Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Algoritma
Ida Ariyani Hasanah
Universitas Budi Luhur
Computer Science & IT Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Perbandingan K-Means, DBSCAN, dan Louvain pada Peserta Pendidikan Kesetaraan di Kabupaten Balangan Ida Ariyani Hasanah; Riama Simanjuntak; Arief Wibowo
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3350

Abstract

This study aims to analyze the clustering patterns of equivalency education participants through a comparative clustering approach using three algorithms: K-Means (centroid-based), DBSCAN (density-based), and Louvain (graph-based). The dataset consists of 1,057 participants with numerical and categorical attributes representing heterogeneous characteristics. The research stages include data preprocessing and the implementation of clustering algorithms on the same dataset to maintain comparison consistency. Evaluation was conducted using the Silhouette Score and Davies-Bouldin Index (DBI) as internal validation metrics, as well as external validation through expert confirmation to ensure the contextual relevance of the results. The findings indicate that K-Means and DBSCAN produced a Silhouette Score of 0.040, reflecting poor cluster separation quality and the dominance of one large cluster. DBSCAN demonstrated an advantage in detecting noise; however, it was unable to significantly improve cluster separation quality in data with a high level of homogeneity. In contrast, the Louvain algorithm generated a more balanced community structure with a low imbalance ratio, making it more capable of representing relational connections among data points that are not fully captured by distance-based approaches. This study contributes through a comparative analysis across clustering approaches in the context of equivalency education, as well as through the integration of quantitative and contextual validation. The findings confirm that graph-based approaches are more adaptive for data with high homogeneity and have the potential to serve as a basis for participant segmentation to support more effective data-driven decision-making in the education sector.
Instagram Content Analysis of a Higher Education Institution using Social Media Analytics and Topic Modeling Jum'atul Fitri; Ida Ariyani Hasanah; Setyawan Widyarto
SISTEMASI Vol 15, No 10 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i10.6946

Abstract

Social media is an important communication channel for higher education institutions to share information and build institutional identity. This study analyzes Instagram content from a selected institutional account, including collaborative posts involving other accounts. A descriptive quantitative approach was applied to 100 valid posts collected through web scraping. The analysis combined Exploratory Data Analysis (EDA) to describe content formats, posting activity, likes, and comments with Latent Dirichlet Allocation (LDA) to identify recurring themes in captions. The results showed that image posts were the most common format, followed by carousel posts and videos. LDA identified three main themes: student achievement and development, religious values and character building, and institutional collaboration and community engagement. Religious values and character building accounted for the largest proportion of posts and had the highest average numbers of likes and comments among the identified topics. Content specifically focused on student admission was less prominent in the identified themes. These findings provide descriptive insights for future content planning; however, they do not establish causal relationships between content characteristics, engagement, or admission outcomes.
Co-Authors Arief Wibowo Jum'atul Fitri Riama Simanjuntak Setyawan Widyarto