Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Ceddi Journal of Information System and Technology (JST) Ceddi Journal of Education
Robbi Rahim
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm Hao Chieh Chiua; Robbi Rahim; Mahmud Mustapa; Kamaruddin; Akbar Hendra; Asnimar; Abigail, Omita
Ceddi Journal of Information System and Technology (JST) Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v2i2.47

Abstract

One of the data mining processes that is often used in machine learning is the data classification process. A decision tree is a classification algorithm that has the advantage of being easy to visualize because of its simple structure. However, the decision tree algorithm is quite susceptible to incorrect classification calculations due to the presence of noise in the data or imbalance in the data, which can reduce the overall level of accuracy. Therefore, the decision tree algorithm should be combined with other methods that can increase the accuracy of classification performance. Machine Learning is used through an artificial intelligence approach to solve problems or carry out optimization. Adaptive Boosting is used to optimize classification calculations. This study aims to examine the performance of Adaptive Boosting in the process of classifying second-degree diabetes mellitus patients using the Decision Tree algorithm. Diabetes mellitus is known as a chronic condition of the human body, the cause of which is an increase in the body's blood sugar levels because the body is unable to produce insulin or is unable to utilize insulin effectively, which is usually referred to as hyperglycemia.. By using a 60:40 data split, the Decision Tree algorithm produces an accuracy value of 95.71%, while the Adaptive Boosting-based Decision Tree results reach a value of 98.99%.
National Exam Try Out Application Design At SMP Negeri 35 Makassar Angraini, Dewi; Ernie C. Avila; Robbi Rahim; Supriadi Sahibu
Ceddi Journal of Education Vol. 2 No. 2 (2023): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/cje.v2i2.56

Abstract

The problem with the current system is that Try Out for the National Examination at SMP Negeri 35 Makassar is still done manually, therefore I provide a solution for designing the Try Out application, which produces an application that can help students in preparation for the National Examination. This research aims to design an effective and efficient national exam try-out application at SMP Negeri 35 Makassar, implementing the Dynamic System Development Method (DSDM) at SMP Negeri 35 Makassar. This data was obtained through literature studies and interviews. Data analysis uses PHP programming scripts and MySQL data. The results of this research show that the Try Out application is user-friendly, has an attractive appearance, and has complete features that can make things easier for students, especially in carrying out Try Outs, so that the process of processing test results is more effective and efficient, the Try Out Application is quite good, useful for users and so needed that it was declared feasible based on the tests carried out, with an average score of 86.00% from 44 respondents.
Co-Authors Abigail, Omita Akbar Hendra Asnimar Dewi Angraini, Dewi Ernie C. Avila Hao Chieh Chiua Kamaruddin Mahmud Mustapa Supriadi Sahibu