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Journal : PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS

Unsupervised YouTube Video Segmentation of “Bendera One Piece” Content Using Medoid-Based Clustering with Statistical Significance Testing Budiaji, Weksi; Kumenap, Patricia; Delano, M Fabian; Wijaya, Ferdian; Riyanto, Rifqi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.639

Abstract

The curse of dimensionality and sparsity are well-documented phenomena in applied statistics where the data’s dimensionality (number of features) far outnumbers the observations. This work aims to present an integrated applied statistics framework to distill semantic structure from high-dimensional data by combining pre-processing, dimensionality reduction via principal component analysis, medoid-based clustering (partitioning around medoids and simple k medoids), and a modified Statistical Significance Clustering (SigClust) test for validation and inference in the context of viral media. In this case study, we demonstrate an approach that segments and interprets YouTube videos from the lens of the Indonesian viral media “Bendera One Piece” through its user commentary. The PCA-based dimensionality reduction helped resolve the curse of dimensionality, where the first principal component alone explained 80% of the variance in text-based features and captured a dominant socio-political pattern. Internal validation and the SigClust test agreed on the presence of a statistically significant three-cluster solution that could be labelled as the audiences of “Pop-Culture Enthusiasts”, “Cautious Observers”, and “Political Protesters”. The study presents the utility of integrating established statistical methods with a modified validation step for high-dimensional text data analysis and pattern recognition.
The Digital Frontline: A Thematic Analysis of User Grievances and Satisfaction Drivers for Indonesian Public Service Apps Bangkit Wijaya, Ferdian; Budiaji, Weksi; Priyantama Ramadhan Bagaskara, Rafly; Ainun Tazkia, Zilda; Dwi Anugrah Pertiwi, Dinda
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.738

Abstract

This research assesses Indonesia's digital public service ecosystem by analyzing 50 mobile applications from a wide range of state agencies. Using a computational content analysis of metadata and user reviews from the Google Play Store, this study presents a dual-faceted evaluation. First, a thematic analysis of negative reviews (1-2 stars) reveals that user grievances are overwhelmingly dominated by foundational issues, such as login/access problems, slow performance, and technical glitches, rather than a lack of advanced features. Second, a corresponding analysis of positive reviews (5 stars) identifies that user satisfaction is primarily driven by high-quality features, ease of use, and overall application reliability. Quantitative findings show significant performance disparities across institutional categories, with Ministrydeveloped apps receiving the lowest average user satisfaction. An Importance-Performance Quadrant Analysis further uncovers a critical paradox: many high-download, mandatory apps suffer from low user ratings, indicating a clear disconnect between enforced adoption and usercentric quality. The research concludes that enhancing digital public services requires a strategic shift from feature proliferation to foundational reliability. Ensuring robust core functionalities is paramount to building citizen trust and achieving a successful digital transformation.