Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Algoritme Jurnal Mahasiswa Teknik Informatika EdLib Journal (Education and Library Journal)
Nasution, Mukhtada Billah
Unknown Affiliation
Computer Science & IT Library & Information Science Other

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

Found 2 Documents
Search

 1 
Analisis Prediktif Tren Pendidikan di Indonesia Menggunakan KNN Studi Kasus Data Pendidikan 2021-2023 Nasution, Mukhtada Billah; Akhiyar Waladi; Ulfa Khaira; Pradita Eko Prasetyo Utomo
Education Library Vol. 1 No. 2 (2025): Education and Library Journal
Publisher : UPT Perpustakaan Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research focuses on the importance of education in improving the competitiveness of the younger generation in Indonesia, especially in facing the challenges of globalization and the digital revolution. Education trends in Indonesia during the 2021-2023 period have been dominated by two main factors, namely digitalization and equal access to education. A data-driven approach is used to predict education trends in 2024, using the K-Nearest Neighbor (KNN) algorithm to analyze data from the Central Statistics Agency (BPS) regarding the percentage of the population aged 25 years and over who have at least a high school education, categorized by gender. The result of this research will predict the trend of education in each region in 2024 whether it is decreasing, stable, or increasing. Through data collection and literature study, this research identifies relevant patterns and presents statistically-based predictions that can serve as a reference for stakeholders in the development of education in Indonesia. The results of this study are also expected to provide insights for policymakers in formulating effective strategies to address the education gap and promote inclusive digitalization..
Analisis Implementasi Algoritma Genetika pada Penjadwalan Mata Kuliah Nasution, Mukhtada Billah; Utomo, Pradita Eko Prasetyo; Iftita, Hasanatul
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.11139

Abstract

Scheduling university courses is a complex challenge involving multiple variables, such as time allocation, room assignment, lecturer availability, and student requirements. This study explores the implementation of a genetic algorithm as a solution for generating optimal and efficient schedules. The genetic algorithm operates through the principles of selection, crossover, and mutation to progressively explore the solution space. Experiments were conducted using parameters of 50 individuals and 40 chromosomes, yielding an optimal schedule at the 124th iteration with a maximum fitness value (fitness = 1). The results indicate that the fitness value of individuals increases as generations progress, affirming the genetic algorithm's capability to achieve optimization iteratively. However, the stochastic nature of the algorithm leads to variations in the number of generations required to reach optimal results, influenced by the problem's complexity and the number of chromosomes. This study demonstrates that genetic algorithms are highly effective in solving complex scheduling problems with significant efficiency, producing solutions that meet constraints and support more structured operations. The algorithm contributes substantially to the development of automated scheduling systems in educational institutions and other sectors.
Co-Authors Akhiyar Waladi Iftita, Hasanatul Khaira, Ulfa Utomo, Pradita Eko Prasetyo