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Open access Jurnal SINTA 3

Analisis Klaster Film Indonesia Menggunakan K-Means Berdasarkan Popularitas TMDb, dan Rating IMDb

SemanTIK: Teknik Informasi · Vol. 12 No. 1 (2026) · pp. 129–138

DOI
10.55679/semantik.v12i1.289
ISSN
2502-8928, 2460-1446
Published
2026-06-30
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Perkembangan industri film Indonesia dalam satu dekade terakhir menunjukkan peningkatan yang signifikan, baik dari sisi jumlah produksi maupun tingkat apresiasi penonton. Namun demikian, penilaian terhadap kualitas dan popularitas film masih cenderung bersifat subjektif dan belum sepenuhnya didukung oleh analisis berbasis data yang sistematis. Penelitian ini bertujuan untuk mengelompokkan film Indonesia menggunakan algoritma K-Means berdasarkan indikator popularitas dari The Movie Database (TMDb) serta penilaian pengguna dari Internet Movie Database (IMDb). Data film diperoleh melalui API (Application Programming Interface) resmi TMDb dan diperkaya dengan rating serta jumlah penilaian IMDb menggunakan external identifier. Tahapan pra-pemrosesan meliputi pembersihan data, normalisasi, dan pemilihan fitur numerik yang relevan. Penentuan jumlah klaster optimal dilakukan menggunakan metode Elbow, sedangkan kualitas klaster dievaluasi menggunakan Silhouette Score. Hasil penelitian menunjukkan bahwa film Indonesia dapat dikelompokkan ke dalam tiga klaster utama, yaitu film dengan popularitas dan kualitas tinggi, film dengan popularitas tinggi namun kualitas rendah, serta film dengan popularitas rendah tetapi kualitas tinggi. Integrasi data TMDb dan IMDb melalui pendekatan K-Means menghasilkan pemetaan film yang lebih objektif dan dapat mendukung pengambilan keputusan strategis dalam industri perfilman nasional. The development of the Indonesian film industry over the past decade has shown significant growth in both production volume and audience appreciation. However, assessments of film quality and popularity remain largely subjective and are not fully supported by systematic data-driven analysis. This study aims to cluster Indonesian films using the K-Means algorithm based on popularity indicators from The Movie Database (TMDb) and user ratings from the Internet Movie Database (IMDb). Film data were collected through the official TMDb API (Application Programming Interface) and enriched with IMDb ratings and vote counts using external identifiers. Data preprocessing included data cleaning, normalization, and the selection of relevant numerical features. The Elbow Method was employed to determine the optimal number of clusters, while the Silhouette Score was used to evaluate clutster quality. The results show that Indonesian films can be grouped into three main clusters: films with high popularity and high quality, films with high popularity but low quality, and films with low popularity but high quality. The integration of TMDb and IMDb data using the K-Means approach provides a more objective film mapping and supports strategic decision-making in the national film industry.

Keywords

Collaboration network of the authors

5 authors · 5 co-authorship links

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  • Nusa Putra University
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Imas PatmasariNusa Putra University1
Rita Ajeng LestariNusa Putra University1
Wisuda JatmikoNusa Putra University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://semantik.uho.ac.id/index.php/journal/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Analisis Klaster Film Indonesia Menggunakan K-Means  Berdasarkan Popularitas TMDb, dan Rating IMDb</dc:title>
	<dc:creator>Rahman, Taufik</dc:creator>
	<dc:creator>Erfina, Adhitia</dc:creator>
	<dc:subject xml:lang="en-US">Data Mining</dc:subject>
	<dc:subject xml:lang="en-US">K-means Clustering</dc:subject>
	<dc:subject xml:lang="en-US">Film Indonesia</dc:subject>
	<dc:subject xml:lang="en-US">TMDb</dc:subject>
	<dc:subject xml:lang="en-US">IMDb</dc:subject>
	<dc:description xml:lang="en-US">Perkembangan industri film Indonesia dalam satu dekade terakhir menunjukkan peningkatan yang signifikan, baik dari sisi jumlah produksi maupun tingkat apresiasi penonton. Namun demikian, penilaian terhadap kualitas dan popularitas film masih cenderung bersifat subjektif dan belum sepenuhnya didukung oleh analisis berbasis data yang sistematis. Penelitian ini bertujuan untuk mengelompokkan film Indonesia menggunakan algoritma K-Means berdasarkan indikator popularitas dari The Movie Database (TMDb) serta penilaian pengguna dari Internet Movie Database (IMDb). Data film diperoleh melalui API (Application Programming Interface) resmi TMDb dan diperkaya dengan rating serta jumlah penilaian IMDb menggunakan external identifier. Tahapan pra-pemrosesan meliputi pembersihan data, normalisasi, dan pemilihan fitur numerik yang relevan. Penentuan jumlah klaster optimal dilakukan menggunakan metode Elbow, sedangkan kualitas klaster dievaluasi menggunakan Silhouette Score. Hasil penelitian menunjukkan bahwa film Indonesia dapat dikelompokkan ke dalam tiga klaster utama, yaitu film dengan popularitas dan kualitas tinggi, film dengan popularitas tinggi namun kualitas rendah, serta film dengan popularitas rendah tetapi kualitas tinggi. Integrasi data TMDb dan IMDb melalui pendekatan K-Means menghasilkan pemetaan film yang lebih objektif dan dapat mendukung pengambilan keputusan strategis dalam industri perfilman nasional.
The development of the Indonesian film industry over the past decade has shown significant growth in both production volume and audience appreciation. However, assessments of film quality and popularity remain largely subjective and are not fully supported by systematic data-driven analysis. This study aims to cluster Indonesian films using the K-Means algorithm based on popularity indicators from The Movie Database (TMDb) and user ratings from the Internet Movie Database (IMDb). Film data were collected through the official TMDb API (Application Programming Interface) and enriched with IMDb ratings and vote counts using external identifiers. Data preprocessing included data cleaning, normalization, and the selection of relevant numerical features. The Elbow Method was employed to determine the optimal number of clusters, while the Silhouette Score was used to evaluate clutster quality. The results show that Indonesian films can be grouped into three main clusters: films with high popularity and high quality, films with high popularity but low quality, and films with low popularity but high quality. The integration of TMDb and IMDb data using the K-Means approach provides a more objective film mapping and supports strategic decision-making in the national film industry. </dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:date>2026-06-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://semantik.uho.ac.id/index.php/journal/article/view/289</dc:identifier>
	<dc:identifier>10.55679/semantik.v12i1.289</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 12 No. 1 (2026): Vol. 12 No. 1 (2026): SemanTIK Teknik Informasi; 129-138</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://semantik.uho.ac.id/index.php/journal/article/view/289/74</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Taufik Rahman, Adhitia Erfina</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>