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All Journal Signifikan : Jurnal Ilmu Ekonomi Media Statistika Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series International Journal of Advances in Intelligent Informatics Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Aplikasi Statistika & Komputasi Statistik Inferensi Madaniya Proceeding ISETH (International Summit on Science, Technology, and Humanity) Bulletin of Applied Mathematics and Mathematics Education Aceh International Journal of Science and Technology Eduvest - Journal of Universal Studies
Dedy Dwi Prastyo
Department Of Statistics, Institut Teknologi Sepuluh Nopember
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CORPORATE FINANCIAL DISTRESS PREDICTION USING STATISTICAL EXTREME VALUE-BASED MODELING AND MACHINE LEARNING Dedy Dwi Prastyo; Rizki Nanda Savera; Danny Hermawan Adiwibowo
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.1-12

Abstract

The industrial sector plays a leading role in an economy such that the financial stability of companies from this sector be a big concern. Two financial ratios, i.e., the Interest Coverage Ratio (ICR) and the Return on Assets (ROA), are used to determine the corporate financial distress conditions. This work considers two schemes for determining financial distress. First, a company is categorized as distressed if either ICR<1 or ROA<0. The second scheme is for when both ICR<1 and ROA<0 are met. The proportion of distressed and non-distressed companies is imbalanced. Our work views the distressed companies (minority class) as a rare event, causing the proportion to be extremely small, such that the Extreme Value Theory can be employed. The so-called Generalized Extreme Value regression (GEVR), developed from GEV distribution, predicts the distressed labels. The GEVR's performance is compared using machine learning with and without feature selection. The feature selection in GEVR uses backward elimination. The model for prediction employs a drift or windowing concept, i.e., using past-period predictors to predict the current response. The empirical results found that the GEVR, with and without the feature selection, provides the best prediction for financial distress.
Strategi Pelayanan dan Pemasaran melalui Pelatihan Data Analytics di Bank X A. Tuti Rumiati; Ismaini Zain; Kartika Fithriasari; Wibawati Wibawati; Setiawan Setiawan; Vita Ratnasari; Dedy Dwi Prastyo; Erma Oktania Permatasari; Adatul Mukarohmah; Shofi Andari; Husna Miratin Nuroini; Veniola Forestryani; Rhifda Zukhrufi
Madaniya Vol. 5 No. 2 (2024)
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/27214834.801

Abstract

Pelatihan Data Analytics yang diadakan oleh salah satu bank terbesar di Indonesia, yaitu Bank X, merupakan sebuah inisiatif untuk meningkatkan kapasitas karyawan dalam memanfaatkan data nasabah guna menentukan strategi pelayanan dan pemasaran kedepan. Pelatihan ini memberikan pemahaman komprehensif tentang data analytics, keterampilan analisis terhadap data nasabah baik oleh bagian riset dan pengembangan maupun oleh bagian marketing untuk memahami potensi pasar, dan kebutuhan nasabah yang dapat digunakan untuk memperbaiki strategi pemasaran. Pelatihan dilakukan oleh Statistics Service Center (SSC) Institut Teknologi Sepuluh Nopember selama dua hari. Materi pelatihan dirancang sesuai dengan kebutuhan Bank X, meliputi: 1) Pembahasan profiling nasabah bank, segmentasi nasabah, target market serta pembahasan faktor-faktor yang mempengaruhi loyalitas nasabah; 2) Metode statistika untuk profiling nasabah, segmentasi pasar dan penentuan target pasar; 3) Studi kasus dan aplikasi praktis. Selain penyampaian materi pelatihan, pendekatan pelatihan melibatkan serangkaian sesi interaktif dan praktek analisis dari studi kasus dan data aktual dari bank yang dipandu oleh fasilitator. Pembelajaran dirancang untuk memfasilitasi peserta memperoleh pemahaman praktis menggunakan metode statistika, diikuti dengan penerapan langsung keterampilan analisis menggunakan perangkat lunak SPSS. Pendekatan ini diharapkan dapat menghubungkan teori dengan praktik dalam konteks yang relevan bagi peserta. Dari kegiatan yang telah dilaksanakan, tercatat peningkatan kompetensi peserta, yang tercermin dari perbedaan yang sangat signifikan hasil pre-test dan post-test serta feedback positif terhadap materi dan metode penyampaian. Manfaat kegiatan ini meliputi peningkatan kemampuan analisis data yang lebih baik dan penguasaan alat-alat analitik yang dapat diterapkan dalam berbagai fungsi bisnis di bank. Kesimpulan dari pelatihan ini menekankan pentingnya pembelajaran berkelanjutan dalam data Analytics sebagai sarana untuk meningkatkan kualitas pengambilan keputusan dan strategi bisnis di sektor perbankan. Keterlibatan karyawan dalam pelatihan ini mencerminkan komitmen bank terhadap pengembangan sumber daya manusia yang berorientasi pada inovasi dan pemanfaatan teknologi.
Modeling Multi-Output Back-Propagation DNN for Forecasting Indonesian Export-Import Maharsi, Rengganis Woro; Saputra, Wisnowan Hendy; Roosyidah, Nila Ayu Nur; Prastyo, Dedy Dwi; Rahayu, Santi Puteri
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.459

