Article Per Year (5 Year)
p-Index From 2021 - 2026
1.633
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Seni ITS TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIV : International Journal on Informatics Visualization BAREKENG: Jurnal Ilmu Matematika dan Terapan SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Pendidikan Matematika (Kudus) J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Varian Teknomatika (Jurnal Teknologi dan Informatika) JURNAL ILMIAH GLOBAL EDUCATION Proceeding ISETH (International Summit on Science, Technology, and Humanity) Eduvest - Journal of Universal Studies
Santi Puteri Rahayu
Departemen Statistika ITS
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Pengelompokan Pertumbuhan Ekonomi (PDRB) dan Pengeluaran di Jawa Timur Berdasarkan Jumlah UMK serta Faktor-faktor yang Mempengaruhi dengan Model Persamaan Simultan Santi Puteri Rahayu; Irene Monica Amanda
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 15 No 2 (2022): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol15.no2.a5802

Abstract

Pertumbuhan ekonomi Produk Domestik Regional Bruto (PDRB) diidentikkan sebagai ukuran kesejahteraan masyarakat. Usaha mikro kecil (UMK) di Indonesia dapat menjadi pendukung dalam pertumbuhan ekonomi karena UMK memiliki karakteristik positif sebagai sektor yang mampu menyediakan lapangan pekerjaan yang besar. Pengeluaran juga dapat mempengaruhi PDRB, karena dapat menunjukkan kesejahteraan masyarakat dalam memenuhi kebutuhan hidupnya. Jawa Timur sebagai salah satu provinsi Indonesia yang memiliki perekonomian yang baik, karena memiliki PDRB terbesar kedua setelah DKI Jakarta dan memiliki UMK yang menjadi pendukung pertumbuhan ekonomi. Adanya hubungan simultan antara PDRB dan pengeluaran yang over identified dapat dimodelkan menggunakan metode persamaan simultan 2SLS dan 3SLS. Hasil menunjukkan bahwa estimasi model persamaan simultan lebih baik daripada model persamaan tunggal, berdasarkan kriteria koefisien determinasi maksimum dan kesamaan nilai estimasi. Lebih dari itu, estimasi model persamaan simultan 3SLS ditunjukkan ecara empiris bersifat lebih baik dibandingkan model 2SLS, dengan kriteria koefisien determinasi maksimum dan standard error minimum. Hasil estimasi model 3SLS menunjukkan bahwa jumlah UMK dan pengeluaran berpengaruh positif terhadap PDRB, tetapi rasio ketergantungan berpengaruh negatif terhadap PDRB. Sementara itu, IPM dan PDRB berpengaruh positif terhadap pengeluaran, tetapi pengangguran berpengaruh negatif terhadap pengeluaran. Hasil konfirmasi pengelompokan estimasi sepuluh daerah PDRB terendah dengan data aktual hanya meliputi empat kabupaten/kota, sedangkan estimasi pengeluaran terdiri dari tiga kabupaten/kota.
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.
ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS Halistin, Halistin; Ratnasari, Vita; Rahayu, Santi Puteri; Patih, Tandri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (885.055 KB) | DOI: 10.30598/barekengvol14iss2pp167-174

Abstract

One model that may explain the pattern of the relationship between the categorical dependent variable and the independent variables is probit regression. In the probit regression, the independent variable can be categorical or continuous. Probit regression is using the link function of the standard normal distribution. If the probit regression modeling involves a cross-section data and time series data, it is called probit data panel model. Parameter estimation of random effect probit data panel model is using the maximum likelihood estimation (MLE) method with Gauss Hermite Quadrature approach. Iterative procedure by using BFGS method. BFGS method used to obtain the close form value of the parameter estimates.
Application of Zero Inflated Ordered Logit (ZIOL) (Case Study: The Employment Status Of The Working-Age Population In Banten Province) Marshiela, Jessie Reyna; Ratnasari, Vita; Rahayu, Santi Puteri
Jurnal Ilmiah Global Education Vol. 6 No. 2 (2025): JURNAL ILMIAH GLOBAL EDUCATION, Volume 6 Nomor 2
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/jige.v6i2.3675

