Analysis of Film Budget and Profit using the Bisecting K-Means Algorithm

Ahmad Fauzi (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Deden Muhamad Furqon (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Riki Ahmad Maulana (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Nurul Dwi Cahya (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Muhammad Nur Sidiq (Teknik Informatika, UIN Sunan Gunung Djati Bandung)

Gunung Djati Conference Series
Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020

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.