{"id":2430,"date":"2025-09-26T13:51:25","date_gmt":"2025-09-26T06:51:25","guid":{"rendered":"https:\/\/www.its.ac.id\/publikasi\/?p=2430"},"modified":"2025-09-26T13:51:25","modified_gmt":"2025-09-26T06:51:25","slug":"comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2","status":"publish","type":"post","link":"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/","title":{"rendered":"Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification"},"content":{"rendered":"<div id=\"authorString\"><em>Tri Yulianto Nugroho, Umi Laili Yuhana, Daniel Siahaan<\/em><\/div>\n<p>&nbsp;<\/p>\n<div id=\"articleAbstract\">\n<h4>Abstract<\/h4>\n<div>Classifying agility levels presents challenges due to variations in team members\u2019 personalities, roles, and undesirable behaviors. This study aims to enhance classification accuracy by comparing the performance of three algorithms: K-Nearest Neighbors (KNN), Random Forest, and Fuzzy-Particle Swarm Optimization (F-PSO) in classifying agility levels using simple feature scaling as part of the data preprocessing. Simple feature scaling is employed to ensure that all parameters are on the same scale, thereby improving the model\u2019s effectiveness in learning classification patterns. F-PSO was selected for its ability to perform adaptive global search optimization within a fuzzy environment, while KNN and Random Forest serve as benchmarks. The study involved 160 participants from various Scrum teams to evaluate the effectiveness of these algorithms. The parameters considered included team members\u2019 personalities (based on the Keirsey model), roles within the team, and the identification of negative behavior patterns (antipatterns). The results indicated that the F-PSO algorithm significantly outperformed KNN and Random Forest in terms of accuracy, improving from an average accuracy of 25% before optimization to 93.75% after applying F-PSO. This approach enables Scrum teams to identify and address obstacles affecting agility, facilitating earlier problem prediction and resolution, leading to more adaptive and effective teams.<\/div>\n<\/div>\n<div id=\"articleSubject\">\n<h4>Keywords<\/h4>\n<div>Agility Level; Anti-patternAntipattern KNN; Random Forest, Scrum; Simple Feature Scaling<\/div>\n<div><a href=\"https:\/\/iptek.its.ac.id\/index.php\/jts\/article\/view\/21992\">PDF<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Tri Yulianto Nugroho, Umi Laili Yuhana, Daniel Siahaan &nbsp; Abstract Classifying agility levels presents challenges due to variations in team members\u2019 personalities, roles, and undesirable behaviors. This study aims to enhance classification accuracy by comparing the performance of three algorithms: K-Nearest Neighbors (KNN), Random Forest, and Fuzzy-Particle Swarm Optimization (F-PSO) in classifying agility levels using [&hellip;]<\/p>\n","protected":false},"author":250,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[256],"tags":[],"class_list":["post-2430","post","type-post","status-publish","format-standard","hentry","category-jts-journal"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification - IJC ( ITS Journal Center )<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification - IJC ( ITS Journal Center )\" \/>\n<meta property=\"og:description\" content=\"Tri Yulianto Nugroho, Umi Laili Yuhana, Daniel Siahaan &nbsp; Abstract Classifying agility levels presents challenges due to variations in team members\u2019 personalities, roles, and undesirable behaviors. This study aims to enhance classification accuracy by comparing the performance of three algorithms: K-Nearest Neighbors (KNN), Random Forest, and Fuzzy-Particle Swarm Optimization (F-PSO) in classifying agility levels using [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/\" \/>\n<meta property=\"og:site_name\" content=\"IJC ( ITS Journal Center )\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-26T06:51:25+00:00\" \/>\n<meta name=\"author\" content=\"itspublikasi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"itspublikasi\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/\"},\"author\":{\"name\":\"itspublikasi\",\"@id\":\"https:\/\/www.its.ac.id\/publikasi\/#\/schema\/person\/1241b8776d05c54fbc628c0a83d59e3c\"},\"headline\":\"Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification\",\"datePublished\":\"2025-09-26T06:51:25+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/\"},\"wordCount\":234,\"commentCount\":0,\"articleSection\":[\"jts journal\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/\",\"url\":\"https:\/\/www.its.ac.id\/publikasi\/en\/2025\/09\/26\/comparison-of-knn-random-forest-and-f-pso-algorithms-on-simple-feature-scaling-for-agility-level-classification-2\/\",\"name\":\"Comparison Of KNN, Random Forest, And F-PSO Algorithms On Simple Feature Scaling for Agility Level Classification - 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