Pengelompokkan Film Pada Platform Netflix Menggunakan Metode K-Means Clustering Sebagai Rekomendasi Film
-
Cahyani Budihartanti
*
Universitas Nusa Mandiri, Jakarta,
Indonesia
- Cyrill Ilario Ifaru Universitas Bina Sarana Informatika, Depok, Indonesia
- Alfia Zahra Universitas Bina Sarana Informatika, Depok, Indonesia
- Muhammad Hardithya Aenuddin Universitas Bina Sarana Informatika, Depok, Indonesia
- Eri Nur Setiawan Universitas Bina Sarana Informatika, Depok, Indonesia
- Cahyadi Anugrah Maulana Universitas Bina Sarana Informatika, Depok, Indonesia
- Riky Putra Pratama Universitas Bina Sarana Informatika, Depok, Indonesia (*) Corresponding Author
Abstract
Netflix has become a major icon in technological developments in the world of entertainment. Advances in information technology allow us to make it easier to access thousands of film and TV show titles. Netflix is a subscription service that makes it easier for users to watch movies and TV shows without ads on devices connected to the Internet. With the increasing number of film and TV show titles, it makes it difficult for viewers to decide which film to watch. The K-Means Clustering method is used to analyze data from various film preference groups on Netflix. The results of this analysis can help users choose film recommendations based on the rating and genre they want. In this way, it will be easier for users to make choices for watching movies and TV shows. The dataset taken came from Kaggle with 9957 data. The results of research using the K-means Clustering method can be concluded that there are 3 clusters, where Cluster 0 is short films to feature films with an average of 33% action genre and 20% comedy, cluster 1 is films - feature films to biographical or documentary films with an average of 37% action genre and 19% drama, and cluster 2 is series or short films with an average of 32% drama genre, 24% crime and 22% action.
Downloads
References
I. Fitrianti, A. Voutama, and Y. Umaidah, “Clustering Film Populer Pada Aplikasi Netflix Dengan Menggunakan Algoritma K-Means Dan Metode CRISP-DM,” JTSI (Jurnal Teknol. Sist. Informasi), vol. 4, no. 2, pp. 301–311, 2023.
A. S. Lubis, T. Tugiono, and H. Hafizah, “Data Mining Estimasi Biaya Produksi Ikan Kembung Rebus Dengan Regresi Linier Berganda,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 6, pp. 888–897, 2022, doi: 10.53513/jursi.v1i6.5732.
S. Setyaningtyas, B. I. Nugroho, and Z. Arif, “Tinjauan Pustaka Sistematis Pada Data Mining: Studi Kasus Algoritma K-Means Clustering,” J. Teknoif Tek. Inform. Inst. Teknol. Padang, vol. 10, no. 2, pp. 52–61, 2022.
N. Suarna, N. Hidayah, and W. Prihartono, “Pengelompokan Data Film Pada Netflix Menggunakan Algoritma K-Means Clustering,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 3834–3842, 2023.
Khoirunnisa Hamidah and A. Voutama, “Analisis Faktor Tingkat Kebahagiaan Negara Menggunakan Data World Happiness Report dengan Metode Regresi Linier,” Explor. IT J. Keilmuan dan Apl. Tek. Inform., vol. 15, no. 1, pp. 1–7, 2023, doi: I https://doi.org/10.35891/explorit.
M. T. M. Ramadhan and E. B. Setiawan, “Netflix Movie Recommendation System Using Collaborative Filtering With K-Means Clustering Method on Twitter,” J. Media Inform. Budidarma, vol. 6, no. 4, pp. 2056–2063, 2022, doi: 10.30865/mib.v6i4.4571.
D. A. Mukhsinin, M. Rafliansyah, S. A. Ibrahim, R. Rahmaddeni, and D. Wulandari, “Implementasi Algoritma Decision Tree untuk Rekomendasi Film dan Klasifikasi Rating pada Platform Netflix,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 570–579, 2024, doi: 10.57152/malcom.v4i2.1255.
M. Billah, M. A. Zartesya, and D. S. Prasvita, “Penerapan Collaborative Filtering, PCA dan K-Means dalam Pembangunan Sistem Rekomendasi Ongoing dan Upcoming Film Animasi Jepang,” in Senamika (Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya), 2021, no. April, pp. 579–587, [Online]. Available: https://conference.upnvj.ac.id/index.php/senamika/article/view/1318/1089.
F. Alghifari and D. Juardi, “Penerapan Data Mining Pada Penjualan Makanan Dan Minuman Menggunakan Metode Algoritma Naïve Bayes,” J. Ilm. Inform., vol. 9, no. 2, pp. 75–81, 2021, doi: 10.33884/jif.v9i02.3755.