Abstract

Introduction/Main Objectives: International trade through the mechanisms of exports and imports plays a significant role in the Indonesian economy, making the timely availability of export and import value data crucial. Background Problems: Export and import values are influenced by inflation and exchange rate factors. Novelty: This study identifies two categories of variables, namely output (export value and import value) and input (inflation rate and the exchange rate of the Rupiah against the US Dollar). Research Methods: the research approach utilizes a Multi-output Deep Neural Network (DNN) with a Back-propagation algorithm to model the input-output relationship. The method can provide forecasting results for two or more bivariate or multivariate output variables. Finding/Results: The modeling analysis results indicate that the optimal model network structure is DNN (3.4). This model successfully predicts output 1 (export value) and output 2 (import value) with Mean Absolute Percentage Error (MAPE) rates of 13.76% and 13.63%, respectively. Additionally, the forecasting results show predicted export and import values for November to be US$ 16,208.13 billion and US$ 15,105.33 billion, respectively. These findings offer important insights into the direction of Indonesia's international trade movement, which can serve as a basis for future economic decision-making.
Assessment and Prediction of Hydrometeorological Drought in Corong River Basin, Indonesia Affandy*, Nur Azizah; Iranata, Data; Anwar, Nadjadji; Maulana, Mahendra Andiek; Prastyo, Dedy Dwi; Yusop, Zulkifli; Wardoyo, Wasis
Aceh International Journal of Science and Technology Vol 12, No 3 (2023): December 2023
Publisher : Graduate School of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.3.32592

Abstract

Hydrological drought analysis in a river basin is crucial because it impacts water resource management, agriculture, drinking water supply, industry, ecology, and disaster risk mitigation. It plays a key role in water usage planning, safeguarding agricultural yields, and ensuring a stable drinking water supply. In the context of this research, the Corong River basin is used as a case study. This study aims to determine the level of hydrological drought (deficit) using the Threshold Level Method (TLM) and predict hydrological drought using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. This model understands the characteristics of drought and predicts drought in the Corong River basin for early mitigation and anticipation of drought in the region. The results of this research indicate a strong relationship between the Hydrological Drought Index (HDI) and streamflow and Precipitation (PRCP), which can be used to forecast future droughts. This model is highly robust and accurate in observing the level of hydrological drought (deficit, duration, and sharpness) and predicting drought events in the Corong River basin. These findings have significant practical implications for water management and disaster risk mitigation in this river basin while also advancing the scientific understanding of hydrological drought.
Impact of Covid-19 Vaccination and Financial Policies on Indonesia’s Property Loan Growth Forestryani, Veniola; Prastyo, Dedy Dwi
Signifikan: Jurnal Ilmu Ekonomi Vol 13, No 1 (2024)
Publisher : Faculty of Economic and Business Syarif Hidayatullah State Islamic University of Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/sjie.v13i1.37419

