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Penerapan Metode Maut Dalam Pemilihan Wartawan Terbaik Dengan Pembobotan Entropy (Kasus Koran Radar Group) Cindy Nanda Sari; M Mesran; A. M Hatuaon Sihite
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.v8i2.615

Abstract

The online press media Radar is an online media entity of Radarindo that is signed by the editor-in-chief. The high number of journalists in the Koran Radar Group makes it challenging for the editor-in-chief to select the best journalists, as there lacks a suitable system for this purpose, including an effective system within the Koran Radar Group. Without a clear system, ensuring an objective and fair selection of the best journalists becomes difficult. The selection of the best journalists is crucial to guarantee the quality and accuracy of information delivered to the public. Hence, a system has been created to aid in the assessment process for choosing the best journalists for the Koran Radar Group. This system utilizes the MAUT method and Entropy weighting to evaluate the values of each criterion and rank alternative candidates. It is expected that implementing this decision support system will provide a positive contribution to the process of selecting the best journalists for the company.After conducting the ranking process, the results obtained from the application of the MAUT method and Entropy weighting show that alternative (A2) with the name Sehat Sembiring scored 0.00983, making it the best alternative in the selection of the best journalists for the Koran Radar Group.
Penerapan Sistem Pendukung Keputusan Dalam Penilaian Kinerja Supervisor Dengan Menggunakan Metode Maut Dan Pembobotan Entropy Wanda Tofani Devi; M Mesran; Annisa Fadillah Siregar
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.v8i2.616

Abstract

Position positions in PT. Bintang Mutiara Cemerlang is a supervisor, where the problem in this company is the supervisor's performance appraisal process which is less effective because it is seen based on the length of work alone, causing injustice to other supervisors who have not worked long enough. Therefore, a decision support system is created that is capable of supporting the performance appraisal procedure. This decision support system applies the MAUT method and the Entropy method to the process to see the value obtained in the system. Each criterion has its own weight so that the alternatives can be ranked. The MAUT (Multi Attribute Utility Theory) method is a quantitative method in which there are structured stages to find out and describe the variables in it. After ranking, the results obtained from the application of the MAUT and Entropy methods are the A10 alternative on behalf of Shadad Putra S.H with a value of 0.84060 as the best alternative in assessing supervisor performance at PT. Shining Pearl Star.
Penerapan Data Mining Dalam Pengelompokan Data Penjualan Paket Internet di Telkomsel Authorized Partner (TAP) Deli Tua Dengan Algoritma K-Means Tesa Aurelia Siregar; M Mesran; Dito Putro Utomo
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.