Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal The Scientific Journal of Information Systems
Pungky Hari Wira Atmaja
Cendekia Abditama University
Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Machine Learning-Based Knowledge Trend Analysis Using LDA for Strategic Decision-Making Fajar Muttaqi; Moh Alfaujianto; Pungky Hari Wira Atmaja
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.331

Abstract

In the digital economy, Knowledge Management Systems (KMS) often fail to provide actionable insights due to information overload, leaving valuable expertise fragmented and underutilized. This research aims to integrate Machine Learning (ML) to transform passive data into proactive strategic foresight by analyzing knowledge trends. Using a longitudinal dataset of search logs and document metadata, the study implements a text-mining pipeline centered on Latent Dirichlet Allocation (LDA) to extract thematic clusters. The model identified eight distinct knowledge domains, with "Advanced Data Analytics" emerging as a high-growth sector (TVI = +0.13), while a critical "Knowledge Gap" in cybersecurity was detected where search demand outpaced document supply by 58%. This study contributes by proposing a Trend Velocity Index (TVI) to quantify knowledge evolution and detect knowledge gaps, providing a robust framework for leaders to optimize resource allocation and ensure institutional agility.
Co-Authors Fajar Muttaqi Moh Alfaujianto