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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika dan Aplikasinya East Java Economic Journal
Prilyandari Dina Saputri
Departemen Aktuaria, Institut Teknologi Sepuluh Nopember, Surabaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Mathematics Social Sciences Other

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Comparison of Feedforward Neural Network and Classical Statistics Methods: Application in Finance Prilyandari Dina Saputri; Pratnya Paramitha Oktaviana
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 3 (2023): MAY, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i3.25379

Abstract

The flexibility and elevated accurateness of the statistical machine learning method makes this method widely applied in various fields. One of the statistical machine learning methods is the neural network, which can be used for data analysis. The great performance of the neural network method can be used in the field of finance. In this study, the neural network method was used to predict Non-Performing Loans (NPL) data and forecast credit receivables. In the NPL prediction, the banks used are State-Owned Banks, Regional Government Banks, and National Private Banks with a main capital of more than 6 trillion rupiahs in March 2021, i.e. 26 banks with the period of March 2018 until March 2021. In predicting NPL, a moving window scheme involves several different periods. In the forecast of the number of credit receivables, the data used is the number of financing receivables. The period from November 2012 to December 2020 is used as training data, while data for the period from January to June 2021 is used as testing data. The results of the analysis show that the neural network for NPL prediction and credit receivables forecasting shows better performance compared to classical methods such as multiple linear regression and ARIMA. A comparison of methods for banking NPLs prediction is based on the RMSE data testing values, while forecasting credit receivable is based on RMSE, MAE, and MAPE data testing values.
Pemodelan Produk Domestik Regional Bruto Sektor Pertanian dan Penyaluran Kredit menggunakan Two Stage Least Square Prilyandari Dina Saputri; Pratnya Paramitha Oktaviana
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07101

Abstract

Green economy is a concept relating to economic development which aimed to improve people's welfare by paying attention to environmental conditions. One main pillar of a green economy is economic growth which can be calculated through GDP (Gross Domestic Product). Financial institutions can play an important role in raising economic growth through optimal credit allocation. This study aims to identify the causal relationship between credit allocation from financial institutions and regional economic growth (GRDP), particularly in the green industry sector. The causal relationship that influences each other between credit allocation and Gross Regional Domestic Product (GRDP) in the agricultural, hunting, forestry, and fisheries sectors can be analyzed using the simultaneous two stage least square equation. The variables that significantly affect credit allocation are the percentage of NPL and GRDP, while the variables that significantly affect GRDP are the area of agricultural land and credit allocation. A significant causal relationship between credit distribution and GRDP shows that financial institutions can play a role in raising the growth of the green sector economy through credit allocation, especially in the green sector.
Klasifikasi Financial Distress Menggunakan Feedforward Neural Network Berdasarkan Rasio Keuangan Altman dan Ohlson Annisa Salsabila Pratiwi; Galuh Oktavia Siswono; Prilyandari Dina Saputri
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27742

Abstract

The ever-changing economy requires companies to anticipate future conditions in order to avoid financial distress, a continuous decline in financial conditions. The research focused on comparing Altman and Ohlson’s financial ratio in classifying financial distress on Property and Real Estate companies using the Feedforward Neural Network. The data used is the financial report data of 19 Property and Real Estate companies listed on the Indonesian Stock Exchange in 2016-2022, with the initial status of financial conditions based on earnings per share. (EPS). The study also used the Synthetic Minority Oversampling Technique (SMOTE) method to address class imbalances.  The best financial ratio is selected based on accuracy values and Area Under Curve (AUC). Altman’s financial ratio with the FFNN model architecture (5-2-1) with a balance of 60:40 yields an accuracy of 84.62% and an AUC of 0.8325. The Ohlson Financial ratio with the 60:40 data balancing process and the FFNN model architecture (9-4-1) yields an accuracy of 93.27% and an AUC of 0.9045. Thus, in predicting financial distress in companies in the Property and Real Estate sector, Ohlson’s financial ratio with the predictor variables Corporate Size (SIZE), Total Liabilities to Total Assets (TLTA), Working Capital to Total Acts (WCTA), Current Liability to Current Asset (CLCA), OENEG, Net Income to total assets (NITA), Cash Flows Operating to Total Responsibilities (CFOTL), Net Revenue (INTWO), and Net Incoming Change (CHIN) yielded the best results. This best ratio can be used as a consideration in using alternative financial ratio to classify financial distress.
The Digitalization Impact of The Payment System Through Qris on East Java Regional Financial Transactions Prilyandari Dina Saputri
East Java Economic Journal Vol. 7 No. 2 (2023)
Publisher : Kantor Perwakilan Bank Indonesia Provinsi Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53572/ejavec.v7i2.120

Abstract

Payment system transaction efficiency can be optimized by utilizing technology in the digital era. Bank Indonesia with the Indonesian Payment System Association (ASPI) launched QRIS as a payment transaction instrument employing a standardized QR code. This study aims to evaluate the impact of the QRIS implementation policy and the QRIS transaction limit change policy on financial transactions in East Java Province. Financial transactions are represented using outflows, which is the cash flow of currency going out from Bank Indonesia. The research was performed using the intervention analysis. The results show that the implementation of QRIS in January 2020 had a significant impact on reducing outflow 5 periods after the policy was implemented. The decline in outflows even exceeds 90% in 2021 and exceeds 80% in 2022. The QRIS transaction limit change policy does not affect the increase/decrease in outflow transactions. This can be caused by QRIS users who are dominated by the MSME sector so the transactions made are retail. Further research on the impact of the QRIS policy using the amount of money in circulation, the net flow of currency, and digital banking transactions as the indicator of financial transactions also need to be carried out.
Co-Authors Annisa Salsabila Pratiwi Galuh Oktavia Siswono Oktaviana, Pratnya Paramitha Pratnya Paramitha Oktaviana