N. Novitasari, N. D. Nuris, and R. Herdiana, “Penerapan Algoritma K-Means untuk Clustering Data Jumlah Penduduk Miskin Berdasarkan Kota/Kabupaten di Jawabarat menggunakan Rapidminer,” J. Inform. Terpadu, vol. 9, no. 1, pp. 68–73, 2023, doi: 10.54914/jit.v9i1.660.
A. Amna et al., Data Mining, Pertama. Padang, Sumatera Barat: PT Global Eksekutif Teknologi, 2023.
P. R. Andini, “Pengaruh Konten pada Official Akun TikTok Ruangguru terhadap Prestasi Belajar Followers,” J. Janaloka, vol. 1, no. 1, pp. 15–24, 2023, [Online]. Available: https://journals.usm.ac.id/index.php/janaloka/article/view/7022.
H. Pratiwi, S. S. Handajani, and R. Respatiwulan, Clustering dan Penerapannya Dalam Bidang Seismologi. Yogyakarta: Deepublish, 2021.
U. Sa’adah, M. Y. Rochayani, D. W. Lestari, and D. A. Lusia, Kupas Tuntas Algoritma Data Mining dan Implementasi Menggunakan R. Universitas Brawij, 2021.
B. G. Sudarsono, M. I. Leo, A. Santoso, and F. Hendrawan, “Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner,” JBASE - J. Bus. Audit Inf. Syst., vol. 4, no. 1, pp. 13–21, 2021, doi: 10.30813/jbase.v4i1.2729.
A. Z. Siregar, “Implementasi Metode Regresi Linier Berganda Dalam Estimasi Tingkat Pendaftaran Mahasiswa Baru,” Kesatria J. Penerapan Sist. Inf. (Komputer dan Manajemen), vol. 2, no. 3, pp. 133–137, 2021, [Online]. Available: https://tunasbangsa.ac.id/pkm/index.php/kesatria/article/view/73.
J. Hutagalung, Kombinasi K-Means Clustering dan Metode Moora. Yogyakarta: Deepublish, 2021.
M. R. Nahjan, N. Heryana, and A. Voutama, “Implementasi Rapidminer Dengan Metode Clustering K-Means Untuk Analisa Penjualan Pada Toko OJ Cell,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 101–104, 2023, doi: 10.36040/jati.v7i1.6094.
F. Zafira, B. Irawan, and A. Bahtiar, “Penerapan Data Mining Untuk Estimasi Stok Barang Dengan Metode K-Means Clustering,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 156–161, 2024, doi: 10.36040/jati.v8i1.8319.
I. Nuryani and D. Darwis, “Analisis Clustering Pada Pengguna Brand Hp Menggunakan Metode K-Means,” in Prosiding Seminar Nasional Ilmu Komputer, 2021, vol. 1, no. 1, pp. 190–211.
L. Muflikhah, D. E. P. Ratnawati, and R. R. Mardi, Data Mining. Malang: UB Press, 2018.
D. Jollyta, W. Ramdhan, and M. Zarlis, Konsep Data Mining dan Penerapan. Deepublish, 2020.
R. T. Vulandari, Data Mining Teori dan Aplikasi Rapidminer. Yogyakarta: Gava Media, 2017.
M. Wahyudi, M. Masitha, R. Saragih, and S. Solikhun, Data Mining : Penerapan Algoritma K-Means Clustering dan K-Medoids Clustering, Pertama., vol. 01. Yayasan Kita Menulis, 2020.
S. Witanto, D. E. Ratnawati, and S. Anam, “Pengelompokan Fungsi Aktif Senyawa Data SMILES (Simplified Molecular Input Line Entry System) Menggunakan Metode K-Means Dengan Inisialisasi Pusat Klaster Menggunakan Metode Heuristic O (N LogN),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 1, pp. 702–707, 2019.
I. V. O. Situmeang, “Terpaan Media Streaming Netflix Dan Kualitas Aplikasi Netflix Terhadap Keputusan Penggunaan (Survei Pada Gen Z Di Perumahan Citra Gran, Cibubur),” IKRAITH-HUMANIORA J. Sos. dan Hum., vol. 6, no. 1, pp. 160–174, 2022, [Online]. Available: https://journals.upi-yai.ac.id/index.php/ikraith-humaniora/issue/archive.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Pengelompokkan Film Pada Platform Netflix Menggunakan Metode K-Means Clustering Sebagai Rekomendasi Film
Pages: 1392-1402
Copyright (c) 2024 Cahyani Budihartanti, Cyrill Ilario Ifaru, Alfia Zahra, Muhammad Hardithya Aenuddin, Eri Nur Setiawan, Cahyadi Anugrah Maulana, Riky Putra Pratama
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