Abstract

Unemployment remains a major economic issue in Indonesia, particularly in Banten Province, which has the highest open unemployment rate. Traditional models struggle to capture the zero inflation characteristics in labor force data, where most individuals are employed. This study applies the Zero-Inflated Ordered Logit (ZIOL) model to better analyze labor force status in Banten by distinguishing between genuinely unemployed individuals and those appearing unemployed due to external factors.Using data from the National Labor Force Survey (SAKERNAS) 2023, this study examines the impact of gender, education, residence, job training access, and work experience on employment. The results show that women, individuals with lower education, and those lacking work experience are more likely to be unemployed or underemployed. ZIOL outperforms traditional ordinal logit models in capturing these dynamics.The findings provide insights for policymakers to design more effective employment strategies, particularly in regions facing high unemployment.
Multivariate Time Series Forecasting using Hybrid Vector Autoregressive and Neural Network for Coupled Roll-Sway-Yaw Motions Prediction Suhermi, Novri; Suhartono, -; Rahayu, Santi Puteri; Ali, Baharuddin; Dahlila, Dea; Aisy, Rahida Rihhadatul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3077

Abstract

There are six types of motion referred to as the six degrees of freedom, which define the motion of a ship. For a ship to remain stable, it must be in a symmetrical position. Therefore, a ship's stability can be determined based on its motion. Ship motions can be analyzed either in an uncoupled system or a coupled system. One of the coupled motion systems that is often studied is the roll-sway-yaw motion. In this study, we apply the Hybrid Vector Autoregressive–Neural Network (VAR-NN) model to build a multivariate time series model for predicting the roll-sway-yaw motions of a prototype ship. The Hybrid VAR-NN is a data analysis technique that integrates the linear capabilities of the VAR model with the nonlinear capabilities of the NN model to capture both linear and nonlinear trends simultaneously. The dataset for this study was generated from waves in a prototype ship experiment and divided into in-sample and out-of-sample data. The model was trained using the in-sample data, and predictions were made on the out-of-sample data using the trained model. The forecast results of the VAR-NN model were compared with those from the pure VAR and pure NN models. Model selection was based on out-of-sample performance criteria, with the Root Mean Square Error (RMSE) employed as the prediction performance metric. According to the experimental results, the Hybrid VAR-NN model outperformed the other models, demonstrating its ability to improve the prediction performance of the pure models through its hybrid approach.
The Theoretical Study of Rare Event Weighted Logistic Regression for Classification of Imbalanced Data Sulasih, Dian Eka Apriana; Purnami, Santi Wulan; Rahayu, Santi Puteri
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2376

Abstract

One of the problems in data classification is imbalanced data. In two-class classification, imbalance problem occurs where one of the two classes has more samples than another class. In such situation, most of the classifier will be biased towards the major class, while the minor class will be subordinated eventually which leads to inaccurate classification. Therefore, a method to classify the imbalanced data is required. Rare Event Weighted Logistic Regression (RE-WLR) which is developed by Maalouf and Siddiqi is a method of classification applied to large imbalanced data and rare event. This study showed the review of RE-WLR for the classification of imbalanced data. It explicated the steps to obtain the estimator specifically, particularly for IRLS. RE-WLR is a combination of Logistic Regression (LR) rare events corrections and Truncated Regularized Iteratively Re-weighted Least Squares (TR-IRLS). Rare event correction in LR is applied to Weighted Logistic Regression (WLR). Regularization was added to reduce over-fitting. The estimation of ߚ is performed by using the method of maximum likelihood (ML), while WLR maximum likelihood estimates (MLE) were obtained by using IRLS method of Newton-Raphson algorithm. In order to solve large optimization problems, Truncated-Newton method is applied.
Sectoral Employment in Indonesia with Spatial and Seemingly Unrelated Regression (SUR) Model Approach Dewi, Vivin Novita; Setiawan, S; Rahayu, Santi Puteri
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2015: Proceeding ISETH (International Conference on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2377

