No abstract

Student attendance in the classroom is one indicator for lecturers in learning activities. At Musamus University, student attendance has been managed digitally. The system is known as SIMAKAD. The weakness of SIMAKAD in doing attendance is in the process of entering data. Student attendance data can be inputted by the lecturer after completing the manual signature process by students. This can cause errors in entering data into the system. Whereas in the industrial revolution of the 4.0 era, the transformation of digital data in education was needed. For example, with the presence of Radio Frequency Identification (RFID) technology. The student attendance prototype using RFID was designed as a solution to solve problems in entering student attendance data at SIMAKAD. RFID technology can identify each student while attending college through reading the student TAG ID. By adding Internet of Things (IoT) capabilities, the results of reading student IDs will be sent directly through the internet and stored in a database server. The database can be directly used by lecturers as monitoring attendance of lectures. So no need to use manual absence.

Talent and interest are very influential on students in the process of their development and learning. This is because every child needs an education program that matches their talents and interests. Many problems occur due to mistakes of parents or teachers in determining talent and asking students so mistakes in determining education still tend to be incorrect. So students find it difficult to adapt to the education they face because it is not in accordance with their abilities and potential. Therefore, we need a modeling of decision making based on the criteria of talent and interests of children using the Simple Multi Attribute Ranking Technique modeling. The implementation of the SMART method in this study is able to provide solutions to the problem of decision making talents and interests of children. This study uses a sample of 15 student data which is then entered into the computation of the SMART calculation method. The result is that 3 students who were sampled were able to have grades above the standard or scores above 70 points that have been set based on predetermined rules. While the other 12 students have the final result below the standard that has been set which is below the value of 70 points.

For prospective new students often feel confused in choosing majors to continue their education at the University. The faculty of engineering is one of the favorite faculties for prospective students but sometimes most feel confused choosing what majors are in accordance with their academic abilities, so that the selection of majors often follows the choice of their closest friends or their parents’ choices. The selection of inappropriate majors will affect the future of the prospective new student. For this reason, prospective new students must know their academic abilities and interests. With a decision support system for determining majors, it is hoped that it can help prospective new students to find out the greatest potential of the choice of majors in accordance with their academic abilities and talents. Decision Support Systems made implementing the Naïve Bayes method to find out which prospective new students can potentially enter one of the departments in the technical faculty other than that the use of the Analytic Hierarchy Process model is used to find out the right choice of majors. The Naïve Bayes method refers to the rules for admitting new prospective students, which will obtain the probability formula yes and the probability formula no, to be used in calculations with data samples. The AHP model refers to the value of subjects that are tested or tested on the admission test for prospective new students. Calculations using Naïve Bayes for student data samples for the 2019-2020 school year, using 10 sample data on prospective students, obtained 3 students who did not enter the department of engineering or have no academic potential to enter the department of engineering. While other students who entered were calculated using the AHP Model, and obtained 1 student whose majors were not appropriate. While other students have chosen the right majors according to their academic abilities. In the end this Decision Support System can be used to find out which potential new students are potential and who have no potential and can also provide recommendations for the selection of the right majors in accordance with the academic abilities of prospective new students.

Flood disasters are usually caused by the rising of sea water and high rainfall resulting in high water discharge. One way to reduce the impact of losses caused by standing water is to measure the height of the water discharge. An ultrasonic sensor that is based on a microcontroller can be used to measure the height of the water discharge. This water level monitoring system is carried out by implementing a microcontroller-based ultrasonic sensor. The use of this system is expected by the writer to be a water level detection simulation where the system works by giving an alarm when the height of the water discharge crosses the limit set on the system. When the height of the water discharge exceeds the limit, the system will provide information in the form of an alarm sound and the system display that is initially green turns red.