eyalet düzeyindeki fetva komiteleri tarafından yayınlanan fetvalar aracılığıyla incelemeyi amaçlamaktadır. Bu çalışmada aynı zamanda fetva uygulamalarının bir standarda sahip olamamasının ve fetvaların birbiriyle çelişmesinin nedenleri de ele alınmaktadır. Sufilerin akaid ve uygulamalarına ilişkin fetvalar; vahdet-i vücud, nûr-ı Muhammedî, semâ, rabıta, halvet, yakaza, teberrük, ve istiğase gibi konular ışığında bu çalışma dahilinde ayrıntılı olarak tartışılmaktadır. Malezya’da yayınlanan fetvalardan etkilenen bazı tarikatlar hakkında da detaylı bilgi verilmiştir. Bu çalışmada, seçilmiş fetvalar Malay alimlerin görüşleri ve fetva otoritelerinin kararları ışığında incelenerek ve açıklanarak analitik yöntem kullanılmıştır.--------------------This research aims to study the approach of the Malaysian Islamic Authorities on Sufism through the national and state-level fatwas (Islamic legal opinions). The study also will uncover the reasons behind the contradictions and standardisation of the fatwas. The fatwas on Sufi beliefs and practices discussed in the study include Wahdat al-Wujud, Nur Muhammad, Sufi Sama’, Rabita, Khalwa, Yaqazah, Tabarruk, and Istighasa. The details of several Sufi orders affected by the fatwas are also included. The methodology used throughout the paper is based on an analytical approach, involving the explanations and details of selected fatwas, in relations to the Malay scholars’ opinions and the Fatwa authorities’ decision