Abstract

Research Originality: This study provides a novel examination of the impact of COVID-19-related financial policies on property loan growth in Indonesia, a critical area with limited prior quantitative research.Research Objectives: The purpose of this research is to assess how interventions such as Loan-to-Value (LTV) over Finance-to-Value (FTV) ratio (LTV/FTV) relaxation, COVID-19 vaccination as a metric for public activity restrictions, and changes in deposit insurance rates have influenced property loan dynamics during the pandemic.Research Methods: Using monthly banking data from January 2016 to May 2022, this study employs ARIMA Intervention Analysis to capture the effects of these policies.Empirical Results: The empirical results reveal a significant positive shift in property loan growth ten months after the first intervention and a notable impact two months after the third intervention, whereas the second intervention shows limited influence.Implications: These findings imply that integrating COVID-19 vaccination targets into public policy and adjusting deposit insurance rates are effective strategies for sustaining the property loan sector during economic crises. These results provide insights into the role of vaccination targets and financial adjustments in supporting the property loan sector during economic disruptions, offering valuable considerations for future policymaking in similar contexts.JEL Classification: C22, C51, C52, C53, C54  
ENSEMBLE-BASED LOGISTIC REGRESSION ON HIGH-DIMENSIONAL DATA: A SIMULATION STUDY Widhianingsih, Tintrim Dwi Ary; Kuswanto, Heri; Prastyo, Dedy Dwi
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.13-24

Abstract

Dramatic computation growth encourages big data era, which induces data size escalation in various fields. Apart from huge sample size, cases arise high-dimensional data having more feature size than its samples. High-computing power compels the usage of modern approaches to deal with this typical dataset, while in practice, common logistic regression method is yet applied due to its simplicity and explainability. Applying logistic regression on high-dimensional data arises multicollinearity, overfitting, and computational complexity issues. Logistic Regression Ensemble (Lorens) and Ensemble Logistic Regression (ELR) are the logistic-regression-based alternative methods proposed to solve these problems. Lorens adopts ensemble concept with mutually exclusive feature partitions to form several subsets of data, while ELR involves feature selection in the algorithm by drawing part of features based on probability ranking value. This paper uncovers the effectiveness of Lorens and ELR applied to high-dimensional data classification through simulation study under three different scenarios, i.e., with feature size variation, for imbalanced high-dimensional data, and under multicollinearity conditions. Our simulation study reveals that ELR outperforms Lorens and obtains more stable performance over different feature sizes and imbalanced data settings. On the other hand, Lorens achieves more reliable performance than ELR on a simulation study with a multicollinearity issue.
APPLICATION OF THE DYNAMIC FACTOR MODEL ON NOWCASTING SECTORAL ECONOMIC GROWTH WITH HIGH-FREQUENCY DATA Supriyatna, Putu Krishnanda; Prastyo, Dedy Dwi; Akbar, Muhammad Sjahid
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.128-139

Abstract

Economic growth is crucial for planning, yet delayed data releases challenge timely decision-making. Nowcasting offers near-real-time insights using high-frequency indicators (released monthly, weekly, or even daily) to predict low-frequency variables (quarterly or yearly). This study uses high-frequency indicators (monthly), such as stock price changes, air quality, transportation data, financial conditions, and Google Trends, to nowcast quarterly GDP through the Dynamic Factor Model (DFM). The data used span from January 2010 until March 2023, which is split into two: January 2010 until March 2022 for training data and the rest as testing data. Compared to the benchmark Autoregressive Moving Average with Exogenous Variables (ARMAX) model, DFM demonstrates superior accuracy with lower symmetric Mean Absolute Percentage Error (sMAPE). In addition, to evaluate the model performance in nowcasting the GDP across the sector using DFM, the additional metrics, i.e., Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Adjusted R-squared, concluded that in the industrial and transportation sectors results in sufficient nowcasting of GDP, Meanwhile, In the financial sector, the results of the nowcasting GDP give poor estimation results that need improvement.
Predictive Analytics of Rural Bank Quality Credit Prandipa, Rayza; Prastyo, Dedy Dwi
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.50826

Abstract

Credit is the main business of rural banks. Credit distribution cannot be separated from the risk of default by the debtor which has an impact on reducing credit quality. Worsening credit quality has the potential to reduce bank income because the bank's main income comes from loan interest income. Apart from that, worsening credit quality also has an impact on increasing the burden of provisions for losses on productive assets. One effort that can be made to minimize credit risk is to predict credit quality so that you can identify early the potential for a decline in credit quality. This research aims to obtain significant features that influence credit quality at Rural Banks and to predict credit quality classification at rural banks. The method used in this research is Ordinal Logistic Regression which will then be evaluated using ROC (Receiver Operating Characteristic) and AUC (Area Under Curve). The research results show that the best model for predicting credit quality uses all X variables, both credit information and debtor information, with an AUC value of 0.90 and a prediction accuracy of 93.44%.
Predicting financial distress in Indonesian life insurance companies with classification methods and synthetic features generation Purwanto, Dwi; Prastyo, Dedy Dwi
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i1.13114

