Claim Missing Document
Check
Articles

Found 3 Documents
Search

Implementasi Metode EDAS Dalam Penilaian Kinerja Dosen Pada Masa Pandemi Covid-19 dengan Pembobotan Entropy Indini, Dwina Pri; Siregar, Tesa Aurelia; Utomo, Dito Putro
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 2 (2023): Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i2.1613

Abstract

Along with the development of technology that makes online learning run well, but the enthusiasm for student learning is not like when face to face and there are still many students who do not take various courses so that students' knowledge abilities are greatly decreased. When online learning takes place, it is very necessary for a lecturer's performance to arouse students' enthusiasm in participating in online learning. Because during this pandemic the role of lecturers is very necessary to develop an innovation and be able to make students not bored in participating in online learning. And also in terms of the performance appraisal process, several problems often occur, namely the speed in the performance appraisal process and also the accuracy of the performance appraisal. In assessing the performance of lecturers during online learning, there are several criteria including Discipline, Delivery of Materials, Interaction, Discussion Questions and Answers and Punctuality. Based on these problems, a decision support system is needed as a problem solving technique and is assisted by a method that can produce an accurate final value. The method is the Evaluation Based On Distance From Average Solution (EDAS) and Entropy method which is very helpful in generating weight values ??from alternative data and criteria so as to get the final results obtained in Alternative L9 with a value of 0.31025 on behalf of Surya Darma Nst, M. Kom.
Penerapan Data Mining Dalam Pengelompokan Data Penjualan Paket Internet di Telkomsel Authorized Partner (TAP) Deli Tua Dengan Algoritma K-Means Siregar, Tesa Aurelia; Mesran, M; Utomo, Dito Putro
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.618

Abstract

With the increasing sales of internet packages at TAP Deli Tua, it is crucial to be more meticulous in processing the sales data to avoid stock shortages that could result in losses for TAP Deli Tua. Determining the best-selling products among the internet package sales is essential, as incorrect grouping may lead to losses for TAP Deli Tua. This could further decrease the sales level at TAP Deli Tua, causing significant financial losses for the company. Therefore, TAP Deli Tua must be more attentive in data processing to prevent any detrimental outcomes.To achieve accurate decision-making, TAP Deli Tua needs to collect sales data from internet packages for analysis. One of the algorithms used for this purpose is the K-Means algorithm, which falls under Non-Hierarchical Clustering. It partitions the dataset into several clusters, optimizing the grouping criteria. The most commonly used criterion is the one that minimizes the clustering error for each point by calculating its squared distance from the corresponding cluster center. Additionally, the sum of distances for all points in a dataset is computed.Based on research findings, data mining with the implementation of the K-Means algorithm can assist Telkomsel Authorized Partner (TAP) in making more accurate and significant decisions. By applying the K-Means algorithm, the analysis revealed that out of 15 sales data points for internet packages, 8 best-selling products were in Cluster 0, while 7 non-best-selling products were in Cluster 1. With the increasing sales of internet packages at TAP Deli Tua, it is crucial to be more meticulous in processing the sales data to avoid stock shortages that could result in losses for TAP Deli Tua. Determining the best-selling products among the internet package sales is essential, as incorrect grouping may lead to losses for TAP Deli Tua. This could further decrease the sales level at TAP Deli Tua, causing significant financial losses for the company. Therefore, TAP Deli Tua must be more attentive in data processing to prevent any detrimental outcomes.To achieve accurate decision-making, TAP Deli Tua needs to collect sales data from internet packages for analysis. One of the algorithms used for this purpose is the K-Means algorithm, which falls under Non-Hierarchical Clustering. It partitions the dataset into several clusters, optimizing the grouping criteria. The most commonly used criterion is the one that minimizes the clustering error for each point by calculating its squared distance from the corresponding cluster center. Additionally, the sum of distances for all points in a dataset is computed.Based on research findings, data mining with the implementation of the K-Means algorithm can assist Telkomsel Authorized Partner (TAP) in making more accurate and significant decisions. By applying the K-Means algorithm, the analysis revealed that out of 15 sales data points for internet packages, 8 best-selling products were in Cluster 0, while 7 non-best-selling products were in Cluster 1.
Analisa Penerapan Metode OCRA Pada Pertukaran Mahasiswa Dalam Mendukung Kampus Merdeka Dengan Pembobotan ROC Siregar, Tesa Aurelia
Jurnal Kajian Ilmiah Teknologi Informasi dan Komputer Vol 1 No 1 (2022): November 2022
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jutik.v1i1.35

Abstract

The independent campus is one of the programs from the policy of the ministry of education and culture that has been approved by the president to motivate students to improve their abilities by participating in learning activities carried out for one or up to three semesters according to interests outside the study program or college, where students follow the lesson. However, in the selection of student exchanges in support of independent campuses, there are often problems, namely the student section is confused in determining it because of the large amount of data that does not meet the criteria but has already registered. So in this study, a decision support system is needed as a system for determining eligible students in an independent campus exchange. As for the selection of student exchanges in supporting an independent campus, there are five criteria including GPA, non-academic achievements, skills, ethics and academic sanctions. So with this, a decision support system is urgently needed to solve problems using the ROC (Rank Order Centroid) and OCRA (Operational Competitiveness Rating Analysis) methods which will produce weight values ​​for each criterion and preference values ​​from the first ranking alternative. So that the chosen one in the selection of the independent campus student exchange is Alternative A1, namely Tasya Salsabilla with a value of 1,071.