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All Journal JUITA : Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JURSIMA (Jurnal Sistem Informasi dan Manajemen) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Computer Science (CO-SCIENCE) Indonesian Journal of Networking and Security - IJNS Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Paradigma Jurnal Insan INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System JURSIMA Jurnal Sistem Informasi dan Manajemen
Herlambang Brawijaya
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Journal : Paradigma

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Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO Widodo, Slamet; Brawijaya, Herlambang; Samudi, Samudi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3282

Abstract

This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.
Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO Widodo, Slamet; Brawijaya, Herlambang; Samudi, Samudi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3282

Abstract

This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.
Co-Authors - UBSI, Slamet Doni Andriansyah Elly Mufida, Elly Endang Retnoningsih Eni Pudjiarti Eva Rahmawati Hikmah, Noer Hylenarti Hertyana Indah Ariyati Lala Nilawati Noer Hikmah Rudianto, Biktra Samudi Samudi Samudi Samudi Samudi Siti Faizah Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Toto Haryanto Verry Riyanto