Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Advances in Data and Information Systems JAMBIS : Jurnal Administrasi Bisnis
Lestari, Agung Tri
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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

Found 2 Documents
Search

 1 
Pengaruh Beban Kerja Dan Budaya Organisasi Terhadap Employee Engagement Pada Nam Air Di Jakarta Lestari, Agung Tri; Rofiyanti, Eka; Wulandari, Winda Wulandari
JAMBIS : Jurnal Administrasi Bisnis Vol. 4 No. 1: Februari 2024
Publisher : JAMBIS : Jurnal Administrasi Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31334/jambis.v4i1.3818

Abstract

This study aims to determine and analyze the influence of workload and organizational culture on employee engagement at NAM Air in Jakarta. This is motivated by the problem that many pilots choose to change jobs. Furthermore, there are also problems with pilot flying hours and working hours which are only valid for two months. This research was conducted at NAM Air in Jakarta from April to August 2021. This study used a quantitative approach. Data was collected by distributing questionnaires to 44 respondents. This study shows that there is a significant influence between the workload variable on the employee engagement variable, which is 24.5%, there is a significant influence between the organizational culture variable on the employee engagement variable, which is 21.4%, and there is a positive and significant effect on workload and culture. organization simultaneously on employee engagement pilot PT. NAM Air is 32.3%
Machine Learning Algorithm for Questionnaire-Based Student Learning Style Classification Yuliansyah, Herman; Lestari, Agung Tri; Yudhana, Anton
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1536

Abstract

Identifying students’ learning styles is an important factor in supporting adaptive and data-driven learning. However, conventional methods based on manual questionnaires still have limitations in terms of efficiency and accuracy for data processing. This study presents a comparative analysis of machine learning algorithms to classify student learning styles based on questionnaire data. The dataset used consists of 1,170 student data with three learning style classes, namely visual, auditory, and kinesthetic. The four supervised learning algorithms used are Naïve Bayes, Decision Tree, Random Forest, and K-Nearest Neighbors. Model performance evaluation was conducted using 5-fold (80:20) and 10-fold (90:10) cross-validation with accuracy, precision, recall, and F1-score metrics. The results of the experiment show that the Naïve Bayes algorithm has the most optimal and stable performance with the highest accuracy value of 90.60% in both validation scenarios. These findings indicate that machine learning-based classification approaches, particularly Naïve Bayes, are effective for identifying student learning styles and have the potential to support the development of adaptive and personalized learning systems.
Co-Authors Anton Yudhana Eka Rofiyanti Herman Yuliansyah, Herman Wulandari, Winda Wulandari