Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal JUITA : Jurnal Informatika ILKOM Jurnal Ilmiah
Arif Munandar
Universitas Amikom Purwokerto
Computer Science & IT

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

Found 2 Documents
Search

 1 
A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients Arif Munandar; Wiga Maulana Baihaqi; Ade Nurhopipah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1632.344-352

Abstract

Cardiovascular disease is one of the deadliest diseases, claiming around 17 million lives worldwide each year. According to data from the World Health Organization (WHO), more than four out of five deaths from cardiovascular disease are caused by heart attacks and strokes, and one-third of these deaths occur prematurely in people under the age of 70. Machine learning approaches can be used to detect the disease. This research aims to improve the prediction model of cardiovascular heart failure patient survival using C4.5, KNN, Logistic Regression algorithms, and the ensemble learning method of Voting Classifier. Based on the testing results, each model showed a significant increase in accuracy in the 70:30 ratio. Logistic Regression and C4.5 achieved the same accuracy, 89.47%, KNN obtained 91.23%, and Voting Classifier experienced a considerable improvement, reaching 94.74%. In testing with ratios of 90:10, 80:20, and 70:30, KNN demonstrated high accuracy but had significant overfitting, with a difference of 7-9% between training and testing accuracy scores in the 90:10 and 80:20 ratios. On the other hand, Voting Classifier showed stable performance in the 70:30 ratio, with an accuracy difference between training and testing scores below 1%. The conclusion of this research is that the Voting Classifier can assist the performance improvement of algorithms for classifying the survival expectancy of cardiovascular heart failure patients into 'Survived' or 'Deceased', compared to Logistic Regression, KNN, and C4.5.
Sentiment Analysis of Student Comment on the College Performance Evaluation Questionnaire Using Naïve Bayes and IndoBERT Wiga Maulana Baihaqi; Arif Munandar
JUITA : Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17336

Abstract

The development of the Internet has played a significant role in various aspects of life and has generated vast amounts of data, including student comments about universities. The challenge in analyzing comment data is the large number of students providing feedback, which makes manual analysis impractical. The purpose of this study is to analyze the performance evaluation of universities by students in terms of positive and negative sentiments, with the aim of assessing the level of student satisfaction with all elements and areas of university operations. This research utilized the Naïve Bayes algorithm and the IndoBERT model to build a classification model based on questionnaire data, starting from the data collection process, data preprocessing, feature extraction, modeling, and evaluation. The results of the IndoBERT model demonstrated the best performance, with an accuracy of 85%. The IndoBERT model effectively recognizes sentiments in text, distinguishing between positive and negative comments regarding university performance.
Co-Authors Nurhopipah, Ade Wiga Maulana Baihaqi