Setiawansyah Setiawansyah, Sanriomi Sintaro, Very Hendra Saputra, A. A. Aldino
{"title":"结合灰色关联分析(GRA)和简化支点成对相对标准重要性评估(PIPRECIA-S)确定最佳员工","authors":"Setiawansyah Setiawansyah, Sanriomi Sintaro, Very Hendra Saputra, A. A. Aldino","doi":"10.61944/bids.v2i2.67","DOIUrl":null,"url":null,"abstract":"Problems in selecting the best staff often involve complex challenges such as difficulty finding candidates with good performance. The problems faced in the selection of the best are only based on the assessment of discipline and productivity of performance carried out by the staff, so the assessment process does not use aspects of criteria that are considered important in selecting the best staff. This study aims to determine the best staff based on predetermined criteria and in determining the selection of the best staff using the Gray Relational Analysis (GRA) decision model while in determining the weight of criteria using the Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) model so that the weight of the resulting criteria is not based on assumptions from decision makers. The results of the best staff assessment ranking using the Gray Relational Analysis method and the Simplified Pivot Pairwise Relative Criteria Importance Assessment weighting method obtained the results, namely for Rank 1 obtained by Denis Irawan with a final Gray Relational Analysis value of 0.243014. The results of data processing in the TRITAM Model test for the best staff selection application were adjusted to the conclusion of the overall results of the TRITAM Model criteria for technology acceptance, the results were good at 82.56%.","PeriodicalId":510953,"journal":{"name":"Bulletin of Informatics and Data Science","volume":"16 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combination of Grey Relational Analysis (GRA) and Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) in Determining the Best Staff\",\"authors\":\"Setiawansyah Setiawansyah, Sanriomi Sintaro, Very Hendra Saputra, A. A. Aldino\",\"doi\":\"10.61944/bids.v2i2.67\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Problems in selecting the best staff often involve complex challenges such as difficulty finding candidates with good performance. The problems faced in the selection of the best are only based on the assessment of discipline and productivity of performance carried out by the staff, so the assessment process does not use aspects of criteria that are considered important in selecting the best staff. This study aims to determine the best staff based on predetermined criteria and in determining the selection of the best staff using the Gray Relational Analysis (GRA) decision model while in determining the weight of criteria using the Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) model so that the weight of the resulting criteria is not based on assumptions from decision makers. The results of the best staff assessment ranking using the Gray Relational Analysis method and the Simplified Pivot Pairwise Relative Criteria Importance Assessment weighting method obtained the results, namely for Rank 1 obtained by Denis Irawan with a final Gray Relational Analysis value of 0.243014. The results of data processing in the TRITAM Model test for the best staff selection application were adjusted to the conclusion of the overall results of the TRITAM Model criteria for technology acceptance, the results were good at 82.56%.\",\"PeriodicalId\":510953,\"journal\":{\"name\":\"Bulletin of Informatics and Data Science\",\"volume\":\"16 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bulletin of Informatics and Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.61944/bids.v2i2.67\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Informatics and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61944/bids.v2i2.67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Combination of Grey Relational Analysis (GRA) and Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) in Determining the Best Staff
Problems in selecting the best staff often involve complex challenges such as difficulty finding candidates with good performance. The problems faced in the selection of the best are only based on the assessment of discipline and productivity of performance carried out by the staff, so the assessment process does not use aspects of criteria that are considered important in selecting the best staff. This study aims to determine the best staff based on predetermined criteria and in determining the selection of the best staff using the Gray Relational Analysis (GRA) decision model while in determining the weight of criteria using the Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) model so that the weight of the resulting criteria is not based on assumptions from decision makers. The results of the best staff assessment ranking using the Gray Relational Analysis method and the Simplified Pivot Pairwise Relative Criteria Importance Assessment weighting method obtained the results, namely for Rank 1 obtained by Denis Irawan with a final Gray Relational Analysis value of 0.243014. The results of data processing in the TRITAM Model test for the best staff selection application were adjusted to the conclusion of the overall results of the TRITAM Model criteria for technology acceptance, the results were good at 82.56%.