Journal of Telecommunication, Electronic and Computer Engineering 

About: Journal of Telecommunication, Electronic and Computer Engineering is an academic journal. The journal publishes majorly in the area(s): Antenna (radio) & Wireless sensor network. It has an ISSN identifier of 2180-1843. Over the lifetime, 1835 publications have been published receiving 4868 citations.

The Application of Google Classroom as a Tool for Teaching and Learning

Abstract: Learning activities in the computer lab is one of the challenging in higher education. Subject that is most practical activities such as Data Mining are by nature illustrative or demonstrative in the computer lab that emphasize the acquisition of observational skills; and allow students to see the concept dealt in action and relate theory more closely to reality. However, the students’ reaction to practical work is often negative as a result they are not effective in laboratory work and this may reflect a student perception that there is lack of clear purpose for the lab hands on task. The main objective of this study is to explore the effectiveness of Google Classroom’s active learning activities for data mining subject under the Decision Sciences program. A set of questionnaire has been distributed to a sample of 100 students who enrolled data mining subject were used in this study. The analysis of the data was carried out using Technology Acceptance Model (TAM) to examine the relationship between the identified factors and the effectiveness of the learning activities. The results prove that majority of the students satisfy with the Google Classroom’s tool that were introduced in the class where all ratios are above averages. In particular, comparative performance is good in the areas of ease of access, perceived usefulness, communication and interaction, instruction delivery and students’ satisfaction towards the Google Classroom’s learning activities.

A Framework for Pre-computated Multi-Constrained Quickest QoS Path Algorithm

Abstract: The era of advance computer networks and advance communication leads the technology to new heights and the algorithms used for the routing needs to be updated side by side parallel with the advancement. This paper presents the new multi-constrained routing algorithm which gives quality of service full-fill the service level agreements with the user. Finding a multi-constrained path is a NP-complete problem, but still the presented algorithm gives the feasible path for the routing in a given polynomial time. Furthermore the performance comparison shows the best results with presented approach and existed approach.

Credit Card Fraud Detection Using Machine Learning As Data Mining Technique

Abstract: The rapid participation in online based transactional activities raises the fraudulent cases all over the world and causes tremendous losses to the individuals and financial industry. Although there are many criminal activities occurring in financial industry, credit card fraudulent activities are among the most prevalent and worried about by online customers. Thus, countering the fraud activities through data mining and machine learning is one of the prominent approaches introduced by scholars intending to prevent the losses caused by these illegal acts. Primarily, data mining techniques were employed to study the patterns and characteristics of suspicious and non-suspicious transactions based on normalized and anomalies data. On the other hand, machine learning (ML) techniques were employed to predict the suspicious and non-suspicious transactions automatically by using classifiers. Therefore, the combination of machine learning and data mining techniques were able to identify the genuine and non-genuine transactions by learning the patterns of the data. This paper discusses the supervised based classification using Bayesian network classifiers namely K2, Tree Augmented Naive Bayes (TAN), and Naive Bayes, logistics and J48 classifiers. After preprocessing the dataset using normalization and Principal Component Analysis, all the classifiers achieved more than 95.0% accuracy compared to results attained before preprocessing the dataset.

Smart City Indicators: A Systematic Literature Review

Abstract: Smart city is currently a trend for major cities in the world and also most cities in Indonesia. The city as center of human civilization cannot be separated from problems related to excess capacities and matters of convenience. The more and more people are moving from the rural to urban areas has increasingly pose new problems in the city. The city needs to change in order to sustain in the future. There are needs of a strong indicators as the support for a city, in terms of the physical environment, social, people, infrastructure, education and ICT infrastructure. In this paper we discuss on a systematic literature review of studies related to smart city. Systematic literature review has three stages, introduction stage, demographic analysis stage and analysis of the results. The final results reveal important indicators in smart city based on the conclusions of previous studies.

Heart disease prediction system using k-Nearest neighbor algorithm with simplified patient's health parameters

Abstract: Heart disease is the primary cause of death nowadays. Treatments of heart disease patients have been advanced, for example with machine-to-machine (M2M) technology to enable remote patient monitoring. To use M2M to take care remote heart disease patient, his/her medical condition should be measured periodically at home. Thus, it is difficult to perform complex tests which need physicians to help. Meanwhile, heart disease can be predicted by analysing some of patient's health parameters. With help of data mining techniques, heart disease prediction can be improved. There are some algorithms that have been used for this purpose like Naive Bayes, Decision Tree, and k-Nearest Neighbor (KNN). This study aims to use data mining techniques in heart disease prediction, with simplifying parameters to be used, so they can be used in M2M remote patient monitoring purpose. KNN is used with parameter weighting method to improve accuracy. Only 8 parameters are used (out of 13 parameters recommended), since they are simple and instant parameters that can be measured at home. The result shows that the accuracy of these 8 parameters using KNN algorithm are good enough, comparing to 13 parameters with KNN, or even other algorithms like Naive Bayes and Decision Tree.