M. Mesran, Ade Ayunda Kusuma, Ridha Maya Faza Lubis
The new branch location is close to people's activities with the availability of adequate facilities, making it convenient for consumers to access the services/products they need. The determination of the feasibility of a new branch location by several product or service producers still uses an inaccurate system, which can lead to problems in determining a strategic and targeted new branch location. However, there are some challenges in selecting a new branch location, so the utilization of technology is considered efficient, easy, and flexible, widely used by entrepreneurs, especially in determining new branch locations. This is done by using the assistance of a decision support system, which is expected to help determine an efficient and strategic new branch location. The aid comes in the form of a Decision Support System using the MAUT method with ROC weighting. After calculating each criterion and alternative, the best ranking is obtained for alternative A6 with a value of 0.6847. This way, business groups will not have difficulty in determining a new branch location through alternatives and criteria. The use of the MAUT method with ROC weighting is expected to assist in obtaining the best and valid alternatives up to the ranking stage
{"title":"Decision Support System for Determining New Branch Location Applying the MAUT Method with ROC Weighting","authors":"M. Mesran, Ade Ayunda Kusuma, Ridha Maya Faza Lubis","doi":"10.61944/bids.v2i2.76","DOIUrl":"https://doi.org/10.61944/bids.v2i2.76","url":null,"abstract":"The new branch location is close to people's activities with the availability of adequate facilities, making it convenient for consumers to access the services/products they need. The determination of the feasibility of a new branch location by several product or service producers still uses an inaccurate system, which can lead to problems in determining a strategic and targeted new branch location. However, there are some challenges in selecting a new branch location, so the utilization of technology is considered efficient, easy, and flexible, widely used by entrepreneurs, especially in determining new branch locations. This is done by using the assistance of a decision support system, which is expected to help determine an efficient and strategic new branch location. The aid comes in the form of a Decision Support System using the MAUT method with ROC weighting. After calculating each criterion and alternative, the best ranking is obtained for alternative A6 with a value of 0.6847. This way, business groups will not have difficulty in determining a new branch location through alternatives and criteria. The use of the MAUT method with ROC weighting is expected to assist in obtaining the best and valid alternatives up to the ranking stage","PeriodicalId":510953,"journal":{"name":"Bulletin of Informatics and Data Science","volume":" 952","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erlin Windia Ambarsari, Vierhan Rahman, Wahyu Nur Cholifah
This study applies the Improved Rank Order Centroid (IROC) to the Indonesian patent submission process within a Multi-Criteria Decision Making (MCDM) framework. The study evaluates four primary elements in patent assessment: "Patent Description," "Illustration," "Inventor's Ownership Statement," and "Rights Assignment Declaration." Preliminary findings indicate the importance of "Patent Description," followed by the other elements in descending order of significance. The evaluation also encompasses three applicant alternatives, with the Second Applicant emerging as the most favorable. The study further contrasts IROC outcomes with MAGIQ and AHP methodologies. While rank-based techniques like ROC and IROC generally produce similar weight distributions, the AHP method, which employs pairwise comparisons, often displays variations. The research underscores the potential of IROC in determining criterion weights, its comparison within the MAGIQ framework, and its validation through AHP. These insights aim to deepen our understanding of decision-making processes and analysis. The conclusion from comparing IROC results with MAGIQ and AHP indicates that the applicant rankings remain consistent. Therefore, further research is needed to understand the differences between evaluation methods and their impacts and explore the influence of cultural or regional factors in the patent submission process
{"title":"Applying IROC Method in Patent Submission Evaluation in Indonesia: A Comparison with MAGIQ and AHP","authors":"Erlin Windia Ambarsari, Vierhan Rahman, Wahyu Nur Cholifah","doi":"10.61944/bids.v2i2.75","DOIUrl":"https://doi.org/10.61944/bids.v2i2.75","url":null,"abstract":"This study applies the Improved Rank Order Centroid (IROC) to the Indonesian patent submission process within a Multi-Criteria Decision Making (MCDM) framework. The study evaluates four primary elements in patent assessment: \"Patent Description,\" \"Illustration,\" \"Inventor's Ownership Statement,\" and \"Rights Assignment Declaration.\" Preliminary findings indicate the importance of \"Patent Description,\" followed by the other elements in descending order of significance. The evaluation also encompasses three applicant alternatives, with the Second Applicant emerging as the most favorable. The study further contrasts IROC outcomes with MAGIQ and AHP methodologies. While rank-based techniques like ROC and IROC generally produce similar weight distributions, the AHP method, which employs pairwise comparisons, often displays variations. The research underscores the potential of IROC in determining criterion weights, its comparison within the MAGIQ framework, and its validation through AHP. These insights aim to deepen our understanding of decision-making processes and analysis. The conclusion from comparing IROC results with MAGIQ and AHP indicates that the applicant rankings remain consistent. Therefore, further research is needed to understand the differences between evaluation methods and their impacts and explore the influence of cultural or regional factors in the patent submission process","PeriodicalId":510953,"journal":{"name":"Bulletin of Informatics and Data Science","volume":"21 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Setiawansyah Setiawansyah, Sanriomi Sintaro, Very Hendra Saputra, A. A. Aldino
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%.
{"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":"https://doi.org/10.61944/bids.v2i2.67","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":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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