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All Journal International Journal of Scientific Multidisciplinary Research (IJSMR) Majalah Ilmiah METHODA
Yusuf Ijonris
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Web-Based Auction Information System at PT. Pegadaian Medan Yusuf Ijonris; Tamado Simon Sagala
International Journal of Scientific Multidisciplinary Research Vol. 3 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijsmr.v3i1.13750

Abstract

The development of the world of technology today is very fast. Technology has been used in various aspects of people's lives, especially the internet. The application of this internet technology makes it easier for people to access information because there are no limitations in terms of access to information. On this occasion, the researcher tried to develop a WEB-Based Pawn Auction System. The online auction service provider is the committee auctioning off customer auction items that are due and the buyer makes a price offer. This Pawn Auction Service Provider Service that is due is made using the PHP programming language, MySQL database, and XAMPP 1.6 webserver. This application will be made web-based so that it is easy to access by various parties and uses a user-friendly interface so that it is comfortable to use. This application can be run on various existing internet web browsers. In creating this application, the researcher concluded that the existence of the Pawnshop Auction Service Provider will increasingly open up opportunities for the wider community to get the chance to auction and obtain auctioned goods in the form of auctioned goods that have matured at a price that is cheaper than the market price
Sistem Pendukung Keputusan Penentuan Role Atlet Esports Provinsi Sumatera Utara Menggunakan Pendekatan Machine Learning Sagala, Tamado Simon; Yusuf Ijonris; Samosir, Nettina
Majalah Ilmiah METHODA Vol. 15 No. 3 (2025): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol15No3.pp313-321

Abstract

The continuous growth of esports as a technology-driven competitive activity has increased the demand for professional team management, particularly in assigning suitable roles to athletes based on their individual skills. One of the major challenges faced by coaches is determining athlete roles objectively, as this process is often influenced by subjective judgment and lacks support from systematic data analysis. To address this issue, this study aims to develop a decision support system for determining esports athlete roles in North Sumatra Province by utilizing machine learning approaches. This research applies several classification methods, namely K-Nearest Neighbor (KNN), Naive Bayes, and Support Vector Machine (SVM). The dataset used in this study consists of performance data for esports athletes that have undergone preprocessing stages and are divided into training and testing sets. The evaluation of model performance is conducted using standard classification assessment metrics to compare the effectiveness of each algorithm. The findings show that the KNN and SVM algorithms are better at classifying esports athletes' roles than the Naive Bayes algorithm. These two methods yield more stable and dependable results, rendering them more appropriate for facilitating decision-making processes concerning athlete role assignment. This study is expected to provide practical support for coaches and relevant stakeholders in making objective and data-driven decisions regarding the determination of esports athlete roles. Furthermore, future research can enhance the proposed system by increasing the amount of data and exploring other machine learning techniques to improve overall system performance
Co-Authors Sagala, Tamado Simon Samosir, Nettina Tamado Simon Sagala