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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Fatika La Viola Ifanka
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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MONITORING DASHBOARD USING LINEAR REGRESSION FOR EMPLOYEE PERFORMANCE Muhammad Oktoda Noorrohman; Mochammad Ilham Aziz; Saifulloh Azhar; Satria Pradana Rizki Yulianto; Widodo; Devi Ratnasari; Fatika La Viola Ifanka; Melvien Zainul Asyiqien
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 11 (2025): OCTOBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v4i11.1101

Abstract

Company management needs to continuously monitor and measure the performance of its employees to ensure the achievement of the goals that have been set. The performance monitoring process requires data and information obtained from 35 employees. The problem is in the process of the employee payroll system, attendance, leave and the main thing is data management for monitoring employees still use conventional method by manual. The results of performance monitoring will then be conveyed to interested parties, efficiently and effectively. After that, the existing data were analyzed using SPSS which stands for Statistical Product and Service Solution. Validity testing can also be done using SPSS and produces a validity test of the data, which is < 0.05 so it is valid. The reliability test of the data is > 0.70, 0.751 for employee salaries and 0.757 for employee performance so it is reliable. The normality test of the data are > 0.05, 0.077 for employee salaries and 0.059 for employee performance so that the data is normally distributed. The linearity test of the data is 0.604 > 0.05, it can be concluded that there is a linear relationship between salary and employee performance. Regression analysis test simple linear data from the data, namely the significance level of 0.001 < 0.05 then the regression model can be used to predict the participation variable or in other words there is an effect of the salary variable on the performance variable.
ANALYSIS OF PATIENT ATTENDANCE RATES USING RUSBOOST Widodo; Dyah Ika Krisnawati; Saifullah Azhar; Fatika La Viola Ifanka; Muhammad Ilham Aziz; Satria Pradana Rizky Yulianto; Devi Ratnasari; Muhammad Oktoda Noorrohman
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 11 (2025): OCTOBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v4i11.1110

Abstract

Patients have the option of undergoing examinations and treatment without having to stay in the hospital. The number of clinics serving patients continues to grow due to the high demand and busy schedules faced by patients. However, hospitals and clinics are still operating well because there are patients who need services, both outpatient and inpatient. In many countries, numerous clinics and hospitals have not implemented an effective data management system for outpatient queues. This results in a number of registered patients not showing up for their appointments, which is certainly detrimental to the nurses and doctors on duty that day. This situation is a loss for clinics and hospitals because manual data management prevents them from predicting the number of patients who will visit. One way to organize patient visit data, both for outpatient and inpatient care, is to utilize big data. The method used in processing this data is Decision Tree classification with Rusboost. By applying Decision Tree classification and Rusboost, we can obtain more accurate predictions, thereby assisting in decision-making.
Co-Authors Devi Ratnasari Dyah Ika Krisnawati Melvien Zainul Asyiqien Mochammad Ilham Aziz Muhammad Ilham Aziz Muhammad Oktoda Noorrohman Saifullah Azhar Saifulloh Azhar Satria Pradana Rizki Yulianto Satria Pradana Rizky Yulianto Widodo