Iswari, Ni Made Satvika and Budiardjo, Eko Kuswardono and Santoso, Harry Budi and Hasibuan, Zainal Arifin (2019) E-Business Application Recommendation for SMEs based on Organization Profile using Random Forest Classification. 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI).
Full text not available from this repository.Abstract
In a country's economy, small and medium-sized enterprises (SMEs) play an important role. In Indonesia, SMEs are the largest business group and are able to survive in conditions of economic crisis. Utilization of e-business applications by SMEs can be widely used, including to increase product sales, organizational collaboration, and support the organization's business processes. By using e-business applications, companies are able to benefit from several advantages, including positive results for organizational efficiency through promoting higher gross margins, profitability, financial management and operational excellence for employees. However, SMEs have a wide variety of characteristics. Several studies identify SMEs according to their scale. Other studies reveal that SMEs can be grouped based on the nature of the organization in using e-business applications. Thus, the proposed e-business application for SMEs is not a single-size solution, but rather needs to consider the profile of the organization. In this study, a method for recommending e¬business applications for SMEs was proposed based on the organizational profile. The proposed method uses Kano Classification to determine E-Business application preferences, as a basis for e-business application requirements. Meanwhile for training data and prediction, Random Forest Classification is used. Based on the Classification Report, the precision results was 0.87, the recall was 0.46, and the f-1 score was 0.55. In the case study carried out, the proposed method can produce appropriate e-business application recommendations based on the readiness profile of the organization.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming 300 Social Sciences > 330 Economics > 338 Production (Agriculture, Business Enterprise, Extraction of Minerals, General Production) |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 11 Oct 2021 13:20 |
| Last Modified: | 14 Oct 2021 13:13 |
| URI: | https://kc.umn.ac.id/id/eprint/18651 |
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