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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Epsilon: Jurnal Matematika Murni dan Terapan Viabel : Jurnal Ilmiah Ilmu-Ilmu Pertanian 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 Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Transportation 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.
PENDEKATAN MAZIMUM PENALIZED LIKELIHOOD UNTUK MENGESTIMASI FUNGSI BASELINE HAZARD PADA MODEL COX: STUDI KASUS PASIEN KANKER PAYUDARA Almira Ivah Edina; Santi Wulan Purnami; Edi Sukur; Prilyandari Dina Saputri; Ady Febrisutisyanto; Aimmatul Ummah Alfajriyah
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 19, No 2 (2025)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v19i2.17087

Abstract

Survival analysis is a statistical method that focuses on time-to-event variables, where the event time represents the duration a patient survives during the observation period. This study applies survival analysis to examine factors influencing the survival of breast cancer patients who are receiving treatment at C-Tech Labs Edwar Technology. The data used are right-censored survival data, referring to patients who either survived until the end of the observation period or died from unrelated causes. Risk factors analyzed include age, gender, and cancer stage, while treatment factors consist of surgery, chemotherapy, radiotherapy, and Frequency of Electro Capacitive Cancer Therapy (ECCT) usage. The Cox Proportional Hazard (PH) model combined with the Maximum Penalized Likelihood (MPL) method is used to analyze the effect of these factors on mortality risk, as well as to estimate regression coefficients and the baseline hazard function more accurately. The results indicate that age, frequency of ECCT use, and the status of additional therapies significantly affect patient survival. Older age increases the risk of death, while a higher frequency of ECCT use and the use of additional therapies help reduce that risk. Routine use of ECCT is shown to contribute to extending the survival time of breast cancer patients at C-Tech Labs Edwar Technology, Tangerang. However, potential confounding variables not examined in this study should be considered when interpreting the findings.
Pemetaan Kabupaten/Kota di Provinsi Jawa Timur berdasarkan Luas Panen, Produksi, dan Produktivitas Beras Mawardi; Prilyandari Dina Saputri
Viabel : Jurnal Ilmiah Ilmu-Ilmu Pertanian Vol 19 No 2 (2025): November 2025
Publisher : Universitas Islam Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/9ycfk177

Abstract

Ketersediaan pangan yang cukup bagi seluruh masyarakat merupakan indikator dari tercapainya ketahanan pangan. Ketahanan pangan dapat dicapai melalui optimalisasi komoditas pangan utama seperti padi pada setiap wilayah kabupaten/kota. Provinsi dengan luas panen dan produksi padi tahun 2024 tertinggi di Indonesia adalah provinsi Jawa Timur. Pada penelitian ini, analisis pengelompokan Kabupaten/ Kota di provinsi Jawa Timur dilakukan menggunakan metode k-means clustering berdasarkan produksi beras, luas lahan, dan produktivitas. Banyaknya cluster optimum berdasarkan nilai sillhoutte adalah sebanyak 3 cluster dengan komposisi anggota masing-masing cluster bervariasi. Pada cluster 1 memuat 11 Kabupaten/Kota, cluster 2 memuat 20 Kabupaten/Kota, dan cluster 3 memuat 7 anggota. Cluster 1 memiliki karakteristik produksi dan luas lahan yang tinggi. Namun, secara produktivitas masih cenderung kurang optimal. Cluster 2 dan cluster 3 memiliki karakteristik luas lahan dan produksi rendah. Namun pada cluster 2 produktivitasnya masih rendah, sedangkan cluster 3 produktivitasnya paling tinggi. Optimalisasi produktivitas beras dapat dilakukan pada wilayah yang masih memiliki produktivitas rendah padahal daerah sekitarnya memiliki produktivitas tinggi, seperti pada Mojokerto dan Pasuruan.
Co-Authors Ady Febrisutisyanto Aimmatul Ummah Alfajriyah Almira Ivah Edina Annisa Salsabila Pratiwi Edi Sukur Galuh Oktavia Siswono Mawardi Oktaviana, Pratnya Paramitha Pratnya Paramitha Oktaviana Santi Wulan Purnami