Analysis of Film Budget and Profit using the Bisecting K-Means Algorithm
Bilah Samping Artikel
Isi Artikel Utama
Abstrak
As the development of the film industry has become increasingly competitive in the last few decades, the film ecosystem needs to get more attention for stakeholders involved in it to continue to carry out various innovative actions and create more effective creative economy marketing strategies. By utilizing existing datasets, film content producers can build a recommendation system that can support the process of assessing business models and analyzing the concept of creative film products that will be launched in the existing film market so that later planning and design of more profitable film production concepts can be produced. And sustainability in terms of funding (budget) and projected revenue (gross profit). This research is a form of elaboration on the design of a recommendation system using the Bisecting K-Means Algorithm to be able to produce an analysis result in the form of classification of various film products contained in a dataset that has been collected as many as 5048 rows of data by taking 1000 lines of data. As a special data allocation for conducting training on the system. The data that has been allocated will then be carried out by the clustering process by dividing into 4 (four) different clusters, each of which is the result of regression based on predetermined parameters and forming a centroids which is the average value of all cluster nodes built.
Unduhan
Rincian Artikel
Artikel ini berlisensi Creative Commons Attribution 4.0 International License.
Referensi
A. Halim, H. Gohzali, D. M. Panjaitan, and I. Maulana, “Sistem Rekomendasi Film menggunakan Bisecting K-Means dan Collaborative Filtering,†Citisee, vol. 1, no. 3, pp. 37–41, 2017.
N. Puspitasari, J. A. Widians, and N. B. Setiawan, “Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model,†J. Teknol. dan Sist. Komput., vol. 8, no. 2, pp. 78–83, 2020, doi: 10.14710/jtsiskom.8.2.2020.78-83.
N. Puspitasari, J. A. Widians, and N. B. Setiawan, “Segmentasi pelanggan menggunakan algoritme bisecting k-means berdasarkan model recency, frequency, dan monetary (RFM),†J. Teknol. dan Sist. Komput., vol. 8, no. 2, pp. 78–83, 2020.
P.-N. Tan, M. Steinbach, and V. Kumar, Introduction do Data Mining. 2005.
H. Jiawei, M. Kamber, J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2006.
R. Patil and A. Khan, “Bisecting K-Means for Clustering Web Log data,†Int. J. Comput. Appl., vol. 116, no. 19, pp. 36–41, 2015, doi: 10.5120/20448-2799.
B. S. V. Krishna, P. Satheesh, and R. Suneel Kumar, “Comparative study of k-means and bisecting k-means techniques in wordnet based document clustering,†Int. J. Eng. Adv. Technol., vol. 1, no. 6, pp. 1–4, 2012.
F. Ridho and A. A. Kusuma, “Deteksi Intrusi Jaringan dengan K-Means Clustering pada Akses Log dengan Teknik Pengolahan Big Data,†J. Apl. Stat. Komputasi Stat., vol. 10, no. 1, p. 53, 2019, doi: 10.34123/jurnalasks.v10i1.202.
D. Herawatie, E. Wuryanto, and P. Purbandini, “Perbandingan Algoritma Pengelompokan Non-Hierarki untuk Dataset Dokumen,†in Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 2014, vol. 1, no. 1.