Abstract

Employment becomes one of the most important focuses of development in Indonesia. Analysis of employment and its factors could be the consideration in making employment policies. Several studies of employment related to a particular economic sector have been carried out. For a comparison, this paper discussed the model of labor absorption with three economic sectors. The source of data was derived from all the provinces in Indonesia for five years. Spatial model was estimated with Maximum Likelihood Estimation (MLE) for each year of observation. Moran’s I and LM test were used to identify the spatial dependency. SUR model was estimated with Ordinary Least Square (OLS) and General Least Square (GLS).The variables used to estimate labor absorption were the output and real wage. The resultindicated that the spatial dependency was significant particularly in the agricultural sector with a spatial error model. Meanwhile, labor absorption was significantly affected by the output and real wage for both OLS estimation and GLS estimation for SUR model. Service sector had the highest R2 value. UR model with GLS estimation was evidenced to be more efficient than OLS estimation, in addition, standard error of parameters using GLS estimation evenly was lower than OLS estimation.
Predicting Flight Delays Using LSTM and BILSTM Models with Shap Interpretation Ifayanti Rohmatul Hidayah; Irhamah Irhamah; Santi Puteri Rahayu
Eduvest - Journal of Universal Studies Vol. 6 No. 6 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i6.53292

Abstract

Flight delays represent a critical challenge in air transportation, affecting passenger satisfaction, operational efficiency, and financial outcomes. This study develops predictive models for flight delay duration at Juanda International Airport, Surabaya, using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) networks integrated with a Shapley Additive Explanations (SHAP) interpretability method. The research utilized 242,638 flight observations spanning January 2023 to October 2025, incorporating flight operational and meteorological variables. The dataset was partitioned into training (62.35%), validation (9.01%), and testing (28.64%) subsets. After Min-Max normalization and preprocessing, models were designed with varying hyperparameters through grid search optimization. Performance evaluation employed Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results demonstrated that LSTM with two hidden layers and sixteen neurons achieved superior performance, with an MAE of 15.657 minutes and an RMSE of 20.859 minutes on test data, slightly outperforming BiLSTM (MAE of 16.705 minutes and RMSE of 21.709 minutes), establishing LSTM as the optimal model. SHAP interpretation revealed that operational factors, particularly the association with major airports and routes (Jakarta and Surabaya), flight type, and airline, represent the dominant predictors of delays, whereas meteorological factors such as wind speed and temporal factors such as scheduling time have a relatively minor effect. Although the model's predictive power remains limited, this research provides valuable interpretability insights into delay determinants, enabling data-driven decision-making for airport management and airlines to enhance punctuality and operational efficiency.
Banking Market Risk Modelling Using QAR-Based CoVaR with Quantile Regression Alma, Luqyana Zakiya; Prastyo, Dedy Dwi; Rahayu, Santi Puteri; Nugroho, Ari
Jurnal Pendidikan Matematika Vol 9, No 1 (2026): Jurnal Pendidikan Matematika (Kudus)
Publisher : Universitas Islam Negeri Sunan Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jpmk.v9i1.35180

