Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Arrazak, Fadlan
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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

Found 2 Documents
Search

 1 
Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients Sari, Windy Junita; Melyani, Nasya Amirah; Arrazak, Fadlan; Anahar, Muhammad Asyraf Bin; Addini, Ezza; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1119

Abstract

Stroke is the second most common cause of death globally, making up about 11% of all deaths from health-related deaths each year, the condition varies from mild to severe, with the potential for permanent or temporary damage, caused by non-traumatic cerebral circulatory disorders. This research began with data understanding through the acquisition of a stroke patient health dataset from Kaggle, consisting of 5110 records. The pre-processing stage involved transforming the data to optimize processing, converting numeric attributes to nominal, and preparing training and test data. The focus then shifted to stroke disease classification using Random Forest, Support Vector Machines, and Neural Networks algorithms. Data processing results from the Kaggle dataset showed high performance, with Random Forest achieving 98.58% accuracy, SVM 94.11%, and Neural Network 95.72%. Although SVM has the highest recall (99.41%), while Random Forest and ANN have high but slightly lower recall rates, 98.58% and 95.72% respectively. Model selection depends on the needs of the application, either focusing on precision, recall, or a balance of both. This research contributes to further understanding of stroke diagnosis and introduces new potential for classifying the disease.
Analisis Sentimen Terhadap Pemain Naturalisasi dan Lokal Tim Nasional Sepakbola Indonesia Menggunakan Support Vector Machine Arrazak, Fadlan; Afdal, M; Novita, Rice; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7471

Abstract

The inclusion of naturalized players in Indonesia's national football team has sparked diverse public reactions, particularly on social media platforms like Twitter. This study aims to compare public opinion toward naturalized and local players through sentiment analysis. A total of 2,342 tweets were categorized into three sentiment classes: positive, neutral, and negative. Naturalized players received a higher number of positive sentiments, totaling 809, compared to 333 negative and 231 neutral sentiments. In contrast, local players gained 465 positive sentiments, 317 negative, and 187 neutral, indicating a generally more favorable perception of naturalized players among the public. Further analysis was conducted using the Support Vector Machine (SVM) classification algorithm along with the SMOTE technique for data balancing, focusing on five key aspects: performance, experience, physical condition, adaptability, and communication. The classification results showed that naturalized players outperformed in physical condition with an accuracy of 96 percent, followed by performance and adaptability, each at 90 percent. On the other hand, local players showed superiority only in communication with an accuracy of 92 percent. In terms of precision and recall, naturalized players again led in physical condition, achieving 97 percent precision and 96 percent recall, while local players excelled in communication with both precision and recall at 92 percent. These findings offer valuable insights for policymakers and football organizations in formulating more effective naturalization strategies.
Co-Authors Addini, Ezza Al-Sawaff, Zaid Husham Anahar, Muhammad Asyraf Bin M Afdal Manickam, Selvakumar Megawati - Melyani, Nasya Amirah Rice Novita Sari, Windy Junita