Engineering Software Diversity

This study was conducted based on the facts about the lack of maximal outcomes of learning English in almost all levels of education, especially in vocational school. Although English has been studied for many years, but the majority of students can not master English well, especially speaking. Many factors cause this phenomenon, and all parties should concern to work together to formulatebetter system and process of English learning at vocational school.The research was conducted at SMK Negeri 1 Pacitan, by taking the students of class XII as subjects research. The sample of this research consists of 94 students of Business Travel and Tourism,Network Computer Engineering, and Engineering Software classes. This research uses descriptive qualitative method. It is used to describe the students’ speaking competence, particularlyin presenting final task report. This research also identifies the internal and external functions to formulate a strategy to improve the students’s speaking competence. The results showed that based on the analysis of English speaking competency of SMK Negeri 1 Pacitan students, it can be concluded as follows: a). Based on the students absorption analysis of presenting final project report, shows that English competency level of students is in the category of “pretty”, with evidence of the studentsvalue average in presenting final project report is more than the passing grade value (7.50),it is 79.5. While the number of student, who passed the presentation test,is 66 students.So it can be obtained the percentage level of mastery learning students on the presentation test of final report is only 70.21%, and it is categorized “less”, because the percentage of students who passed this exam is still under 75%. b). While the analysis of the questionnaire can be scribed that the average overall score of 3.44 speaking competence components are categorized “pretty”, and it can be concluded that the level of competence of English speaking students generally categorized “enough”. Based on the level ofstudents’ English speaking competence , it can be formulated the strategies to increase its competence. They are: a). Maximizing the use of English as a language instruction and reducing the use of Indonesian, especially in the learning process, to improve English language competency, so that students can have the ability to explain the sequence of events and the students can pass the exam well. b). Having more English presentation activitiesin other topics to encourage the students’ bravery and self-confidence in speaking English, so that students have good Englishcompetence as preparation to getjobs or to continue their study at university.c). Having more exercise of using body language to improve the mastery of speech, and smooth attitude, so that students have good English competence,and they are able to compete in the global world in obtaining employment. d). And maximizing the use of school’s facilities by using various methods of learning, and focusing on the students’ center method to improve the studentsEnglish competency so that they are able to compete for educational scholarships in this country and abroad.

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Neural Network Model to Calculate the Creep Data Using Size

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.60 ◽

2017 ◽

Vol 7 (8) ◽

pp. 257

Author(s):

Seetharam .K ◽

Sharana Basava Gowda ◽

. Varadaraj

Keyword(s):

Neural Network ◽

Risk Factors ◽

Software Engineering ◽

Network Model ◽

Neural Network Model ◽

Software Metrics ◽

Creep Data ◽

Engineering Software ◽

Function Points ◽

Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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