Abstract

Financial sector stability is essential for economic resilience, particularly in Indonesia’s banking industry. Commonly used risk measures such as Value-at-Risk (VaR) capture individual risk but do not adequately account for systemic interdependence. Existing studies often rely on linear models that are less capable of capturing asymmetric and heavy-tailed return behaviour. This study addresses this gap by developing a Conditional Value-at-Risk (CoVaR) framework based on the Quantile Autoregressive (QAR) approach. This study uses daily closing prices of 15 largest market-cap banking firms listed on the Indonesia Stock Exchange from July 4, 2022, to June 30, 2025. VaR is estimated using QAR, followed by CoVaR estimation through quantile regression, and evaluated using the Kupiec Proportion of Failures (POF) test. The results show that the QAR-based VaR model performs consistently well, with all 15 banks passing the Kupiec test at both the 1% and 5% quantiles, indicating robust tail risk estimation. In contrast, CoVaR results are less stable, with 14 banks passing at the 1% quantile and only 7 at the 5% quantile, suggesting challenges in capturing conditional dependence. Banks such as ARTO and BBHI exhibit stronger systemic spillover effects. This study contributes by integrating QAR into CoVaR modelling and provides insights for systemic risk monitoring in emerging banking markets. Stabilitas sektor keuangan sangat penting dalam menjaga ketahanan ekonomi, khususnya pada industri perbankan di Indonesia. Ukuran risiko yang umum digunakan seperti Value-at-Risk (VaR) mampu menangkap risiko individual, namun belum memadai dalam merepresentasikan keterkaitan sistemik antar institusi. Studi yang ada umumnya masih mengandalkan model linier yang kurang mampu menangkap karakteristik return yang asimetris dan berekor tebal. Penelitian ini mengatasi kesenjangan tersebut dengan mengembangkan kerangka Conditional Value-at-Risk (CoVaR) berbasis pendekatan Quantile Autoregressive (QAR). Penelitian ini menggunakan data harga penutupan harian dari 15 perusahaan perbankan dengan kapitalisasi pasar terbesar yang terdaftar di Bursa Efek Indonesia selama periode 4 Juli 2022 hingga 30 Juni 2025. Estimasi VaR dilakukan menggunakan model QAR, kemudian dilanjutkan dengan estimasi CoVaR melalui regresi kuantil, dengan evaluasi kinerja model menggunakan uji Kupiec Proportion of Failures (POF). Hasil penelitian menunjukkan bahwa model VaR berbasis QAR memiliki kinerja yang konsisten baik, dengan seluruh 15 bank lolos uji Kupiec pada kuantil 1% dan 5%, yang mengindikasikan estimasi risiko ekor yang andal. Sebaliknya, hasil CoVaR menunjukkan stabilitas yang lebih rendah, dengan 14 bank lolos pada kuantil 1% dan hanya 7 bank pada kuantil 5%, yang mengindikasikan adanya tantangan dalam menangkap ketergantungan kondisional. Bank seperti ARTO dan BBHI menunjukkan kontribusi risiko sistemik yang lebih tinggi. Penelitian ini memberikan kontribusi dengan mengintegrasikan QAR ke dalam pemodelan CoVaR serta memberikan implikasi praktis bagi pemantauan risiko sistemik pada pasar perbankan di negara berkembang.
Co-Authors - Suhartono Ahmad Saikhu Aisy, Rahida Rihhadatul Alma, Luqyana Zakiya Amilia Firda Rahmana Ari Nugroho Bagus Rahmatullah Baharuddin Ali, Baharuddin Dahlila, Dea Dedy D. Prastyo Dedy Dwi Prastyo Dedy Prastyo Dewi, Vivin Novita Djoko Kuswanto Fakhrul Azizi Indillah Habi Cahya Kumara Halistin, Halistin I Made Gde Meranggi Dana Ida Puspita Ningtias Ifayanti Rohmatul Hidayah Imam Safawi Ahmad, Imam Safawi Irene Monica Amanda Irhamah - Maharsi, Rengganis Woro Marshiela, Jessie Reyna Muhammaf Sjahid Akbar Nayla Nayla Ma’rufah Ma’rufah Neni Alya Firdausanti Nur Fajriyah Ria Restu Aripin Roosyidah, Nila Ayu Nur Santi Wulan Purnami Saputra, Wisnowan Hendy Setiawan Setiawan Setiawan Setiawan Setiawan, S Siti Soraya Suhartono Suhartono Suhartono Suhartono Suhermi, Novri Sulasih, Dian Eka Apriana Tandri Patih Vita Ratnasari