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Journal : Scientific Journal of Informatics

Implementasi Decision Support System Dynamic Menggunakan Weight Product Untuk Menentukan Uang Kuliah Tunggal Dyah Ayu Wiranti; Kurnia Siwi Kinasih; Ainafatul Nur Muslikah; Dyah Wardani; Agung Teguh Wibowo Almais
Jurnal Ilmiah Informatika Vol. 5 No. 1 (2020): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v5i1.546

Abstract

Single tuition is the extension of the single tuition, which can be interpreted as a payment system made at the time of admission in both State and private colleges in Indonesia. Where this single tuition can provide benefits for the equitable of each student and help the students who are less able in terms of the economy that is certainly derived from the underprivileged family. In the calculation process determines the single tuition money each student needs a long process and time. So, there is an idea to implement a Decision Support System Dynamic (DSSD) so that at the time of determination of single tuition can be evenly and by the actual situation. One method that can be used on DSSD is the Weighted Product (WP) method. By implementing the method of WP combined with the concept of DSSD, then generated values of confusion matrix (recall, precision, f-measure, and accuracy) obtained by looking for the value of comparison between test data with pattern data. Obtained confusion matrix value with system testing and get the results Precision 88.89%, Recall 82.76%, Accuracy 77.14%, F-Measure 85.71%.
PENERAPAN DECISION SUPPORT SYSTEM DYNAMIC MENGGUNAKAN SIMPLE ADDICTIVE WEIGHTING DALAM PENENTUAN PEGAWAI TERBAIK Tanti Rismawati; Muhammad Aji Pangestu; Agung Teguh Wibowo Almais
Jurnal Ilmiah Informatika Vol. 5 No. 1 (2020): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v5i1.547

Abstract

Lots of applications or programs that are very useful to simplify human work. This includes applications that are made within a company. A company needs an intelligent system or an agent that controls the company's system. In a company has employees who work. This research will discuss the Dynamic Decision Support System in determining the best employees using one of the web-based Multi-Criteria Decision Making methods, which is Simple Additive Weighting (SAW). By using 2 types of data namely pattern data and test data. The data inputted were 15 data consisting of 10 test data and 5 pattern data. Then a confusion matrix can be obtained in the form of an accuracy value of 25%, a precision of 100%, a recall of 14%, and an F Measure of 24.5%.
Decision Support System dalam Menentukan Mahasiswa Bermasalah Menggunakan Metode Topsis Adinda Dhea Pramitha; Anis Fatul Fu'adah; Agung Teguh Wibowo Almais; Laela Nurul Qomariyah
Jurnal Ilmiah Informatika Vol. 5 No. 1 (2020): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v5i1.550

Abstract

At present many old semester students are starting to be undisciplined in attending lectures, this is due to the increasing burden of their assignments causing the enthusiasm of students to relax. This can create serious problems in the department because it can affect the accreditation level of the department. The purpose of this journal, which is to help the department admins to determine students who have problems in the field of lectures, so that the department can find out how many problem students can affect the accreditation of majors. In this journal, we implement the Decision Support System for manufacturing the system. With the TOPSIS method for calculations on the system, and using the Confusion Matrix for testing the system. From testing using confusion matrix, it can be concluded that precision produces 75%, recall produces 75%, accuracy produces 73%, and f-measure produces 75%. This shows that the system has a pretty good ability because it has exceeded the value of 70%.