Edy Budiman

Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation

Student academic evaluation is part of academic information system (AIS) performance, in order to control student learning progress is necessary. Furthermore, the evaluation showing whether the student will pass or fail would benefit the student/instructor and act as a guide for future recommendations/evaluations on performance. An in depth study on the student academic evaluation technique by using Decision Tree C4.5 has been conducted. Specific parameters including age, place of birth, gender, high school status (public or private), department in high school, organization activeness, age at the start of high school level, and progress GPA (pGPA) and Total GPA (tGPA) from semester 1–4 with three times graduation criteria (i.e., fast, on, and delay) times have been defined and tested. The scope of the paper has been set for undergraduate programs. The experimental results show that accuracy algorithm (AC) of 78.57% with true positive rate (TP) of 76.72% by using quality training data of 90% have best performance accuracy value.

Mobile Learning: Visualization Tools of Data Structures Course to Support Learning Students

Mobile learning course of data structure, it cannot only be used as auxiliaries of learning, but also an independent learning mode. The various approaches theory of research and development, software design, and methods we use into built of media. The main objective is the construction of educational software adapted to learning. First and foremost, it is essential to have in mind that our application is designed for two very specific public; on the one side the lectures, who will use it as a teaching tool, and another side the students, for learning purposes. This research has created a mobile learning application for courses of data structures, the learning material is presented visually according curriculum-syllabus and learning outcomes of data structures. This application as one a learning media to support students who can be used all the time (long life learning). Keywords—Mobile Learning, Data-Structures, Course, Students, Visualization.

Mobile Packet Internet Groper (Moping) Application and Database Connection for Testing the QoS of Internet Services

Baseline characteristics of students’ knowledge in the concept of learning technology is required. One is the students learning style characteristics including attitudes, cognitive and psychomotor or skills. Then, characteristics of learning styles based approach sensory preferences, including visually, auditory, and kinesthetic or learning by doing/movement and touch. In this study, methods of teaching as a learning process consisting of multimedia-based teaching, lectures, demonstrations, problem solving, discussion, exercises (drill), case studies, frequently asked questions, and experiment. Three types of questionnaire consisting of the distribution characteristics of student learning styles, the tendency of students learning methods, and the tendency of lecturers teaching methods have been implemented. In order to determine the grade level of each questionnaire with grade visual (V), auditory (A), kinesthetic (K), auditory-kinesthetic (AK), and visual-auditory (VA) have been exploited. The results showed that the relationship between these three variables has yielded a value of 68.2%. It means that the FIS method could be used in order to assess the performance relationship.applicant selection system by using Tahani fuzzy logical method and Simple Additive Weighting (SAW). This research is designed due to the job applicant selection system that hasn’t been optimized so that the management of an institution as well as a company faces some difficulties and barners in selecting the perfect human resources to be hired. The method applied in this research are Tahani fuzzy logical method and Simple Additive Weighting (SAW) producing web application-based software system that can handle the job applicant selection process by analyzing the data of job application as well as job applicant’s data. This kind of system produced some outputs consisting of job-applicant candidate rank, ideal job applicant rank, and also final rank.