Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Muhammad Rafli Wijaya
Universitas Negeri Medan
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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

Found 2 Documents
Search

 1 
Analisis Perbandingan DFA dan NFA Dalam Pencocokan String Muhammad Rafli Wijaya; Zulfahmi Indra; M. Gali Almahdi; Sebastian Saut Marulitua Sinaga
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp64-70

Abstract

String matching is a fundamental process in pattern recognition systems and large-scale text processing, where computational efficiency significantly affects system performance. This study analyzes the comparison between Deterministic Finite Automata (DFA) and Non-deterministic Finite Automata (NFA) in string matching using the pattern (a|b)*abb. The research was implemented through a web-based simulator developed with JavaScript, HTML, and CSS and evaluated using eight short test strings representing accepted and rejected inputs, as well as longer strings to observe execution time differences. The results indicate that DFA and NFA produce identical acceptance outcomes for all test cases, indicating that both automata recognize the same language. However, DFA demonstrates better computational efficiency because each input symbol is processed through a single deterministic transition, whereas NFA requires tracking a set of active states, which increases computational overhead. For longer strings, the execution time gap becomes more pronounced, with DFA remaining consistently faster than NFA. These findings suggest that DFA is more suitable for string matching applications requiring time efficiency, while NFA offers greater flexibility in transition design.
Application of the K-Means Clustering Algorithm in the Analysis of Popularity and Growth Trends of Python Packages on the PyPI Dataset Muhammad Rafli Wijaya; M Gali Almahdi; Sebastian Saut Marulitua Sinaga; Benedict Sandi Pangestu Rosa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2233

Abstract

The rapid growth of the Python ecosystem has led to an increasing number of packages on the Python Package Index (PyPI), generating a massive volume of download data. This data can be utilized to analyze popularity levels and growth trends of libraries used by the developer community. This study aims to identify popularity patterns and growth trends of Python packages using the K-Means Clustering algorithm. The dataset was obtained from PyPI via the Google BigQuery platform with a one-year observation period using a 1% sampling technique. The pre-processing stage included a filtering process to select the 100 packages with the highest number of downloads and the formation of six main features representing the characteristics of library usage patterns. The data was then normalized using Standard Scaling, while the optimal number of clusters was determined using the Elbow Method and evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results showed that the optimal number of clusters is four, with a DBI value of 0.5534 and a Silhouette Score of 0.5748 (the highest among k = 2-10 ), representing the categories of ecosystem foundation libraries, medium-popularity libraries, libraries with concentrated download spikes, and libraries with very rapid usage growth. These results indicate that K-Means Clustering is effective for identifying popularity patterns and library growth trends in large-scale PyPI datasets.
Co-Authors Benedict Sandi Pangestu Rosa M Gali Almahdi M. Gali Almahdi Sebastian Saut Marulitua Sinaga Zulfahmi Indra, Zulfahmi