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Menampilkan 1–3 dari 3 artikel
Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering
Fikri Muhamad Fahmi
; Budiman Budiman
; Nur Alamsyah
International Journal of Science and Mathematics Education
Vol 2
, No 2
(2025)
Given the increasing prevalence of mental health challenges in digital work settings, especially among IT remote workers, early detection mechanisms have become critically important. This study aims to improve the prediction accuracy of mental health conditions among IT remote workers by integrating feature engineering techniques within machine learning models. Five algorithms consisting of Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes were evaluated. Th...
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Enhancing Data Management Efficiency in Higher Education: A Case Study on the Development of P2M Applications
Dirham Triyadi
; Rijwan Rijwan
; Budiman Budiman
; Nur Alamsyah
; Reni Nursyanti
; Elia Setiana
International Journal of Computer Technology and Science
Vol 2
, No 1
(2025)
Developing research and community service (P2M) applications is crucial in enhancing efficiency and accuracy in managing related data at higher education institutions. This research aims to design a web-based application that simplifies the data management process for research, community service, and associated activities at Universitas Informatika dan Bisnis Indonesia (UNIBI). The research engaged the Rapid Application Development (RAD) methodology to actively incorporate stakeholders throughou...
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Optimizing Heart Disease Prediction : A Comparative Study of Machine Learning Models Using Clinical Data
Budiman Budiman
; Nur Alamsyah
; Elia Setiana
; Valencia Claudia Jennifer Kaunang
; Syahira Putri Himmaniah
International Journal of Science and Mathematics Education
Vol 1
, No 4
(2024)
Cardiovascular disease is a leading cause of death globally, necessitating effective predictive systems. This research aims to analyze the effectiveness of various machine learning (ML) models—Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN)—in predicting heart disease using publicly available health data. The study involved pre-processing data, training models, and evaluating them using accuracy, precision, recall, F1...
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