Prediksi Pendapatan Sewa Dengan Data Mining Pada Perusahaan XYZ
DOI:
https://doi.org/10.21512/comtech.v1i2.2344Keywords:
leasing income prediction, data mining, neural network, backpropagation, Levenberg Marguardt, CRISP-DM.Abstract
XYZ Company has a program to predict leasing income that only predict in constant condition where every tenant assumed for leasing renewal. This research is done to build accurate income prediction system that accommodate in making strategic decision towards the company. Premier data collecting is through direct interview with the company management. The analysis is through data training from the previous years to build neural network model. The analysis result shows that this model has produced error total value that is smaller than the previous error total value in years before. Therefore, it could be concluded that data mining with neural network technique that produced more accurate leasing income that could help the company making decision based on the hidden information in the database.
Plum Analytics
References
Demuth, H., et al. (2009). Neural Network ToolboxTM 6 User’s Guide. Natick: MathWorks.
Mohd, Z., & Azlan, et al. Application of Regression and ANN Techniques for Modeling of the Surface Roughness in End Milling Machining Process, IEEE Computer Society, DOI 10.1109/AMS.2009.76.
Zhang, H., et al. (2009). Multi-objective simultaneous prediction of waterborne coating properties. Journal of Mathematical Chemistry 46, 1050-1059.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. 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.
c. 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.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows:
