Building an Initiation of Financial Reporting System Using Blockchain and Classification Analysis to Financial Distress
Arum Handini Primandari, Masthura
Arum Handini Primandari
Available Online 11 October 2020.
- https://doi.org/10.2991/assehr.k.201010.028How to use a DOI?
- Blockchain, Financial Distress, Financial Reporting System, SMOTE, SVM
- Indonesia’s regional government still uses a conventional financial reporting method by manually submitting some documents. As a consequence, there is no digital database provided. Our proposed technology is blockchain to be implemented for distributed tech financial reports. As popular as a cryptocurrency database system, Blockchain is a time-stamped series of immutable data records in chained blocks. As all the involved institutions (i.e., SKPD, PPKD, Regional Secretary, Regent/Mayor, BPK, and DPRD) will become nodes on the financial report system, its transparency and confidentiality are maintained. This research builds an initiation of the system using python programming. Adding the information about the regional financial condition, whether it experiences financial distress, or not will give the report overview. The prediction of financial distress is made by employing Support Vector Machine (SVM) with Radial Basis Function (RBF). In order to overcome the imbalance class, the Synthetic Minority Over-Sampling Technique (SMOTE) is applied. As a result, SVM with SMOTE can classify financial distress with the accuracy of the testing dataset is about 94.44%, the precision of 100%, and the kappa value of 82.34%.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Arum Handini Primandari AU - Masthura PY - 2020 DA - 2020/10/11 TI - Building an Initiation of Financial Reporting System Using Blockchain and Classification Analysis to Financial Distress BT - Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019) PB - Atlantis Press SP - 193 EP - 199 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.201010.028 DO - https://doi.org/10.2991/assehr.k.201010.028 ID - Primandari2020 ER -