Abstract

Financial problems in life insurance companies can become serious if not addressed immediately. Companies experiencing financial distress, for instance, are unable to meet their obligations to pay their liabilities. A company can be categorized as experiencing financial distress when it has an RBC ratio of less than 120%—based on regulation by the Indonesian Finance Service Authority—or ROA < 0 (suffering loss). Therefore, financial distress prediction is carried out to assess the company's current financial condition so that it can be handled early. In this study, we aimed to predict financial distress of Indonesian life insurance companies. We utilized the Support Vector Machine (SVM) classification method, Generalized Extreme Value Regression (GEVR), and Extreme Gradient Boosting (XGB) and by incorporating synthetic feature generation in variable selection. The results of financial distress prediction obtained the best model that can predict the financial condition of life insurance companies in Indonesia at each size, where for sizes 0 and 1, the XGB model with variable selection produces accuracy values of 98.00% and 94.10%, respectively, and AUC values of 100% and 87.40%. Then, at size 2, we can use Stepwise Generalized Extreme Value Regression with accuracy and AUC results of 90.20% and 82.60%, respectively. Each addition of size to the time window classification results tends to reduce the model's performance in predicting the financial condition of life insurance companies in Indonesia.
Impact of Covid-19 Vaccination and Financial Policies on Indonesia’s Property Loan Growth Forestryani, Veniola; Prastyo, Dedy Dwi
Signifikan: Jurnal Ilmu Ekonomi Vol. 13 No. 1 (2024)
Publisher : Faculty of Economic and Business, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/sjie.v13i1.37419

Abstract

Research Originality: This study provides a novel examination of the impact of COVID-19-related financial policies on property loan growth in Indonesia, a critical area with limited prior quantitative research.Research Objectives: The purpose of this research is to assess how interventions such as Loan-to-Value (LTV) over Finance-to-Value (FTV) ratio (LTV/FTV) relaxation, COVID-19 vaccination as a metric for public activity restrictions, and changes in deposit insurance rates have influenced property loan dynamics during the pandemic.Research Methods: Using monthly banking data from January 2016 to May 2022, this study employs ARIMA Intervention Analysis to capture the effects of these policies.Empirical Results: The empirical results reveal a significant positive shift in property loan growth ten months after the first intervention and a notable impact two months after the third intervention, whereas the second intervention shows limited influence.Implications: These findings imply that integrating COVID-19 vaccination targets into public policy and adjusting deposit insurance rates are effective strategies for sustaining the property loan sector during economic crises. These results provide insights into the role of vaccination targets and financial adjustments in supporting the property loan sector during economic disruptions, offering valuable considerations for future policymaking in similar contexts.JEL Classification: C22, C51, C52, C53, C54  
Co-Authors A. Tuti Rumiati Abdul Karim Abdul Karim Adatul Mukarohmah Affandy*, Nur Azizah Ahsan, Muhammad Akhmad Faturohman Bambang Widjanorko Otok Bambang Widjanorko Otok Budi Manfaat budi manfaat, budi Danny Hermawan Adiwibowo Data Iranata dwi purwanto Era Ardhya Pramesti Erma Oktania Permatasari Faturohman, Akhmad Fithriasari, Kartika Forestryani, Veniola Fredi Suryadi Fredi Suryadi Hendri Prabowo Heri Kuswanto Hidayatul Khusna Husna Miratin Nuroini Ismaini Zain Luh Putu Shintya Handayani Maharsi, Rengganis Woro Maulana, Mahendra Andiek Muhammad Hisyam Lee Muhammad Sjahid Akbar Nadjadji Anwar Novi Ajeng Salehah Prandipa, Rayza Pusaka, Semerdanta Rhifda Zukhrufi Rizki Nanda Savera Roosyidah, Nila Ayu Nur S Suhartono Salafiyah Isnawati Santi Eksiandayani Santi Puteri Rahayu Saputra, Wisnowan Hendy Setiawan Setiawan Setiawan Setiawan Shofi Andari Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Supriyatna, Putu Krishnanda Veniola Forestryani Vita Ratnasari Wardoyo, Wasis Wibawati Wibawati Widhianingsih, Tintrim Dwi Ary Yusop, Zulkifli