Alex Rizky Saputra
Universitas Amikom Yogyakarta

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Implementasi Algoritma ARAS Pada SPK Untuk Menentukan Peringkat Dosen Terbaik Alex Rizky Saputra; Supriatin
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): Indonesian Journal of Computer Science Volume 11. No. 2 (2022)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3057

Abstract

In the world of higher education, lecturers are one of the main components in building quality and quantity. Good quality will give good results as well, to improve the quality of each lecturer, it is necessary to have an award given to lecturers from the campus so that it becomes a motivation for lecturers to improve the quality given to students and the community. Amik Mitra Gama is a private campus located in the Duri Riau area, in this case to improve the quality of education one of the steps taken is to give awards and appreciation to the best lecturers who will be selected every year. To realize this, we need an easy calculation system that is carried out in the form of ranking according to the final value, therefore a decision support system using the ARAS method is chosen because it is very appropriate for the selection process and provides convenience in the calculations which are determined based on ranking. The decision support system using the ARAS method uses 8 criteria that are set as a reference in determining the best lecturers, namely Recent Education, Lecturer Functional Position, Lecturer Certification, Number of Journal Publications, Roles in Research, Journal Publication History, Research Grants, and Community Service. There are 10 lecturers in the field of computers who will be used as alternative data with lecturer codes D01, D02, D03, D04, D05, D06, D07, D08, D09, D10. The results obtained from this study are the lecturer code D04 = 0.0974, D06 = 0.0965, D09 = 0.0932, D07 = 0.0903, D03 = 0.0901 was selected as the best lecturer in 2021/2022. So that the results of this study can help the campus to determine the best lecturers every year fairly and be selected based on rankings.
Implementasi Sistem Pendukung Keputusan Menggunakan Algoritma MOORA untuk Pemilihan Jenis Bibit Cabai Unggul Abdussalam Al Akbar; Alimuddin Yasin; Alex Rizky Saputra; Sepriano; Ratu Mutiara Siregar; Budy Satria; Elfitra
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3464

Abstract

Cultivating chili plants is a business opportunity that has quite a large income. However, many farmers still use traditional concepts in determining which seeds to plant, such as trying out chili seeds without carrying out in-depth analysis or observation. A decision support system (DSS) is a system that is capable of providing decision recommendations using several criteria determined through method processes in the decision making system, namely ARAS, SAW, MOORA, AHP and others. The MOORA method is useful for separating the subjective part of an evaluation process into a decision weight criterion with several decision making attributes. And also the level of selectivity of this method is very good because it can determine objectives from conflicting criteria. Where the criteria can be profitable (benefit) or unprofitable (cost). Based on the results obtained after using the MOORA calculation method, there are 4 types of superior seed varieties that can be recommended for farmers, namely Taro Chili Seeds = 0.2875; Indrapura Chili Seeds = 0.2595 ; Lado Chili Seeds = 0.2490 ; Chili Seeds TM = 0.2154. By creating this decision support system, it is hoped that farmers will be able to use it as a reference in selecting superior chili seeds and be able to get maximum harvest results and increase commodity income for chili farmers.