The research was conducted to be able to develop an employment information system at Indeks Media Teknologi Inc. which later if this system is successful can help the company tasks when making employee payroll for each period. Research is carried out at Indeks Media Teknologi Inc.. The method of collecting data by interview asking questions related to the employee salary management system. By means of observation, namely making observations directly to the Indeks Media Teknologi Inc.. In this study, the system development method used is waterfall with the supporting software used are PHP and MySQL. The network topology used is a star topology. The results of this study are in the form of client-server based employee salary data management which can improve service and facilitate the provision of salaries for employees of Indeks Media Teknologi Inc..

In this age the emergence of information and communication technologies (ICTs) has been identified as a major step toward solve the problems challenged the nation development. Problems such as corruption, delays in service delivery, lack of public sector accountability can be overcome with ICT. Furthermore, ICT are the key factors in improving government business and human sustainable development in all life aspects. Whilst the ICT considered the key to these problems but owning these technologies was facing many obstacles staring from bought them to continuous use, and create a gap between countries and within a country from the perspective of who does have computer and networks communication and who doesn't, and this refers to the digital divide. Some aspects of the digital divide exist everywhere and not only related to developing countries but also the size of the gap, which is different in countries and within a single community.
This study focuses on the digital divide problem by exploring the current state of the access digital divide in Kingdom of Saudi Arabia (KSA) based on three main research questions. And to achieve that, Data collected from International Telecommunication Union (ITU), Communications and Information Technology Commission (CITC), and World Bank were used.
The study found that Saudi Arabia is suffering from the access digital divide, and there is a strong link between household income and the access digital divide resulting from unaffordable prices in both ICT and broadband services and this gap tends to be larger in the regions where the inhabitants have the lowest income level.
The study recommends that the government should give improving household income the highest priorities and at the same time offering affordable prices for broadband services. Also, the study finds that mobile penetration represents a valuable resource for the Saudi Arabia government to be investing in delivering government services through mobile platforms. Finally, the study recommends that public-private partnerships with promoting and encouraging the private sectors to invest in ICT is one of the most important measurements in bridging the access digital divide.

Various techniques have been used over the years to implement recommendation systems. In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system. To build a recommender system, we require abundant amount of data which comprises of a spectrum of reviews and sentiments from all user domains. Websites like Yelp and TripAdvisor, allow users to post reviews for various businesses, products and services. In this work we have two objectives 1) Recommend restaurants to user based on user reviews in Yelp dataset and 2) Suggest improvements to business based on user reviews. In the first scenario, we will use the comments and ratings available in the Yelp dataset to generate restaurant recommendations and personalize them with user profile data. In the second scenario, we intend to suggest improvements to businesses based on various user reviews and provide them with a ranked list of predefined parameters to help them understand where they stand with respect to their competitors and where they should improve to do better. For both scenarios, we will perform two major steps to achieve our objective 1) Sentiment Analysis and 2) Content Based Recommendation. The first step gives us the - sentiment, quality, subject of discussion relevant to product and in the second step we use the outcomes of first step for personalizing and ranking our results. We came across Gensim and Latent Dirichlet Allocation which seemed to be interesting and was tailored to our requirements. In the yelp dataset, user comments are a mixture of various topics which are extracted by the algorithm (LDA) to provide accurate recommendation for all the users. A prototype of this method provided us with 93% accuracy.

Cloud computing is the newest web based computing network that offers the users with convenient and flexible resources to access or function with different cloud applications. Cloud computing is the availability of the computer network services, mainly storing data and computational power, without explicit user active control. The data in cloud computing is stored and accessed on a distant server by using cloud service provider’ applications. Providing protection is a main issue because information is transferred to the remote server through a medium. It is important to tackle the security issues of cloud computing before implementing it in an organization. In this paper, we call attention to the data related security issues and solution to be addressed in the cloud computing network. To protect our data from malicious users we can implement encryption. We have discussed the advantages of cloud computing in our paper.

A modern technology used for extracting knowledge from a huge amount of data using different models and tasks such as prediction and description is called data mining. The data mining approach has a great contribution on solving a different problem for data miners. This paper focuses on the application of data mining in health centers using different models. The model development process helps to identify or predict the behavior of blood donors whether they are eligible or ineligible to donate blood by their right status way and protects any blood bank health center from the collection of unsafe blood. Classification techniques are used for the analysis of Blood bank datasets in this study. For continuous blood donors, it will help to enable to donate voluntary individuals and organizations systematically. J48 decision tree, neural network as well as naïve Bays algorithms have been implemented in Weka to analyze the dataset of blood donors. The study is used to classify the blood donor's eligibility or ineligibility status based on their genders, deferral time, weight, age, body priced, tattoos, HIV AIDS, blood pressure, donation frequency, hepatitis, illegal drug use attributes. From the 11 attributes, gender does not affect the result. We have used 1502 datasets for the train set and 100 datasets for testing the model using cross-fold validation. Cross-fold data, partition was used in this study. The efficiency and effectiveness of the algorisms are measured automatically by the system. The obtained result showed that the J48 classifier outperforms the best result as well as both neural network and navies, Bayes, in terms of matrix evolution, with its 97.5% overall model accuracy has offered interesting rules.