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Random Forest Optimization Using Particle Swarm Optimization for Diabetes Classification Pratama, Pangeran Fadillah; Rahmadani, Desvita; Nahampun, Rahma Sani; Harmutika, Della; Rahmadeyan, Akhas; Evizal, Muhammad Fikri
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.809

Abstract

Diabetes mellitus is a chronic degenerative disease caused by a lack of insulin production in the pancreas or the body's ability to use insulin less effectively. According to a report by the World Health Organization (WHO), 4% of the total deaths in the world are caused by diabetes. The International Diabetes Federation (IDF) notes that in 2013 there has been an increase in diabetes sufferers. Indonesia is the seventh place with the largest number of cases of diabetes mellitus. In this study, the method used to classify diabetes is using a random forest algorithm with Particle Swarm Optimization (PSO) optimization. This study resulted in an accuracy of the random forest classification algorithm of 78.2% and 82.1 using PSO optimization with an increase in value of 3.9%. It can be concluded that PSO optimization can provide a better increase in classification accuracy values when compared to the random forest algorithm without PSO optimization
EMPLOYEE PAYROLL SYSTEM DESIGN USING EXTREME PROGRAMMING ALGORITHM Hamzah, Muhammad Luthfi; Savra, Daffa Takratama; Harmutika, Della; Nahampun, Rahma Sani; Muzaffar, Ali Alamuddin; Syahroni, Anjasy; Hamid, Abdul
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.344

Abstract

The employee payroll system was built to make it easier for companies, especially those that still handle payroll manually, to manage employee payroll data. Company X still records employee information, absence information, and salary information manually in a book as part of its employee payroll process. The design of the employee payroll system in this study uses the Extreme Programming method. The Extreme Programming approach is used in the design of the employee payroll system in this study. This study also tests the system. The system is tested using White-box and Black-box testing methodologies, with the aim of testing the functional and non-functional requirements of the system. The findings of this study lead to the creation of an employee payroll information system that Company X can use to facilitate the payroll process for its employees.