Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi
Triandes Sinaga
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Transportation Veterinary

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Sosialisasi Peran Virtual Reality terhadap Pembelajaran dan Edukasi Kevin Bastian Sirait; Jefri Junifer Pangaribuan; Okky Putra Barus; Triandes Sinaga; Romindo, Romindo
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 2 (2025): Mei 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i2.5022

Abstract

Education orients on the process of transferring and acquiring knowledge and skills from learning activities. With the use of Virtual Reality (VR) in education, it can help students to experiment with the learned concepts and assess their implications within the virtual environment. The idea and implementation of VR in education are crucial since they enhance the student’s learning experience and process to understand various concepts and implement them to solve problems. Therefore, this socialization aims to provide deeper insights to the students of SMA Chandra Kumala Medan on how VR can help them improve their learning experience and performance. At this event, the socialization is conducted by following three sessions: (1) material presentation, (2) questions and answers session, and (3) VR demonstration where the students can take part. The results show that the students are highly engaged in all three sessions. It is found in the questions asked by the students, from how to create a virtual environment to the roles and impact of VR in real life (e.g., business). These findings indicate that the students are interested in how VR can improve their learning experience by understanding and testing new ideas or concepts within the virtual environment.
Analisis Kualitas Wine Menggunakan Machine Learning dengan Pendekatan SMOTE dan Seleksi Fitur Triandes Sinaga; Kevin Bastian Sirait; Pangaribuan, Jefri Junifer; Barus, Okky Putra; Romindo, Romindo
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5436

Abstract

Conventional wine quality assessment remains reliant on subjective expert judgment, which introduces potential bias and inconsistency in quality control processes. This study aims to develop an objective and automated machine learning-based classification model to enhance the accuracy of wine quality prediction. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, along with ANOVA F-test-based feature selection to optimize model performance. The White Wine Quality dataset from the UCI Machine Learning Repository (4,898 samples, 11 numerical features) was utilized to evaluate five classification algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Before SMOTE application, the Random Forest model achieved an accuracy of only 67.55%. After implementing SMOTE and parameter tuning, the Random Forest (Tuned) model demonstrated the best performance with 90.29% accuracy, 89.99% precision, 90.29% recall, and 89,97%.  % F1-score. Additionally, Decision Tree and KNN algorithms also exhibited notable improvements. SMOTE effectively balanced extreme minority class representations (quality levels 3 and 9). The most influential features in quality classification were alcohol content, density, and chlorides. These findings indicate that the proposed framework offers a reliable, objective, and scalable solution for automated wine quality control in industrial production environments.
Evaluasi Robustness dan Deployment Readiness Model XGBoost untuk Prediksi Risiko Gagal Jantung di Indonesia Triandes Sinaga; Ayumi, Ayumi; Pangaribuan , Jefri Junifer
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.7087

Abstract

Cardiovascular diseases, particularly heart failure, remain a leading cause of mortality in Indonesia, affecting an estimated 2.78 million individuals. This study aims to develop a heart failure risk prediction model using the XGBoost algorithm and to evaluate its performance through a comparative validation approach across two datasets with distinct characteristics. The primary model was trained on a large-scale Indonesian population dataset (N = 158,355; 28 features) representing the complexity of real-world clinical data, while the UCI Heart Disease dataset (N = 918; 12 features) was used as a benchmark under more controlled conditions. Experimental results show that the Indonesian model achieved a testing accuracy of 73.50% with a very small training–testing performance gap of 0.53% and an AUC-ROC value of 0.814, indicating strong stability and generalization capability. In contrast, the model trained on the UCI dataset obtained a higher accuracy of 88.59% but exhibited moderate overfitting, reflected by a larger performance gap of 4.60%. Feature importance analysis consistently identified a history of heart disease, hypertension, and smoking behavior as the most influential predictors across both datasets. These findings highlight that model stability and generalization on real-world data are more critical than raw accuracy derived from small, idealized datasets when assessing the clinical deployment readiness of medical artificial intelligence systems in Indonesia.
Co-Authors Ayumi, Ayumi Barus, Okky Putra Kevin Bastian Sirait Pangaribuan , Jefri Junifer Pangaribuan, Jefri Junifer Romindo