Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems
Muhammad Fadhil Hani
Unknown Affiliation
Computer Science & IT Education

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

Found 2 Documents
Search

 1 
Analysis of Naive Bayes and Support Vector Machine Algorithms in Classification of Diabetes Cases Based on Lifestyle Factors Awalia, Andi Dio Nurul; Muhammad Fadhil Hani; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9783

Abstract

The increase in diabetes mellitus cases globally, including in Indonesia, demands a more adaptive lifestyle-based risk prediction strategy. This study aims to evaluate and compare the efficiency of Support Vector Machine (SVM) and Naive Bayes in the diabetes risk classification process using a Hybrid clustering-classification approach . The data analyzed in this study were obtained from the Kaggle platform , with 8,500 data of diabetes patients analyzed based on the attributes of age, gender, and smoking history. The Clustering process was carried out using K-Means (k=3), resulting in three main groups with different lifestyle characteristics. The classification results showed that Naive Bayes provided stable performance with an F1-score of 0.975. Meanwhile, SVM excelled in terms of F1-score 98.3% and perfect AUC (1,000), and was able to classify all data in cluster C3 without error. However, SVM recorded a higher classification error in the majority cluster . This study concluded that SVM was superior by 0.8% over Naive Bayes . Naive Bayes is more suitable for balanced data, while SVM is effective for detecting patterns in minority groups. These findings support the use of a hybrid approach in lifestyle data-based diabetes early detection systems. Future research directions include integrating additional variables and ensemble techniques to improve model generalization.
The Influence of Students’ Perceptions of ChatGPT Use on Problem-Solving Ability: Integration of the Technology Acceptance Model and Self-Determination Theory Muhammad Fadhil Hani; Muhammad Yahya; Muhammad Yusuf Mappeasse; Ridwan Daud Mahande; M. Miftach Fakhri
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.10462

Abstract

The emergence of ChatGPT has fundamentally reshaped programming education while raising concerns about student over-reliance and weakened independent problem-solving. This research examines how students' attitudes toward ChatGPT including perceived utility (PU), accessibility (PEOU), capability (PC), and self-directedness (PA) influence their problem-resolution skills (PSA). The study integrates Technology Acceptance Model (TAM) with Self-Determination Theory (SDT) frameworks. Using quantitative methodology, data from 165 Informatics Education students at Universitas Negeri Makassar underwent PLS-SEM analysis. Measurement reliability was confirmed (factor loadings exceeded 0.733, AVE surpassed 0.50, CR topped 0.896, HTMT below 0.90). The structural model accounted for 70.2% variance in problem-solving capability (R² = 0.702). Three hypotheses received support: accessibility positively influenced problem-solving (β = 0.377, p < 0.001, f² = 0.138), capability showed positive effects (β = 0.334, p = 0.001, f² = 0.121), and self-directedness contributed positively (β = 0.218, p = 0.030, f² = 0.052). However, perceived utility showed no meaningful association (β = -0.017, p = 0.443). Results reveal cognitive achievement relies more on system accessibility and psychological need satisfaction than perceived utility, contradicting traditional TAM assumptions. Pedagogically, instructors should position ChatGPT as an intellectual companion enhancing critical thinking and independence rather than creating dependency. Study limitations include cross-sectional design, self-report measures, and single-institution sampling. Future research should employ longitudinal designs with objective assessments while controlling confounding variables.
Co-Authors Awalia, Andi Dio Nurul Dewi Fatmarani Surianto M. Miftach Fakhri Muhammad Yahya Muhammad Yusuf Mappeasse Ridwan Daud Mahande