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All Journal Kubik Inferensi Enthusiastic : International Journal of Applied Statistics and Data Science
Santi Puteri Rahayu
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Social Sciences

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Penentuan Lokasi Baru Kantor Otoritas Jasa Keuangan (OJK) di Provinsi Sumatra Utara dengan Metode Kombinasi Analisis Faktor dan Analisis Klaster Berbasis Fuzzy Santi Puteri Rahayu; Mutiara Avista Candra Dewi Lasahido; Hendra Budi Kusuma
Inferensi Vol 5, No 1 (2022): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v5i1.12574

Abstract

Mengingat pentingnya keberadaan kantor Otoritas Jasa Keuangan (OJK) untuk mendukung perekonomian di wilayah Sumatera Utara, maka perlu adanya penambahan kantor di lokasi baru. Penelitian ini, bertujuan untuk memperoleh hasil penentuan lokasi baru kantor OJK di Provinsi Sumatera Utara dengan menerapkan dua metode Analisis Klaster, yaitu metode Fuzzy C-Means (FCM) dan Fuzzy Gustafson-Kessel (FGK), yang  secara umum merupakan suatu teknik pengelompokan observasi yang mempertimbangkan sifat keanggotaan fuzzy dalam suatu kelompok sebagai dasar pembobotan. Kedua metode Analisis Klaster mengelompokkan seluruh Kabupaten/Kota di Sumatera Utara berdasarkan variabel terpilih hasil Analisis Faktor diantara lima variabel, yaitu Produk Domestik Regional Bruto (PDRB), Pengeluaran Pemerintah, Jumlah Penduduk, Indeks Pembangunan Manusia (IPM), dan Piutang Perusahaan Pembiayaan. Berdasarkan hasil Analisis Klaster terbaik FCM dengan empat kelompok optimum dan dua variabel terpilih (PDRB dan IPM), maka Kabupaten/Kota di Provinsi Sumatera Utara yang disarankan menjadi lokasi kantor baru adalah Kabupaten Deli Serdang dan Simalungun, serta Kota Pematang Siantar.  Selain itu, sebagai Klaster Kabupaten/Kota di Provinsi Sumatera Utara yang memiliki rata-rata PDRB dan IPM tertinggi, Kota Medan dikonfirmasi layak telah memiliki Kantor OJK.
A Zero-Inflated Ordered Probit Approach to Modeling Household Poverty Levels Yudhani, Nidya Putri; Vita Ratnasari; Santi Puteri Rahayu
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art6

Abstract

This research addressed the limitations of the ordered probit (OP) regression model in handling data that contains an excessive number of zero responses. The zero-inflated ordered probit (ZIOP) model was employed to overcome this issue. This model separates the estimation of structural zeros and ordinal outcomes through two distinct components: a binary probit for zero inflation and an OP for ordered categories. Due to the absence of closed-form solutions, parameter estimation was conducted using the maximum likelihood estimation (MLE) method with the Berndt-Hall-Hall-Hausman (BHHH) iterative algorithm. The analysis was based on 4,067 household-level observations from Indonesia’s National Socio-Economic Survey, incorporating indicators of health, education, and standard of living derived from the multidimensional poverty index (MPI) framework. The result of the Vuong test (4.56) confirmed that the ZIOP model significantly outperformed the conventional OP model for zero-inflated ordinal data. Therefore, the ZIOP model is considered more appropriate for analyzing household poverty classifications with a high prevalence of zero observations.
Identification of Banking Stock Risk Factors through Stochastic Search Variable Selection in a CoVaR Models Based on Quantile Regression and Quantile Autoregressive Luqyana Almas; Dedy Dwi Prastyo; Santi Puteri Rahayu
KUBIK Vol 10 No 2 (2025): IN PRESS
Publisher : Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The stability of the banking sector is crucial for maintaining economic balance, particularly in Indonesia where banks play a central role in the financial system. Conventional risk measures such as Value-at-Risk (VaR) mainly capture individual bank risk and are limited in assessing systemic risk arising from interbank spillovers. This study proposes an integrated systemic risk framework that combines Quantile Autoregressive (QAR) based VaR estimation with Conditional Value-at-Risk (CoVaR) derived from quantile regression, while incorporating Stochastic Search Variable Selection (SSVS) to identify key risk factors. The QAR approach accommodates asymmetry and heavy-tailed characteristics of bank return distributions, whereas CoVaR measures the conditional impact of bank distress on the overall financial system.  The SSVS is implemented within a Bayesian framework to select significant market and macroeconomic variables based on posterior inclusion probabilities. Model performance is evaluated using the Kupiec Proportion of Failures (POF) test. The results show that QAR-based VaR effectively captures tail risk at the 5% and 1% quantiles. CoVaR estimates reveal heterogeneity in systemic risk exposure, with medium-sized and digital banks exhibiting greater sensitivity to systemic stress than large banks. Overall, the CoVaR–SSVS model demonstrates superior validation performance and estimation stability compared to the conventional CoVaR approach.
Co-Authors Dedy Dwi Prastyo Hendra Budi Kusuma Luqyana Almas Mutiara Avista Candra Dewi Lasahido Vita Ratnasari Yudhani, Nidya Putri