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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Computatio : Journal of Computer Science and Information Systems Faktor Exacta JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Nasional Komputasi dan Teknologi Informasi The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Academia Perspectives Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Prioritas : Jurnal Pengabdian Kepada Masyarakat Journal of Computing and Informatics Research Jurnal Publikasi Teknik Informatika (JUPTI) Journal of Informatics, Electrical and Electronics Engineering Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Bulletin of Artificial Intelligence Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Aksi Kita: Jurnal Pengabdian Kepada Masyarakat International Journal of Informatics and Data Science
Erlin Windia Ambarsari
Program Studi Teknik Informatika, Universitas Indraprasta PGRI
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Journal : International Journal of Informatics and Data Science

 1 
Clustering of YouTube Viewer Data Based on Preferences using Leiden Algorithm Erlin Windia Ambarsari; Aulia Paramita; Desyanti
International Journal of Informatics and Data Science Vol. 1 No. 2 (2024): June 2024
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v1i2.45

Abstract

This study aims to analyze YouTube viewer engagement patterns by applying the Leiden algorithm for clustering based on user interactions such as likes, dislikes, and subscription behaviors in correlation with video duration. Therefore, the method that we used begins with data cleaning to ensure completeness, followed by selecting relevant features and applying z-score normalization to equalize their contributions. A similarity graph is constructed using cosine similarity, representing instances as nodes and their relationships as edges. The Leiden algorithm is then applied to optimize modularity and extract clusters, with results integrated into the original dataset for analysis. Dimensionality reduction using PCA facilitates cluster visualization, while statistical summaries and distribution plots provide deeper insights into cluster characteristics. Subsequently, we obtained a dataset sourced from the YouTube content creator @ArmanVesona, which includes 237 instances with ten features: Shares, Comments Added, Dislikes, Likes, Subscribers Lost, Subscribers Gained, Views, Watch Time (hours), Impressions, and Click-Through Rate (%). The analysis reveals two distinct clusters: Cluster 0, characterized by lower engagement and stable audience, and Cluster 1, exhibiting higher engagement but higher subscriber churn. The findings highlight the effectiveness of the Leiden algorithm in detecting well-connected communities and provide insights into viewer behavior, aiding in the development of improved content strategies and targeted marketing approaches.
Film Popularity Analysis through Combined K-Means Clustering and Gradient Boosted Trees Agi Candra Bramantia; Desyanti; Jeperson Hutahaean; Erlin Windia Ambarsari
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.81

Abstract

The dynamic and competitive nature of the global film industry presents complex challenges in predicting film popularity, as success is shaped by the interplay of production investment, casting decisions, and audience preferences. This research addresses the limitations of previous studies that have focused primarily on direct relationships, such as budget versus box office returns, by introducing an integrated analytical framework that combines K-Means clustering and Gradient Boosted Trees (GBT) with explainable AI techniques. Utilizing the TMDB movie dataset and constructing features such as actor influence and studio power, the study segments films and predicts audience ratings while providing interpretable visualizations. The results reveal four distinct film clusters and demonstrate that actor influence and budget allocation are the most significant predictors of popularity. The proposed model achieves an R² score of 0.75 and a mean squared error of 0.35 in predicting audience ratings, while cluster analysis shows that Blockbuster films reach the highest average ratings (6.76), and Underperforming films the lowest (2.42). By integrating interpretable predictive modeling and interactive scenario tools, this research offers both theoretical advancement and practical value for industry stakeholders. However, the findings are limited by the available metadata and do not account for factors such as marketing or real-time audience trends, suggesting opportunities for future research to expand the analytical framework.
Co-Authors Abdul Karim Ade Irma Agustina Lubis Agi Candra Bramantia Agus Perdana Windarto Aulia Paramita Bramantia, Agi Candra Dedin Fathudin Desyanti Desyanti Desyanti Dodi Siregar Dodi Siregar Dona Katarina Dudi Parulian Fathudin, Dedin Fazrie, Mohammad Finata Rastic Andrari Frieyadie Furqoni Yudhistira Gravita Alfiani Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda, Herlinda Hetty Rohayani Jahril Jayawarsa, A.A. Ketut Jeperson Hutahaean Jumaryadi, Yuwan KUSTIAN, NUNU Laurent Nababan Mesran, Mesran Mohammad Taufan Asri Zaen Noni Selvia Noni Selvia Nunu Kustian Nurfidah Dwitiyanti Nurfidah Dwitiyanti Paramita, Aulia Parulian, Dudi Puji Lestari, Fibria Anggraini Purba, Bister Putri Dina Mardika Rahman, Vierhan Randi Ramliyana Reflan Revife Purba Relo Subagio Rezkiyana Hikmah Ridha Maya Faza Lubis Rizki Ridwan Rizki Ridwan Roswita Daeli RR. Ella Evrita Hestiandari Saidah, Fatiyah Setiawansyah Setiawansyah Siagian, Jaya Sari Anggraini Siti Khotijah Sofiansyah Fadli Sri Agustiani Br Siburian Sri Melati Sagita Subagio, Relo Sunarmintyastuti, Lies Vierhan Rahman Wahyu Nur Cholifah Wilatiktah, Anatasya Aulya