Analisa dan Penerapan Metode Algoritma K-Means Clustering Untuk Mengidentifikasi Rekomendasi Kategori Baru Pada List Movie IMDb
Abstract
IMDb (Internet Movie Database) is a comprehensive website that offers information about movies from all over the world, as well as various information about director, actor, actress, and writer biographies and award nominations. Visitors to the IMDb website can browse ratings and reviews based on the movies they plan to watch. Top 250 Movies and Most Popular Movies are two categories on IMDb. Because the results of the highest rating and the largest votes are only displayed based on the highest order of votes or ratings, the two existing categories are judged less useful and irrelevant to the suggestions for visitors to choose and decide on a film. This is due to the results of the highest rating and the most numerous votes, as determined by the highest ruling on either the votes or the rating. As a result of this, data mining with the K-means clustering algorithm is used to geolocate data in order to view data and accuracy using Davies-Bouldin Index (DBI) to combine ratings and votes with average approach to determine the centroid. Based on the results of this study, it is concluded that the DBI population with the highest accuracy is Cluster K=2 with population 509, with a score of 0.456, based on the voting and rating information, it can be deduced that a new category of movies called Best Recommended Movie is being recommended to potential moviegoers on the imdb.com website.
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References
G. Cahyani, W. Widayani, S. D. Anggita, and ..., “Klasifikasi Data Review IMDb Berdasarkan Analisis Sentimen Menggunakan Algoritma Support Vector Machine,” J. Media …, vol. 6, pp. 1418–1425, 2022, doi: 10.30865/mib.v6i3.4023.
Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, p. 100, 2021, [Online]. Available: http://jim.teknokrat.ac.id/index.php/JTSI
T. M. Dista and F. F. Abdulloh, “Clustering Pengunjung Mall Menggunakan Metode K-Means dan Particle Swarm Optimization,” vol. 6, pp. 1339–1348, 2022, doi: 10.30865/mib.v6i3.4172.
D. Anggarwati, O. Nurdiawan, I. Ali, and D. A. Kurnia, “Penerapan Algoritma K-Means Dalam Prediksi Penjualan Karoseri,” J. Data Sci. Inform., vol. 1, no. 2, pp. 58–62, 2021.
G. Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan,” J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019, doi: 10.25077/teknosi.v5i1.2019.17-24.
T. Hartati, O. Nurdiawan, and E. Wiyandi, “Analisis Dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari,” J. Sains Teknol. Transp. Marit., vol. 3, no. 1, pp. 1–7, 2021, doi: 10.51578/j.sitektransmar.v3i1.30.
R. R. Putra and C. Wadisman, “Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K-Means Implementation of Data Mining for Potential Customer Selection Using K-Means Algorithm,” J. Inf. Technol. Comput. Sci., vol. 1, no. 1, pp. 72–77, 2018.
I. F. Ashari, R. Banjarnahor, D. R. Farida, S. P. Aisyah, A. P. Dewi, and N. Humaya, “Application of Data Mining with the K-Means Clustering Method and Davies Bouldin Index for Grouping IMDB Movies,” J. Appl. Informatics Comput., vol. 6, no. 1, pp. 07–15, 2022, doi: 10.30871/jaic.v6i1.3485.
A. Maulana and A. A. Fajrin, “Penerapan Data Mining Untuk Analisis Pola Pembelian Konsumen Dengan Algoritma Fp-Growth Pada Data Transaksi Penjualan Spare Part Motor,” Klik - Kumpul. J. Ilmu Komput., vol. 5, no. 1, p. 27, 2018, doi: 10.20527/klik.v5i1.100.
I. Romli, “Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Klasifikasi Penyakit Ispa,” Indones. J. Bus. Intell., vol. 4, no. 1, p. 10, 2021, doi: 10.21927/ijubi.v4i1.1727.
I. R. Mahartika and A. Wibowo, “Data Mining Klasterisasi dengan Algoritme K-Means untuk Pengelompokkan Provinsi Berdasarkan Konsumsi Bahan Bakar Minyak Nasional,” Pros. Semin. Nas. SISFOTEK (Sistem Inf. dan Teknol., vol. 3, no. 1, pp. 87–91, 2019, [Online]. Available: https://seminar.iaii.or.id/index.php/SISFOTEK/article/view/108
I. Kamila, U. Khairunnisa, and M. Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 5, no. 1, p. 119, 2019, doi: 10.24014/rmsi.v5i1.7381.
R. Gustrianda and D. I. Mulyana, “Penerapan Data Mining Dalam Pemilihan Produk Unggulan dengan Metode Algoritma K-Means Dan K-Medoids,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 27, 2022, doi: 10.30865/mib.v6i1.3294.
S. B. F. Ginting, S. Sawaluddin, and M. Zarlis, “Kombinasi Pembobotan Symmetrical Uncertainty Pada K-Means Clustering Dalam Peningkatan Kinerja Pengelompokan Data,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 484, 2022, doi: 10.30865/mib.v6i1.3366.
W. Gie and D. Jollyta, “Perbandingan Euclidean dan Manhattan Untuk Optimasi Cluster Menggunakan Davies Bouldin Index : Status Covid-19 Wilayah Riau,” Pros. Semin. Nas. Ris. Dan Inf. Sci. 2020, vol. 2, no. April, pp. 187–191, 2020.
M. R. L. Iin Parlina, Agus Perdana Windarto, Anjar Wanto, “Memanfaatkan Algoritma K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Center,” Memanfaatkan Algoritm. K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Cent. Untuk Clust. Progr. Sdp, vol. 3, no. 1, pp. 87–93, 2018.
S. Hendrian, “Algoritma Klasifikasi Data Mining Untuk Memprediksi Siswa Dalam Memperoleh Bantuan Dana Pendidikan,” Fakt. Exacta, vol. 11, no. 3, pp. 266–274, 2018, doi: 10.30998/faktorexacta.v11i3.2777.
S. Ramadhani, D. Azzahra, and T. Z, “Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis,” Digit. Zo. J. Teknol. Inf. dan Komun., vol. 13, no. 1, pp. 24–33, 2022, doi: 10.31849/digitalzone.v13i1.9292.
A. A. Az-zahra, A. F. Marsaoly, I. P. Lestyani, R. Salsabila, and W. O. Z. Madjida, “Penerapan Algoritma K-Modes Clustering Dengan Validasi Davies Bouldin Index Pada Pengelompokkan Tingkat Minat Belanja Online Di Provinsi Daerah Istimewa Yogyakarta,” J. MSA ( Mat. dan Stat. serta Apl. ), vol. 9, no. 1, p. 24, 2021, doi: 10.24252/msa.v9i1.18555.
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