Paradigma

Analysis of IT Service Desk Applications Using the Servqual Method at The Republic of Indonesia National Library

2024-03-20T14:50:25+07:00

The National Library has implemented an Electronic-Based Government System (SPBE) so that library service operations and operations for the national library staff have used a computerized system which results in the PERPUSNAS requiring centralized management of IT problems, namely the IT Service Desk. As an IT problem service, IT Service Desk needs to be analyzed to measure the level of user satisfaction to determine the quality of its services. In this study, the measurement of the level of user satisfaction of IT service desk services was carried out using the Servqual method which has 5 dimensions of service quality, namely Tangible, Reliability, Responsiveness, Assurance, and Emphaty by looking for the value of the gap between the value of perception and the value of expectations from users. The results of the IT Service Desk measurement at PERPUSNAS RI obtained a gap of -0.78 with the category "Good Enough".

Comparison of Supervised Learning Classification Methods on Accreditation Data of Private Higher Education Institutions

2024-03-13T14:40:13+07:00

This research aims to analyze and compare supervised learning classification methods using a case study of accreditation data for private higher education institutions within the LLDikti Region III contained in BAN-PT. In addition, this research also uses Weka machine learning software in its calculations. The initial step taken is to prepare the software used for supervised learning analysis, then pre-processing the data, namely labeling data that has a categorical data type, after that determining data for testing data. The next step is to test each classification method. The methods used for comparison are logistic regression, K-nearest neighbor, naive bayes, super vector machine, and random forest. Based on the calculation results, the Kappa Statistic and Root mean squared error values obtained are 1 and 0 for the logistic regression method, 0.979 and 0.0061 for the K-nearest neighbor method, 1 and 0.2222 for the super vector machine method, 0.969 and 0.0341 for the naive bayes method, 1 and 0 for the decision tree method, and 0.5776 and 0.1949 for the random forest method, respectively. The logistic regression and decision tree methods in this study get Kappa Statistic and Root mean squared error values of 1 and 0 respectively so that they are said to be good and acceptable, thus the two classification methods are the most appropriate methods and are considered to have the highest accuracy.

Optimizing Heart Failure Detection: A Comparison between Naive Bayes and Particle Swarm Optimization

2024-03-05T18:25:03+07:00

This research focuses on the importance of early detection of heart failure which is a serious global health problem. Given the variety of symptoms of heart failure, accurate early detection methods are needed with the aim of reducing the impact of this disease. This study uses the Naïve Bayes (NB) method which has been proven effective in classifying heart failure with significant variations in accuracy by integrating Particle Swarm Optimization (PSO) to improve the model. The evaluation model involves a confusion matrix including accuracy, precision, recall, and Area Under the Curve. The research results show that the integration of PSO in NB results in an increase in accuracy of 7.73%, an increase in precision of 6.42%, and an increase in recall of 1.93%. Although there was a small decrease in AUC. This research shows that the success of NB with PSO can help improve the performance of early detection of heart failure. This indicates the importance of this research in developing more accurate and effective detection methods for critical health conditions such as heart failure.